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Endothelial adherens junctions and the actin cytoskeleton: an 'infinity net'?

A recent paper in BMC Biology reports that actin stress fibers in adjacent cultured endothelial cells are linked through adherens junctions. This organization might provide a super-cellular network that could ena...

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Robust and specific inhibition of microRNAs in Caenorhabditis elegans

MicroRNAs (miRNAs) are small non-coding RNAs that regulate the expression of numerous target genes. Yet, while hundreds of miRNAs have been identified, little is known about their functions. In a recent report...

Genome of a songbird unveiled

An international collaborative effort has recently uncovered the genome of the zebra finch, a songbird model that has provided unique insights into an array of biological phenomena.

The mathematics of sexual attraction

Pollen tubes follow attractants secreted by the ovules. In a recent paper in BMC Plant Biology , Stewman and colleagues have quantified the parameters of this attraction and used them to calibrate a mathematical m...

Diversity lost: are all Holarctic large mammal species just relict populations?

Population genetic analyses of Eurasian wolves published recently in BMC Evolutionary Biology suggest that a major genetic turnover took place in Eurasian wolves after the Pleistocene. These results add to the gr...

Hybridization and speciation in angiosperms: arole for pollinator shifts?

The majority of convincingly documented cases of hybridization in angiosperms has involved genetic introgression between the parental species or formation of a hybrid species with increased ploidy; however, ho...

Evolution underground: shedding light on the diversification of subterranean insects

A recent study in BMC Evolutionary Biology has reconstructed the molecular phylogeny of a large Mediterranean cave-dwelling beetle clade, revealing an ancient origin and strong geographic structuring. It seems li...

A modern circadian clock in the common angiosperm ancestor of monocots and eudicots

The circadian clock enhances fitness through temporal organization of plant gene expression, metabolism and physiology. Two recent studies, one in BMC Evolutionary Biology , demonstrate through phylogenetic analys...

Scale-eating cichlids: from hand(ed) to mouth

Two recent studies in BMC Biology and Evolution raise important questions about a textbook case of frequency-dependent selection in scale-eating cichlid fishes. They also suggest a fascinating new line of researc...

Top dogs: wolf domestication and wealth

A phylogeographic analysis of gene sequences important in determining body size in dogs, recently published in BMC Biology , traces the appearance of small body size to the Neolithic Middle East. This finding stre...

No better time to FRET: shedding light on host pathogen interactions

Understanding the spatio-temporal subversion of host cell signaling by bacterial virulence factors is key to combating infectious diseases. Following a recent study by Buntru and co-workers published in BMC Biolo...

Making progress in genetic kin recognition among vertebrates

A recent study in BMC Evolutionary Biology has shown that genetically similar individual ring-tailed lemurs are also more similar in their scent composition, suggesting a possible mechanism of kin recognition. Th...

Regeneration review reprise

There have been notable advances in the scientific understanding of regeneration within the past year alone, including two recently published in BMC Biology . Increasingly, progress in the regeneration field is be...

Acoel and platyhelminth models for stem-cell research

Acoel and platyhelminth worms are particularly attractive invertebrate models for stem-cell research because their bodies are continually renewed from large pools of somatic stem cells. Several recent studies,...

Madm (Mlf1 adapter molecule) cooperates with Bunched A to promote growth in Drosophila

The TSC-22 domain family (TSC22DF) consists of putative transcription factors harboring a DNA-binding TSC-box and an adjacent leucine zipper at their carboxyl termini. Both short and long TSC22DF isoforms are ...

Bunched and Madm: a novel growth-regulatory complex?

By combining Drosophila genetics and proteomics Gluderer et al. report in this issue of Journal of Biology the isolation of a novel growth-regulatory complex consisting of Bunched and Madm. Future study of this c...

Q&A: What can microfluidics do for stem-cell research?

Regulation of metabolism in caenorhabditis elegans longevity.

The nematode Caenorhabditis elegans is a favorite model for the study of aging. A wealth of genetic and genomic studies show that metabolic regulation is a hallmark of life-span modulation. A recent study in BMC ...

Reprogramming of the non-coding transcriptome during brain development

A recent global analysis of gene expression during the differentiation of neuronal stem cells to neurons and oligodendrocytes indicates a complex pattern of changes in the expression of both protein-coding tra...

The THO complex as a key mRNP biogenesis factor in development and cell differentiation

The THO complex is a key component in the co-transcriptional formation of messenger ribonucleoparticles that are competent to be exported from the nucleus, yet its precise function is unknown. A recent study in B...

SnoPatrol: how many snoRNA genes are there?

Small nucleolar RNAs (snoRNAs) are among the most evolutionarily ancient classes of small RNA. Two experimental screens published in BMC Genomics expand the eukaryotic snoRNA catalog, but many more snoRNAs remain...

Sometimes one just isn't enough: do vertebrates contain an H2A.Z hyper-variant?

How much functional specialization can one component histone confer on a single nucleosome? The histone variant H2A.Z seems to be an extreme example. Genome-wide distribution maps show non-random (and evolutio...

Apical polarity in three-dimensional culture systems: where to now?

Delineation of the mechanisms that establish and maintain the polarity of epithelial tissues is essential to understanding morphogenesis, tissue specificity and cancer. Three-dimensional culture assays provide...

The water flea Daphnia - a 'new' model system for ecology and evolution?

Daphnia pulex is the first crustacean to have its genome sequenced. Availability of the genome sequence will have implications for research in aquatic ecology and evolution in particular, as addressed by a series...

Top ten in Journal of Biology in 2009: stem cells, influenza, pit bulls, Darwin, and more

The bacterial pathogen listeria monocytogenes : an emerging model in prokaryotic transcriptomics.

A major challenge in bacterial pathogenesis is understanding the molecular basis of the switch from saprophytism to virulence. Following a recent whole-genome transcriptomic analysis using tiling arrays, an ar...

Forward genetics in Tribolium castaneum : opening new avenues of research in arthropod biology

A recent paper in BMC Biology reports the first large-scale insertional mutagenesis screen in a non-drosophilid insect, the red flour beetle Tribolium castaneum . This screen marks the beginning of a non-biased, '...

Mapping the protistan 'rare biosphere'

The use of cultivation-independent approaches to map microbial diversity, including recent work published in BMC Biology , has now shown that protists, like bacteria/archaea, are much more diverse than had been re...

Scribble at the crossroads

Although proteins involved in determining apical-basal cell polarity have been directly linked to tumorigenesis, their precise roles in this process remain unclear. A recent report in BMC Biology clarifies the si...

Q&A: Quantitative approaches to planar polarity and tissue organization

Gene regulation, evolvability and the limits of genomics, the transcriptome of human monocyte subsets begins to emerge.

Human monocytes can be divided into subsets according to their expression or lack of the cell-surface antigen CD16. In papers published recently in the Journal of Proteome Research and in BMC Genomics , two groups...

Chromatin 'programming' by sequence - is there more to the nucleosome code than %GC?

The role of genomic sequence in directing the packaging of eukaryotic genomes into chromatin has been the subject of considerable recent debate. A new paper from Tillo and Hughes shows that the intrinsic therm...

Fishing for the signals that pattern the face

Zebrafish are a powerful system for studying the early embryonic events that form the skull and face, as a model for human craniofacial birth defects such as cleft palate. Signaling pathways that pattern the p...

Coordinated gene expression by post-transcriptional regulons in African trypanosomes

The regulation of gene expression in trypanosomes is unique. In the absence of transcriptional control at the level of initiation, a subset of Trypanosoma brucei genes form post-transcriptional regulons in which ...

Promoter architecture and the evolvability of gene expression

Evolutionary changes in gene expression are a main driver of phenotypic evolution. In yeast, genes that have rapidly diverged in expression are associated with particular promoter features, including the prese...

Adaptations of proteins to cellular and subcellular pH

Bioinformatics-based searches for correlations between subcellular localization and pI or charge distribution of proteins have failed to detect meaningful correlations. Recent work published in BMC Biology finds ...

TBP2 is a general transcription factor specialized for female germ cells

The complexity of the core promoter transcription machinery has emerged as an additional level of transcription regulation that is used during vertebrate development. Recent studies, including one published in BM...

Generalized immune activation as a direct result of activated CD4 + T cell killing

In addition to progressive CD4 + T cell immune deficiency, HIV infection is characterized by generalized immune activation, thought to arise from increased microbial exposure resulting from diminishing immunity.

Life and death as a T lymphocyte: from immune protection to HIV pathogenesis

Detailed analysis of T cell dynamics in humans is challenging and mouse models can be important tools for characterizing T cell dynamic processes. In a paper just published in Journal of Biology , Marques et al . s...

What we still don't know about AIDS

The gene complement of the ancestral bilaterian - was urbilateria a monster.

Expressed sequence tag analyses of the annelid Pomatoceros lamarckii , recently published in BMC Evolutionary Biology , are consistent with less extensive gene loss in the Lophotrochozoa than in the Ecdysozoa, but ...

The nature of cell-cycle checkpoints: facts and fallacies

The concept of checkpoint controls revolutionized our understanding of the cell cycle. Here we revisit the defining features of checkpoints and argue that failure to properly appreciate the concept is leading ...

An expanded evolutionary role for flower symmetry genes

CYCLOIDEA (CYC) -like TCP genes are critical for flower developmental patterning. Exciting recent breakthroughs, including a study by Song et al. published in BMC Evolutionary Biology , demonstrate that CYC -like ge...

Mechanisms of ubiquitin transfer by the anaphase-promoting complex

The anaphase-promoting complex (APC) is a ubiquitin-protein ligase required for the completion of mitosis in all eukaryotes. Recent mechanistic studies reveal how this remarkable enzyme combines specificity in...

Targeting TNF-α for cancer therapy

As the tumor vasculature is a key element of the tumor stroma, angiogenesis is the target of many cancer therapies. Recent work published in BMC Cell Biology describes a fusion protein that combines a peptide pre...

TEs or not TEs? That is the evolutionary question

Transposable elements (TEs) have contributed a wide range of functional sequences to their host genomes. A recent paper in BMC Molecular Biology discusses the creation of new transcripts by transposable element i...

Molecular machines or pleiomorphic ensembles: signaling complexes revisited

Signaling complexes typically consist of highly dynamic molecular ensembles that are challenging to study and to describe accurately. Conventional mechanical descriptions misrepresent this reality and can be a...

Ockham's broom: A new series

Adaptation by introgression.

Both selective and random processes can affect the outcome of natural hybridization. A recent analysis in BMC Evolutionary Biology of natural hybridization between an introduced and a native salamander reveals th...

Journal of Biology

ISSN: 1475-4924

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Mice lacking DIO3 exhibit sex-specific alterations in circadian patterns of corticosterone and gene expression in metabolic tissues

Disruption of circadian rhythms is associated with neurological, endocrine and metabolic pathologies. We have recently shown that mice lacking functional type 3 deiodinase (DIO3), the enzyme that clears thyroi...

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Optimization of seeding density of OP9 cells to improve hematopoietic differentiation efficiency

OP9 mouse stromal cell line has been widely used to induce differentiation of human embryonic stem cells (hESCs) into hematopoietic stem/progenitor cells (HSPCs). However, the whole co-culture procedure usuall...

Development of an in vitro human alveolar epithelial air-liquid interface model using a small molecule inhibitor cocktail

The alveolar epithelium is exposed to numerous stimuli, such as chemicals, viruses, and bacteria that cause a variety of pulmonary diseases through inhalation. Alveolar epithelial cells (AECs) cultured in vitr...

Mechanical stretch leads to increased caveolin-1 content and mineralization potential in extracellular vesicles from vascular smooth muscle cells

Hypertension-induced mechanical stress on vascular smooth muscle cells (VSMCs) is a known risk factor for vascular remodeling, including vascular calcification. Caveolin-1 (Cav-1), an integral structural compo...

Melatonin reduces lung injury in type 1 diabetic mice by the modulation of autophagy

In recent years, the role of autophagy has been highlighted in the pathogenesis of diabetes and inflammatory lung diseases. In this study, using a diabetic model of mice, we investigated the expression of auto...

TonEBP/NFAT5 expression is associated with cisplatin resistance and migration in macrophage-induced A549 cells

Macrophages promote angiogenesis, metastasis, and drug resistance in several cancers. Similarly, TonEBP/NFAT5 induces metastasis in renal carcinoma and colon cancer cells. However, the role of this transcripti...

Optimizing combination therapy in prostate cancer: mechanistic insights into the synergistic effects of Paclitaxel and Sulforaphane-induced apoptosis

Combination therapies in cancer treatment have demonstrated synergistic or additive outcomes while also reducing the development of drug resistance compared to monotherapy. This study explores the potential of...

CTC together with Shh and Nrf2 are prospective diagnostic markers for HNSCC

The lack of appropriate prognostic biomarkers remains a significant obstacle in the early detection of Head and Neck Squamous Cell Carcinoma (HNSCC), a cancer type with a high mortality rate. Despite considera...

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is dela...

Sumoylation of SAP130 regulates its interaction with FAF1 as well as its protein stability and transcriptional repressor function

Fas-associated factor 1 (FAF1) is a multidomain protein that interacts with diverse partners to affect numerous cellular processes. Previously, we discovered two Small Ubiquitin-like Modifier (SUMO)-interactin...

Loss of Dec1 inhibits alcohol-induced hepatic lipid accumulation and circadian rhythm disorder

Chronic alcohol exposure increases liver damage such as lipid accumulation and hepatitis, resulting in hepatic cirrhosis. Chronic alcohol intake is known to disturb circadian rhythms in humans and animals. DEC...

Association between plasma L-carnitine levels and mitochondrial DNA copy number

Mitochondria are key cytoplasmic organelles in eukaryotic cells that generate adenosine triphosphate (ATP) through the electron transport chain and oxidative phosphorylation. Mitochondrial DNA (mtDNA) copy num...

Effect of Emi1 gene silencing on the proliferation and invasion of human breast cancer cells

Breast cancer is the most common malignant tumour in women. The early silk-splitting inhibitor protein 1 Emi1 is responsible for mediating ubiquitin protein degradation. The present study investigated the effe...

TNFα induces Caspase-3 activity in hematopoietic progenitor cells CD34+, CD33+, and CD41 + of myelodysplastic syndromes

Cytopenia is the primary feature of Myelodysplastic Syndrome, even in the presence of hypercellular bone marrow. TNFα is recognized as both a proinflammatory, and proapoptotic cytokine with a well established ...

From network analysis to experimental validation: identification of regulators of non-muscle myosin II contractility using the folded-gastrulation signaling pathway

The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular pattern...

Simple, low-cost, and well-performing method, the outgrowth technique, for the isolation of cells from nasal polyps

Epithelial cells are an important part of the pathomechanism in chronic rhinosinusitis with nasal polyps. It is therefore essential to establish a robust method for the isolation and culture of epithelial cell...

Comprehensive brain tissue metabolomics and biological network technology to decipher the mechanism of hydrogen-rich water on Radiation-induced cognitive impairment in rats

Hydrogen-rich water (HRW) has been shown to prevent cognitive impairment caused by ionizing radiation. This study aimed to investigate the pharmacological effects and mechanisms of HRW on ionizing radiation by...

Mineral elements and adiposity-related consequences in adolescents with intellectual disabilities

Patients with intellectual disabilities are shown to have a limited capacity for cooperation, communication,and other biological consequences, which significantly require a specialized interest in healthcare p...

Glycyrrhizin inhibits LPS-induced inflammatory responses in goat ruminal epithelial cells in vitro

Inflammation plays a crucial role in the progression of Subacute Ruminal Acidosis (SARA). The experiment was designed to investigate anti-inflammatory effects of glycyrrhizin on goats ruminal epithelial cells ...

D-galactose-induced mitochondrial oxidative damage and apoptosis in the cochlear stria vascularis of mice

Age-related hearing loss, known as presbycusis, is the result of auditory system degeneration. Numerous studies have suggested that reactive oxygen species (ROS) and mitochondrial oxidative damage play importa...

Keratin 19 binds and regulates cytoplasmic HNRNPK mRNA targets in triple-negative breast cancer

Heterogeneous nuclear ribonucleoprotein K (HNRNPK) regulates pre-mRNA processing and long non-coding RNA localization in the nucleus. It was previously shown that shuttling of HNRNPK to the cytoplasm promotes ...

A computational peptide model induces cancer cells’ apoptosis by docking Kringle 5 to GRP78

Cells can die through a process called apoptosis in both pathological and healthy conditions. Cancer development and progression may result from abnormal apoptosis. The 78-kDa glucose-regulated protein (GRP78)...

BMP9 maintains the phenotype of HTR-8/Svneo trophoblast cells by activating the SDF1/CXCR4 pathway

Bone morphogenetic protein 9 (BMP9) has been shown to regulate processes such as angiogenesis, endothelial dysfunction, and tumorigenesis. However, the role of BMP9 in preeclampsia (PE) is unclear. The purpose...

Emodin and aloe-emodin, two potential molecules in regulating cell migration of skin cells through the MAP kinase pathway and affecting Caenorhabditis elegans thermotolerance

Emodin and aloe-emodin are two anthraquinones having positive effects in wound healing. However, their mechanism of action of wound healing is not fully understood. The MAP kinase family, which plays an active...

Knockdown of ELF4 aggravates renal injury in ischemia/reperfusion mice through promotion of pyroptosis, inflammation, oxidative stress, and endoplasmic reticulum stress

Renal ischemia/reperfusion (I/R) injury is a major cause of acute kidney injury (AKI). Dysfunction of E74-like ETS transcription factor 4 (ELF4) leads to inflammation. This research intended to look into the f...

Janus Kinase 3 phosphorylation and the JAK/STAT pathway are positively modulated by follicle-stimulating hormone (FSH) in bovine granulosa cells

Janus kinase 3 (JAK3) is a member of the JAK family of tyrosine kinase proteins involved in cytokine receptor-mediated intracellular signal transduction through the JAK/STAT signaling pathway. JAK3 was previou...

Genetic and protein interaction studies between the ciliary dyslexia candidate genes DYX1C1 and DCDC2

DYX1C1 (DNAAF4) and DCDC2 are two of the most replicated dyslexia candidate genes in genetic studies. They both have demonstrated roles in neuronal migration, in cilia growth and function and they both are cytosk...

SUMOylation of PDGF receptor α affects signaling via PLCγ and STAT3, and cell proliferation

The platelet-derived growth factor (PDGF) family of ligands exerts their cellular effects by binding to α- and β-tyrosine kinase receptors (PDGFRα and PDGFRβ, respectively). SUMOylation is an important posttra...

Myogenic differentiation of human myoblasts and Mesenchymal stromal cells under GDF11 on Poly-ɛ-caprolactone-collagen I-Polyethylene-nanofibers

For the purpose of skeletal muscle engineering, primary myoblasts (Mb) and adipogenic mesenchymal stem cells (ADSC) can be co-cultured and myogenically differentiated. Electrospun composite nanofiber scaffolds...

Computational analysis of missense variant CYP4F2*3 (V433M) in association with human CYP4F2 dysfunction: a functional and structural impact

Cytochrome P450 4F2 (CYP4F2) enzyme is a member of the CYP4 family responsible for the metabolism of fatty acids, therapeutic drugs, and signaling molecules such as arachidonic acid, tocopherols, and vitamin K...

Using RNA-seq to identify suitable housekeeping genes for hypoxia studies in human adipose-derived stem cells

Hypoxic culture conditions have been used to study the impact of oxygen deprivation has on gene expression in a number of disease models. However, hypoxia response elements present in the promoter regions of s...

SCAT8/miR-125b-5p axis triggers malignant progression of nasopharyngeal carcinoma through SCARB1

Nasopharyngeal carcinoma is a tumor with high malignancy and poor prognosis, which severely affects the health of the patients. LncRNAs and microRNAs are crucial for the occurrence and development of nasophary...

ARNTL2 upregulation of ACOT7 promotes NSCLC cell proliferation through inhibition of apoptosis and ferroptosis

Recent studies have reported that the circadian transcription factor aryl hydrocarbon receptor nuclear translocator like 2 (ARNTL2) promotes the metastatic progression of lung adenocarcinoma. However, the mole...

Evolutionary relevance of single nucleotide variants within the forebrain exclusive human accelerated enhancer regions

Human accelerated regions (HARs) are short conserved genomic sequences that have acquired significantly more nucleotide substitutions than expected in the human lineage after divergence from chimpanzees. The f...

The DNA demethylation-regulated SFRP2 dictates the progression of endometriosis via activation of the Wnt/β-catenin signaling pathway

Endometriosis cause decreases in life quality and pelvic pain in reproductive-age women. Methylation abnormalities played a functional role in the progression of endometriosis, this study aimed to explore the ...

Pre-treatment with IL-6 potentiates β-cell death induced by pro-inflammatory cytokines

Type I Diabetes mellitus (T1D) is characterized by a specific destruction of β-cells by the immune system. During this process pro-inflammatory cytokines are released in the pancreatic islets and contribute for β...

Role of the human solute carrier family 14 member 1 gene in hypoxia-induced renal cell carcinoma occurrence and its enlightenment to cancer nursing

Hypoxia is considered a critical contributor to renal cell carcinoma progression, including invasion and metastasis. However, the potential mechanisms by which it promotes invasion and metastasis have not yet ...

Cyclic tensile force modifies calvarial osteoblast function via the interplay between ERK1/2 and STAT3

Mechanical therapies, such as distraction osteogenesis, are widely used in dental clinics. During this process, the mechanisms by which tensile force triggers bone formation remain of interest. Herein, we inve...

Urine-derived mesenchymal stem cells-derived exosomes enhances survival and proliferation of aging retinal ganglion cells

This study was designed to investigate to test the effect of exosomes from urine-derived mesenchymal stem cells (USCs) on the survival and viability of aging retinal ganglion cells (RGCs), and explored the pre...

RPL11 promotes non-small cell lung cancer cell proliferation by regulating endoplasmic reticulum stress and cell autophagy

Abnormal biogenesis and ribosome free function of ribosomal proteins (RPs) is important for tumorgenesis and development. Ribosomal protein L11 (RPL11) is a component of ribosomal 60 S large subunit with diffe...

Sperm capacitation and transcripts levels are altered by in vitro THC exposure

Delta-9-tetrahydrocannabinol (THC) is the primary phytocannabinoid responsible for the psychoactive properties of cannabis and is known to interact with the endocannabinoid system, which is functionally presen...

The dual role of Nrf2 in melanoma: a systematic review

Melanoma is the most lethal type of skin cancer that originates from the malignant transformation of melanocytes. Although novel treatments have improved patient survival in melanoma, the overall prognosis rem...

Hyperoxia exposure upregulates Dvl-1 and activates Wnt/β-catenin signaling pathway in newborn rat lung

Bronchopulmonary dysplasia is a serious and lifelong pulmonary disease in premature neonates that influences around one-quarter of premature newborns. The wingless-related integration site /β-catenin signaling...

Circ-ATL1 silencing reverses the activation effects of SIRT5 on smooth muscle cellular proliferation, migration and contractility in intracranial aneurysm by adsorbing miR-455

Alterations in vascular smooth muscle cells (VSMCs) contribute to the pathogenesis of intracranial aneurysms (IAs). However, molecular mechanisms underlying these changes remain unknown. The present study aime...

HMGB1 mediates lipopolysaccharide-induced macrophage autophagy and pyroptosis

Autophagy and pyroptosis of macrophages play important protective or detrimental roles in sepsis. However, the underlying mechanisms remain unclear. High mobility group box protein 1 (HMGB1) is associated with...

N-Acetyl-L-cysteine facilitates tendon repair and promotes the tenogenic differentiation of tendon stem/progenitor cells by enhancing the integrin α5/β1/PI3K/AKT signaling

Tendon injury is associated with oxidative stress, leading to reactive oxygen species (ROS) production and inflammation. N-acetyl-L-cysteine (NAC) is a potent antioxidant. However, how NAC affects the biologic...

The 3- O sulfation of heparan sulfate proteoglycans contributes to the cellular internalization of tau aggregates

Considering the high correlation between the functional decline in Alzheimer’s disease (AD) and the propagation of aggregated tau protein, many research efforts are focused on determining the underlying molecu...

microRNA-338-3p suppresses lipopolysaccharide-induced inflammatory response in HK-2 cells

Inflammation is the most common cause of kidney damage, and inflammatory responses in a number of diseases are mediated by microRNA-338-3p (miR-338-3p). However, there are only a few reports which described th...

Overexpression of lncRNA HOXA-AS2 promotes the progression of oral squamous cell carcinoma by mediating SNX5 expression

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancers. Long non-coding RNA HOXA-AS2 (lncRNA HOXA-AS2) have been extensively studied in various cancers. However, the expression and...

Overexpressed cold inducible RNA-binding protein improves cell viability and EGF expression in glial cells

Cold inducible RNA-binding protein (CIRP) is a key protein in the hypothermic therapy. Highly expressed CIRP exerts a neuroprotective effect on neurons. The aim of this study is to provide the evidence of the ...

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2022 Citation Impact 2.8 - 2-year Impact Factor 2.9 - 5-year Impact Factor 0.678 - SNIP (Source Normalized Impact per Paper) 0.775 - SJR (SCImago Journal Rank)

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Biological Research

Special series on Microbial Interactions

The nine articles of this special issue of  Biological Research  address biochemical and genetic determinants of microbial response and tolerance to stressors in different biological models and environmental contexts. Individual articles provide a broad exploration of our current knowledge of response to stressors, with a special emphasis on metal metabolism and toxic compounds.

Special series on Antarctic Research

This special issue on Antarctic research in Biological Research comprises of recent studies, related to the discovery of several new enzymes and biotechnological applications that allow to expand the knowledge of Antarctic organisms and their potential applications.

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Cx43 hemichannels and panx1 channels contribute to ethanol-induced astrocyte dysfunction and damage

Authors: Gonzalo I. Gómez, Tanhia F. Alvear, Daniela A. Roa, Arantza Farias-Pasten, Sergio A. Vergara, Luis A. Mellado, Claudio J. Martinez-Araya, Juan Prieto-Villalobos, Claudia García-Rodríguez, Natalia Sánchez, Juan C. Sáez, Fernando C. Ortíz and Juan A. Orellana

Galectins in epithelial-mesenchymal transition: roles and mechanisms contributing to tissue repair, fibrosis and cancer metastasis

Authors: Elisa Perez-Moreno, Claudia Oyanadel, Adely de la Peña, Ronny Hernández, Francisca Pérez-Molina, Claudia Metz, Alfonso González and Andrea Soza

Glutaminolysis regulates endometrial fibrosis in intrauterine adhesion via modulating mitochondrial function

Authors: Pei Chen, Chaoshuang Ye, Yunke Huang, Bingning Xu, Tianyu Wu, Yuanhang Dong, Yang Jin, Li Zhao, Changchang Hu, Jingxia Mao and Ruijin Wu

The long-chain flavodoxin FldX1 improves the biodegradation of 4-hydroxyphenylacetate and 3-hydroxyphenylacetate and counteracts the oxidative stress associated to aromatic catabolism in Paraburkholderia xenovorans

Authors: Laura Rodríguez-Castro, Roberto E. Durán, Valentina Méndez, Flavia Dorochesi, Daniela Zühlke, Katharina Riedel and Michael Seeger

MicroRNA-148b secreted by bovine oviductal extracellular vesicles enhance embryo quality through BPM/TGF-beta pathway

Authors: Karina Cañón-Beltrán, Yulia N Cajas, Vasileios Almpanis, Sandra Guisado Egido, Alfonso Gutierrez-Adan, Encina M González and Dimitrios Rizos

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Awareness and current knowledge of breast cancer

Authors: Muhammad Akram, Mehwish Iqbal, Muhammad Daniyal and Asmat Ullah Khan

Stress and defense responses in plant secondary metabolites production

Authors: Tasiu Isah

Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency

Authors: Muhammad Anas, Fen Liao, Krishan K. Verma, Muhammad Aqeel Sarwar, Aamir Mahmood, Zhong-Liang Chen, Qiang Li, Xu-Peng Zeng, Yang Liu and Yang-Rui Li

Coping with drought: stress and adaptive mechanisms, and management through cultural and molecular alternatives in cotton as vital constituents for plant stress resilience and fitness

Authors: Aziz Khan, Xudong Pan, Ullah Najeeb, Daniel Kean Yuen Tan, Shah Fahad, Rizwan Zahoor and Honghai Luo

Biotechnological applications of archaeal enzymes from extreme environments

Authors: Ma. Ángeles Cabrera and Jenny M. Blamey

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Biological Research , formerly Archives of Experimental Medicine and Biology , was founded in 1964 and transferred to BioMed Central in 2014. An electronic archive of articles published between 1999 and 2013 can be found in the SciELO database.

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Editor’s profile

Manuel J Santos, Editor-in-Chief

Dr Santos is an Associate Professor in the Faculty of Biological Sciences and Medicine at the Pontificia Catholic University of Chile.

Dr Santos received his MD from the University of Chile and his PhD in Cell and Molecular Biology from the Pontificia Catholic University of Chile. He majored in Medical Genetics at The John Hopkins University (USA) and The René Descartes University of Paris (France), and held a post doctorate position in Cell Biology and Genetics at the Rockefeller University (USA).

His research has focused on the biogenesis of cellular organelles, particularly peroxisomes. A pioneer in this field, his research lead him to discover a new type of human genetic disease, the peroxisomal biogenesis disorders, which include Zellweger Syndrome. More recently his research has centered on studying the role of peroxisomes in Alzheimer’s disease, and he also works in the field of bioethics.

Over the span of his career, Dr Santos has published more than 70 peer reviewed papers and been the President of the Society of Biology of Chile, the Genetics Society of Chile and the Bioethical Society of Chile.

About the Society

The Chilean Biology Society (Sociedad de Biología de Chile), previously the Biological Society of Santiago, was founded in late 1928 as a subsidiary of The Societé de Biologie of Paris, France. For several years the summaries of its communications were published in Comps Rendú of the Societé de Biologie du Paris. The Society is currently a member of the International Union of Biological Sciences (IUBS).

The Chilean Biology Society promotes theoretical and experimental studies and research leading to advancement in and dissemination of the biological sciences for the benefit of the community. To accomplish this, the Society organizes periodic scientific meetings in which scientists communicate, comment and discuss research carried out in Chilean or foreign research laboratories. In addition, relations and cooperation with similar domestic and foreign institutions are stimulated, and communication by all appropriate means of biological research carried out in Chile. 

Members of the Society will receive a discount on Biological Research 's article-processing charge when they provide a discount code (which members can obtain by emailing the Society) during the submission process.  The discounted article-processing charge for Society members is £1150 in 2023.

The Society also publishes Revista Chilena de Historia Natural ( Chilean Journal of Natural History, founded in 1897).

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Korunes, KL; Myers, RB; Hardy, R; Noor, MAF

Drosophila pseudoobscura is a classic model system for the study of evolutionary genetics and genomics. Given this long-standing interest, many genome sequences have accumulated for D. pseudoobscura and closely related species D. persimilis, D. miranda, and D. lowei. To facilitate the exploration… read more about this publication  »

Zipple, MN; Roberts, EK; Alberts, SC; Beehner, JC

Bartoš et al. (2021; Mammal Review 51: 143–153; https://doi.org/10.1111/mam.12219) reviewed the mechanisms involved in the ‘Bruce effect’ – a phenomenon originally documented in inseminated female house mice Mus musculus, who block pregnancy following exposure to a novel (non-sire) male. They argue… read more about this publication  »

Byrne, M; Koop, D; Strbenac, D; Cisternas, P; Yang, JYH; Davidson, PL; Wray, G

The molecular mechanisms underlying development of the pentameral body of adult echinoderms are poorly understood but are important to solve with respect to evolution of a unique body plan that contrasts with the bilateral body plan of other deuterostomes. As Nodal and BMP2/4 signalling is involved… read more about this publication  »

Wang, Q; Xu, P; Sanchez, S; Duran, P; Andreazza, F; Isaacs, R; Dong, K

BackgroundInsects rely on their sense of smell to locate food and hosts, find mates and select sites for laying eggs. Use of volatile compounds, such as essential oils (EOs), to repel insect pests and disrupt their olfaction-driven behaviors has great practical significance in integrated pest… read more about this publication  »

Castano-Duque, L; Ghosal, S; Quilloy, FA; Mitchell-Olds, T; Dixit, S

Rice production is shifting from transplanting seedlings to direct sowing of seeds. Following heavy rains, directly sown seeds may need to germinate under anaerobic environments, but most rice (Oryza sativa) genotypes cannot survive these conditions. To identify the genetic architecture of complex… read more about this publication  »

Peng, L; Shan, X; Wang, Y; Martin, F; Vilgalys, R; Yuan, Z

Clitopilus hobsonii (Entolomataceae, Agaricales, Basidiomycetes) is a common soil saprotroph. There is also evidence that C. hobsonii can act as a root endophyte benefitting tree growth. Here, we report the genome assembly of C. hobsonii QYL-10, isolated from ectomycorrhizal root tips of Quercus… read more about this publication  »

Yan, W; Wang, B; Chan, E; Mitchell-Olds, T

The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS… read more about this publication  »

Doak, DF; Waddle, E; Langendorf, RE; Louthan, AM; Isabelle Chardon, N; Dibner, RR; Keinath, DA; Lombardi, E; Steenbock, C; Shriver, RK; Linares, C; Begoña Garcia, M; Funk, WC; Fitzpatrick, SW; Morris, WF; Peterson, ML

Structured demographic models are among the most common and useful tools in population biology. However, the introduction of integral projection models (IPMs) has caused a profound shift in the way many demographic models are conceptualized. Some researchers have argued that IPMs, by explicitly… read more about this publication  »

Kim, JH; Hilleary, R; Seroka, A; He, SY

A grand challenge facing plant scientists today is to find innovative solutions to increase global crop production in the context of an increasingly warming climate. A major roadblock to global food sufficiency is persistent loss of crops to plant diseases and insect infestations. The United… read more about this publication  »

Yuan, M; Jiang, Z; Bi, G; Nomura, K; Liu, M; Wang, Y; Cai, B; Zhou, J-M; He, SY; Xin, X-F

The plant immune system is fundamental for plant survival in natural ecosystems and for productivity in crop fields. Substantial evidence supports the prevailing notion that plants possess a two-tiered innate immune system, called pattern-triggered immunity (PTI) and effector-triggered immunity (… read more about this publication  »

Benito-Kwiecinski, S; Giandomenico, SL; Sutcliffe, M; Riis, ES; Freire-Pritchett, P; Kelava, I; Wunderlich, S; Martin, U; Wray, GA; McDole, K; Lancaster, MA

The human brain has undergone rapid expansion since humans diverged from other great apes, but the mechanism of this human-specific enlargement is still unknown. Here, we use cerebral organoids derived from human, gorilla, and chimpanzee cells to study developmental mechanisms driving evolutionary… read more about this publication  »

Lofgren, LA; Nguyen, NH; Vilgalys, R; Ruytinx, J; Liao, H-L; Branco, S; Kuo, A; LaButti, K; Lipzen, A; Andreopoulos, W; Pangilinan, J; Riley, R; Hundley, H; Na, H; Barry, K; Grigoriev, IV; Stajich, JE; Kennedy, PG

While there has been significant progress characterizing the 'symbiotic toolkit' of ectomycorrhizal (ECM) fungi, how host specificity may be encoded into ECM fungal genomes remains poorly understood. We conducted a comparative genomic analysis of ECM fungal host specialists and generalists,… read more about this publication  »

Mitchell, RM; Ames, GM; Wright, JP

Background and aimsUnderstanding impacts of altered disturbance regimes on community structure and function is a key goal for community ecology. Functional traits link species composition to ecosystem functioning. Changes in the distribution of functional traits at community scales in response to… read more about this publication  »

DeMarche, ML; Bailes, G; Hendricks, LB; Pfeifer-Meister, L; Reed, PB; Bridgham, SD; Johnson, BR; Shriver, R; Waddle, E; Wroton, H; Doak, DF; Roy, BA; Morris, WF

Spatial gradients in population growth, such as across latitudinal or elevational gradients, are often assumed to primarily be driven by variation in climate, and are frequently used to infer species' responses to climate change. Here, we use a novel demographic, mixed-model approach to dissect the… read more about this publication  »

Shaw, EC; Fowler, R; Ohadi, S; Bayly, MJ; Barrett, RA; Tibbits, J; Strand, A; Willis, CG; Donohue, K; Robeck, P; Cousens, RD

Aim: If we are able to determine the geographic origin of an invasion, as well as its known area of introduction, we can better appreciate the innate environmental tolerance of a species and the strength of selection for adaptation that colonizing populations have undergone. It also enables us to… read more about this publication  »

Rushworth, CA; Mitchell-Olds, T

Despite decades of research, the evolution of sex remains an enigma in evolutionary biology. Typically, research addresses the costs of sex and asexuality to characterize the circumstances favoring one reproductive mode. Surprisingly few studies address the influence of common traits that are, in… read more about this publication  »

Jorge, JF; Bergbreiter, S; Patek, SN

Small organisms can produce powerful, sub-millisecond impacts by moving tiny structures at high accelerations. We developed and validated a pendulum device to measure the impact energetics of microgram-sized trap-jaw ant mandibles accelerated against targets at 105 m s-2 Trap-jaw ants (… read more about this publication  »

Markunas, AM; Manivannan, PKR; Ezekian, JE; Agarwal, A; Eisner, W; Alsina, K; Allen, HD; Wray, GA; Kim, JJ; Wehrens, XHT; Landstrom, AP

Long QT syndrome (LQTS) is a genetic disease resulting in a prolonged QT interval on a resting electrocardiogram, predisposing affected individuals to polymorphic ventricular tachycardia and sudden death. Although a number of genes have been implicated in this disease, nearly one in four… read more about this publication  »

Stone, DF; Mccune, B; Pardo-De La Hoz, CJ; Magain, N; Miadlikowska, J

The new genus Sinuicella, an early successional lichen, was found on bare soil in Oregon, USA. The thallus is minute fruticose, grey to nearly black, branching isotomic dichotomous, branches round, 20-90 μm wide in water mount. The cortex is composed of interlocking cells shaped like jigsaw puzzle… read more about this publication  »

Oita, S; Ibáñez, A; Lutzoni, F; Miadlikowska, J; Geml, J; Lewis, LA; Hom, EFY; Carbone, I; U'Ren, JM; Arnold, AE

Understanding how species-rich communities persist is a foundational question in ecology. In tropical forests, tree diversity is structured by edaphic factors, climate, and biotic interactions, with seasonality playing an essential role at landscape scales: wetter and less seasonal forests… read more about this publication  »

Hibshman, JD; Webster, AK; Baugh, LR

Standard laboratory culture of Caenorhabditis elegans utilizes solid growth media with a bacterial food source. However, this culture method limits control of food availability and worm population density, factors that impact many life-history traits. Here, we describe liquid-culture protocols for… read more about this publication  »

Caves, EM; Green, PA; Zipple, MN; Bharath, D; Peters, S; Johnsen, S; Nowicki, S

AbstractSensory systems are predicted to be adapted to the perception of important stimuli, such as signals used in communication. Prior work has shown that female zebra finches perceive the carotenoid-based orange-red coloration of male beaks-a mate choice signal-categorically. Specifically,… read more about this publication  »

Reed, PB; Peterson, ML; Pfeifer-Meister, LE; Morris, WF; Doak, DF; Roy, BA; Johnson, BR; Bailes, GT; Nelson, AA; Bridgham, SD

Predicting species' range shifts under future climate is a central goal of conservation ecology. Studying populations within and beyond multiple species' current ranges can help identify whether demographic responses to climate change exhibit directionality, indicative of range shifts, and whether… read more about this publication  »

Sallee, JL; Crawford, JM; Singh, V; Kiehart, DP

Actin filament crosslinking, bundling and molecular motor proteins are necessary for the assembly of epithelial projections such as microvilli, stereocilia, hairs, and bristles. Mutations in such proteins cause defects in the shape, structure, and function of these actin - based protrusions. One… read more about this publication  »

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A Decade of Systems Biology

Han-yu chuang.

1 Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, California 92093

2 Bioinformatics Program, University of California, San Diego, La Jolla, California 92093

Matan Hofree

3 Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093

Trey Ideker

4 Department of Bioengineering, University of California, San Diego, La Jolla, California 92093

Associated Data

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: ( a ) pathway-based biomarkers, ( b ) global genetic interaction maps, ( c ) systems approaches to identify disease genes, and ( d ) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

INTRODUCTION

Nearly a decade has passed since systems biology was introduced into the language of modern biology ( Ideker et al. 2001 , Kitano 2002 ). In that time it has expanded greatly in breadth; it now embraces much of the life sciences and is used to address many research problems across humans and diverse model species ( Figure 1 ). Systems biology has also deepened considerably; many more systematic technologies and methods, both experimental and computational, are in use now than were available a decade ago.

Meta-analysis of systems biology publications over the past decade. ( a ) A map of the 34 leading topics in systems biology during the years 2000–2009. The map represents a 2D scaling of the mutual information score between topics, i.e., closely associated topics in the map represent similar themes. The size of the text is roughly proportional to the number of papers. The color gradient indicates a change in rate of citations ( from blue to purple to red ). Blue indicates topics that were more common prior to 2007; red indicates topics that have been more common since 2007 (see the Supplemental Methods Section for more details on the method and topic word lists). ( b ) Gray bars show the number of articles indexed in PubMed per year that are labeled with the Medical Subject Heading (MeSH) “Systems Biology.” As a reference, the gold dashed line shows the number of total articles in thousands indexed in PubMed per year.

Yet the field in many ways remains in its infancy. The available genome-scale experimental tools are still in an exponential development phase; new technologies turn the field on its head every few years. Even using current assays, bioinformatic methodology lags significantly behind, such that many more data are generated than possibly can be analyzed or interpreted. Moreover, and perhaps most humbling, the field still has not reached consensus on the definition of systems biology. Part of the reason is that systems biology is in vogue, and some have found it easier to change its definition than to change their research habits. However, it is evident that interesting changes are afoot in biology, and given the newness of some these changes, building consensus may take time.

Systems Biology: A Framework for Modeling Biological Systems from Systematic Measurements

Systems approaches, by necessity, involve systematic data. It is impossible to study a biological system as a whole without them. On one hand, the ability to make genome-wide (or proteome-wide or transcriptome-wide) measurements on a system is arguably the single greatest force driving the rise of systems biology. On the other hand, systems biology is not only about genome-scale measurements; it is about a philosophy and a hypothesis-driven approach for experimental design and analysis ( Ideker et al. 2001 ). Therefore, systems biology does not apply to genome-scale studies that are focused solely on discovery. Rather, it is a framework for using genome-scale experiments to perform predictive, hypothesis-driven science ( Figure 2 ). Using genome-scale data to test hypotheses is nontrivial because it requires that the hypotheses themselves be genome-scale. This, in turn, only becomes possible with a genome-scale model of the system. Of course, systematic technologies are not the only means of measuring biological systems. It is critical that systems-level models are consistent with, and validated by, detailed single-molecule measurements and literature.

Overview of the experimental process in classical biology ( top ) versus systems biology ( bottom ).

Enabled by advances in genome-scale technology, the available molecular data are growing exponentially. A property of exponential growth is that the amount of data describing a pathway that will be collected in the next year is on par with the amount of data that has ever been collected about that pathway in the history of science. In light of this fact, clearly the main challenge confronting the field is not to look back (incorporating previous findings is critical but will be comparatively easy) but to look forward to how one might plan and interpret the mountains of new data that soon will be generated.

Another principle emerging from systems biology research is that it is not enough to map out the physical components and interactions of a system—one must also map how information propagates through this system in response to perturbations. Similarly, it has proven extremely difficult to infer physical or structural interactions in the system from functional data alone (e.g., expression profiles). Thus, systems approaches must necessarily investigate both the physical and functional aspects of the system. For this reason, many approaches seek to integrate multiple data sets, each of which contains a different slice of information about system structure or state.

Finally, as previous authors have done, we distinguish between systems biology and synthetic biology. Systems biology attempts to understand the workings of natural biological systems; synthetic biology uses this understanding to construct new genetic and biochemical systems in vivo or in vitro. Several good reviews of recent progress in synthetic biology are available elsewhere ( Andrianantoandro et al. 2006 , Benner & Sismour 2005 ).

A Systems Approach to the Systems Biology Literature

To obtain a systems-level map of the current status of the field, we performed a meta-analysis of all systems biology publications recorded in PubMed over the past decade ( Figure 1 ). The field has grown from a handful of publications published in 2001 to nearly 2,000 published in 2009. Next, we mined the abstracts of systems biology articles published from 2000 to 2009 to extract popular research topics ( Figure 1 a ). We estimated the trends in publication over the decade and compared the topics prominent in systems biology publications prior to 2007 to those in the latter part of the decade (see the Supplemental Methods Section for details; follow the Supplemental Material link from the Annual Reviews home page at http://www.annualreviews.org ). Certain topics, such as gene expression analyses and evolutionary biology, have maintained their places as mainstays of systems biology ( Figure 1 a ). Others are on the rise, such as stem cells and network biology. A few topics, such as protein structure and comparative genomics, show a decline in publication rates. Nonetheless, the increase in breadth and versatility of research carried out under the banner of systems biology sends a clear message.

In the remainder of this review, we describe progress in systems approaches for mapping biological pathways and for using these maps in biomedical research. Guided by topics in the systems biology meta-map ( Figure 1 a ), we focus on four areas in particular. All of these are strongly emerging topics in systems biology over the past few years: pathway-based biomarkers and diagnosis, systematic measurement and modeling of genetic interactions, systems biology of stem cells, and identification of disease genes. Each of these topics has recently been the focal point of significant research progress brought about because of innovative use of systems-wide measurement methods and computational approaches. In addition, we review the software tools available for network visualization and interactive exploration of systems biology data, which can be used to formulate hypotheses for further investigation and discovery.

SYSTEMS APPROACHES TO MOLECULAR DIAGNOSTICS

A first area in which systems approaches have gained recent traction is molecular diagnostics. For complex diseases such as cancer, gene and protein expression profiling have become the methods of choice for identifying diagnostic biomarkers able to diagnose the severity of disease and predict future disease outcomes (reviewed by Asyali et al. 2006 , Quackenbush 2006 , Cheang et al. 2008 ). Markers are selected by scoring each individual gene or protein on how well its expression pattern can discriminate between different classes of disease or between cases and controls. The disease status of new patients is predicted using classifiers tuned to the expression levels of the markers.

Despite their promise, expression-based diagnostics continue to face serious challenges owing to their questionable accuracy when predicting patient outcomes in some diseases ( Ein-Dor et al. 2005 , Sotiriou & Piccart 2007 ). Problems are thought to arise as the result of at least two factors: cellular heterogeneity within tissues and genetic heterogeneity across patients. The impact of cellular heterogeneity depends on the nature of the disease. For some diseases, such as B cell lymphoma, the diseased cell population is well defined such that it is possible to harvest a relatively pure cell population yielding a distinct expression signature, or to subdivide a mixed B cell population on the basis of expression. In other diseases, such as breast cancer, it has been difficult to cleanly separate tumor from normal cells, such that the resulting expression profile represents an average signal diluted over a mixed cell population.

In contrast, genetic heterogeneity refers to the fact that the same genes may not be dysregulated in each patient. For instance, patient A may have protein A dysregulated, patient B may have protein B dysregulated, patient C may have protein C dysregulated, and so on. Given this disparity across patients who nevertheless may have the same clinical outcomes (e.g., aggressive cancer), classification algorithms have trouble because no single marker is indicative of the status of all (or even most) patients.

To address these problems and improve on expression-based diagnostics, several groups have begun to integrate patient expression profiles with system-wide maps of the pathways in a cell ( Anastassiou 2007 , Calvano et al. 2005 , Doniger et al. 2003 , Draghici et al. 2003 , Nibbe et al. 2009 , Pavlidis et al. 2004 , Tian et al. 2005 , Ulitsky et al. 2008a , Wei & Li 2007 ). Depending on the scenario, such pathway maps can involve signaling cascades, transcriptional regulation, or metabolic reactions. They can be as detailed as a series of discrete actions among proteins that lead to a defined end point or functional outcome, or as abstract as a functional annotation on a set of genes. Pathway information provides an overarching layer of organization that can tie seemingly disparate expression responses together into a common pattern. For instance, although any protein A, B, or C may indicate an aggressive form of disease, the knowledge that A, B, and C form a coherent module—e.g., they are subunits of a common protein complex, successive enzymes in a metabolic pathway, or successive steps in a signal transduction cascade—allows us to formulate new biomarker functions that take all of these proteins into account. Some approaches draw this knowledge from known pathways curated from the literature ( Subramanian et al. 2005 , Vert & Kanehisa 2003 ); others incorporate pathway knowledge from unbiased networks of physical protein-protein interactions ( Chuang et al. 2007 , Ma et al. 2007 , Taylor et al. 2009 , Tuck et al. 2006 ). In either case, the goal is to identify biomarkers not as lists of individual genes or proteins but as functionally related groups of genes or proteins whose aggregate expression accounts for the phenotypic differences between the different populations of patients. Unlike conventional expression diagnostics based on individual genes, these diagnostic pathway markers provide a strong biological interpretation for the association of an expression profile with a particular type of disease. As a result, the pathway-based approach can be inherently more reliable—which isn’t to say, however, that knowing the pathway relationships assures the success of a diagnostic profile.

In addition to explaining gene expression differences between phenotypes, diagnostic pathways can be used to predict the expression profiles of unknown disease states. Some of these approaches represent pathway activity with a function summarizing the expression values of member genes ( Breslin et al. 2005 , Guo et al. 2005 , Lee et al. 2008 ); other approaches estimate pathway activation probabilities based on the consistency of changes in gene expression ( Efroni et al. 2007 , Svensson et al. 2006 ). Others have engineered normal cells to activate preselected oncogenic pathways to determine gene signatures that can distinguish tumor characteristics ( Bild et al. 2006 , Glinsky et al. 2005 ). For example, Bild et al. (2006) overexpressed a panel of oncogenes, one at a time, in primary cultures of human mammary epithelial cells. The goal was to link each oncogene with a distinct set of dysregulated genes. Given these links, they showed that the expression profile of a new tumor sample could be analyzed to identify which oncogenes had been activated.

Chuang et al. (2007) demonstrated an approach that mines pathway biomarkers directly from protein-protein interaction networks. Gene expression profiles of breast cancer patients were superimposed on a human protein-protein interaction network to identify protein subnetworks able to predict cancers likely to metastasize within five years ( Figure 3 a – c ). The activity of a subnetwork was inferred by averaging the normalized expression values of its member genes. The dysregulation of a subnetwork was quantified in terms of the mutual information between subnetwork activity and patient phenotype (metastatic or nonmetastatic). Chuang et al. (2007) also showed that subnetwork markers overlap much more extensively between patient cohorts than individual marker genes and are more informative regarding cancer susceptibility.

Predictive subnetwork markers for breast cancer metastasis. ( a – c ) Subnetworks identified by Chuang et al. (2007) involving the key susceptibility regulators ( a ) TP53 , ( b ) BRCA1 , or ( c ) ERRB2 . Nodes and links represent human proteins and protein interactions, respectively. The color of each node scales with the change in expression of the corresponding gene for metastatic versus nonmetastatic cancer. The shape of each node indicates whether its gene is significantly differentially expressed ( diamond ) or not ( circle ). The predominant cellular functions are listed next to each module: M, metabolism; CT, cell and tissue remodeling; A, apoptosis; S, signaling of cell growth and survival; CR, cell proliferation and replication. Known breast cancer susceptibility genes are marked by asterisks. ( d ) BRCA1 and its interactors (e.g., BRCA2 and MRE11 , as indicated) are highly ordered (green edges indicate correlated expression between protein pairs) in surviving patients, whereas this organization is lost in patients with aggressive cancer. In contrast, interactions involving SP1 are not significantly altered. PCC denotes the Pearson’s correlation coefficient between the expression patterns of two interacting partners. Panels ( a – c ) are adapted with permission from Chuang et al. (2007) . Panel ( d ) is adapted with permission from Taylor et al. (2009) .

Rather than summarizing member gene expression into subnetwork activity, Taylor et al. (2009) proposed to measure changes in interaction coherence between member genes in a subnetwork under different phenotypes ( Figure 3 d ). The interaction coherence in a sample was defined using the difference in expression of the central hub gene in a subnetwork with each of its interacting partners. Although Taylor et al. (2009) and Chuang et al. (2007) differ in the way that they detect pathway dysregulation, both capture a common set of contributions to breast cancer (for example, BRCA1 in Figure 3 b versus ​ versus3 d ). 3 d ). Moreover, both studies find that subnetwork markers are more accurate in the classification of breast cancer metastasis than previous predictors based on collections of noninterconnected genes.

In summary, projection of gene expression profiles onto pathway databases or interaction networks is proving to be a powerful approach for understanding disease. On one hand, diagnostic pathways are more reproducible than single genes and can improve the prediction accuracy of disease states. On the other hand, the studies to date are preliminary, and much work is needed before the approach can be translated into advanced diagnostics. One useful direction will be to complement expression and pathway connectivity with other large-scale data sets that include information on genetic perturbations, epigenetic regulation, signal transduction, metabolism, and other factors. Finally, many real and functionally relevant interactions are missing in current protein-protein interaction data sets. Further insights can be expected from reanalysis of the same diseases as the data increase in coverage and quality. Nonetheless, it is clear that constructing functionally coherent, pathway-aggregated biomarkers has great inherent value versus choosing sets of independently selected genes.

GENETIC INTERACTION MAPS: A TOOLBOX WITH IMPLICATIONS FOR CANCER AND DISEASE

Cell function is governed by a large and complex network of combinatorial interactions among genes, collectively referred to as genetic interactions. Recently, several systems biology studies in yeast, fly, worm, and mammalian cell lines have made important strides in our ability to map this genetic interaction network and its impact on function. Classically, a genetic interaction is defined as the phenomenon whereby combined mutations at several genes produce a phenotype that is unexpected from any of the single mutants ( Avery & Wasserman 1992 ).Genetic interactions are often quantified under the assumption that combining two unrelated (independent) mutations should result in a multiplicative effect on phenotype, such that any deviation is considered an indication of a genetic interaction ( Bandyopadhyay et al. 2008 , Collins et al. 2006 , Costanzo et al. 2010 , Dixon et al. 2009 , Mani et al. 2008b , St Onge et al. 2007 ). A phenotypic score that is less than expected is a negative or “aggravating” interaction, whereas a score that is greater than expected is a positive or “alleviating” interaction. An extremely negative genetic interaction that is often studied is the “synthetic lethal” in which the combined gene mutations result in cell death.

When viewed globally over a genome, the network of genetic interactions becomes quite large. To appreciate the magnitude of such a network, consider that among the approximately 30,000 human genes there are on the order of a billion (30,000 2 = 900,000,000) potential pair-wise genetic interactions. In a recent near-comprehensive screen of genetic interactions in the yeast Saccharomyces cerevisiae , more than 3% of gene pairs showed signs of genetic interaction in rich media conditions ( Costanzo et al. 2010 ). Moreover, genetic interactions need not involve only pairs of genes; rather, they can involve much larger combinations.

Detection of Genetic Interactions in Model Species and Humans

The development of rapid screening techniques for genetic interactions, such as synthetic genetic arrays (SGAs) ( Tong et al. 2001 , 2004 ), diploid synthetic lethality analysis by microarray (dSLAM) ( Ooi et al. 2003 ), and epistatic miniarray profiles (E-MAP) ( Schuldiner et al. 2005 ), have allowed the quantification of genetic interaction profiles for the majority of genes in S. cerevisiae . Whereas studies of this scope have yet to be implemented in higher organisms, limited genetic interaction screens in human cell lines and model organisms such as Caenorhabditis elegans and Drosophila melanogaster , as well as screens in Schizosaccharomyces pombe , have already been conducted ( Bakal et al. 2008 , Bommi-Reddy et al. 2008 , Dixon et al. 2008 , Lehner et al. 2006 , Roguev et al. 2007 ; for a comprehensive review of epistasis and genetic interaction data sources, see Dixon et al. 2009 ).

Explicit construction of double gene knockouts in mammals remains a laborious process. Viable alternatives, such as testing combinations of RNA interference (RNAi) knockdowns ( Bommi-Reddy et al. 2008 , Yang & Stockwell 2008 , Zender et al. 2008 ), are emerging but will naturally take time to mature into genome-scale research tools. In the meantime, a potential role for genetic interaction networks in humans comes from the unlikely direction of statistical genetics, and in particular genome-wide association studies (GWAS). GWAS involves rapidly scanning genetic markers along the genome [such as single nucleotide polymorphisms (SNPs) or copy number variations (CNVs)] to find genetic variations associated with a particular phenotype, such as a heritable trait or disease ( Hirschhorn & Daly 2005 ). However, in many cases GWAS has thus far failed to explain more than a few percent of the genetic contribution to a particular disease, especially for common diseases such as type II diabetes, hypertension, or bipolar disorder ( Donnelly 2008 , Maher 2008 ). Evidence is emerging, however, that some of the missing heritability is attributable to combinatorial genetic interactions within and across pathways ( Peng et al. 2009 , Torkamani et al. 2008 , Wang et al. 2007 ). The need for inclusion of combinatorial genetic interactions also showcases the importance of developing new approaches to systems-level analysis of genetic interactions ( Benfey & Mitchell-Olds 2008 ).

Characteristics of Genetic Networks

Studies in yeast have shown the relative robustness of the cell to systematic deletions, as only a small subset (~20%) of genes are essential in rich media conditions ( Dixon et al. 2009 ). Hillenmeyer et al. (2008) , however, showed that under a variety of stress conditions this list is in fact expanded to include most protein-coding genes (~97%). As for the effect seen on single deletions, St Onge et al. (2007) reported a ~24-fold increase in the number of genetic interactions observed after exposure to methyl-methane sulfonate (MMS), a known DNA-damaging agent. Comparisons of normal versus stress conditions suggest that although the genetic network contains some degree of redundancy, it is a highly optimized response mechanism ( Costanzo et al. 2010 ).These experiments illustrate that one should beware of confusing redundancy with robustness.

Topological analysis of the yeast genetic network showed a negative correlation between a gene’s number of genetic interactions and the fitness of its deletion mutant, i.e., hubs in the genetic network tend to have a higher impact on fitness ( St Onge et al. 2007 ). Furthermore, hubs exhibit higher pleiotropy, as estimated by the variety of functional annotations of genetic interactions connected with the hub. A gene’s number of genetic interactions was also found to be correlated with its conservation across yeast species, suggesting that genetic interactions have substantial evolutionary effects ( Costanzo et al. 2010 ). Comparison of genetic interaction networks across different yeasts or between yeast and metazoans suggests that evolutionary conservation is greater at the network level, where the topological characteristics are similar, than at the level of individual interactions, which are not always shared ( Dixon et al. 2009 , Roguev et al. 2008 ).

Integration with Physical Interactions

Several studies ( Bandyopadhyay et al. 2008 , Pu et al. 2008 , Ulitsky et al. 2008b ) have attempted to integrate genetic interaction networks with networks of physical interactions between proteins. As an example, Bandyopadhyay et al. (2008) scored the likelihoods of a protein pair operating either within the same protein complex or between functionally related complexes on the basis of the strength of its genetic and physical interactions. They first learned the appropriate pattern of physical and genetic interactions from known protein complexes curated in databases. Protein pairs with a strong genetic but weak physical interaction typically were found to operate between two functionally related complexes. An agglomerative clustering procedure was then used to merge the protein pairs into increasingly larger complexes and to identify pairs of complexes interconnected by bundles of many strong genetic interactions. Figure 4 a shows three example complexes enriched for aggravating genetic interactions (i.e., synthetic lethality).

( a ) Complexes associated with RAD6-C histone ubiquitination. Protein-protein interactions are enriched among the proteins within each of the three complexes; in contrast, genetic interactions are enriched both within and between complexes. Adapted with permission from Bandyopadhyay et al. (2008) . COMPASS, complex of proteins associated with SET1 ; SWR-C, SWR1 complex; RAD6-C, RAD6 complex. ( b ) Interacting genomic loci ( green and blue ) that represent significantly dense groups of marker-marker interactions in a genome-wide association study. ( c ) Interacting complexes spanned by dense bundles of genetic interactions recovered from the same study. Adapted with permission from Hannum et al. (2009) .

Hannum et al. (2009) used a similar integrative approach to analyze and reinforce genetic interactions extracted from GWAS ( Figures 4 b , c ). They first identified pairs of SNP markers whose combined state was associated with the expression phenotypes of one or more genes. A biclustering method was then used to discover consecutive intervals of these SNP pairs on two distinct chromosomes and define a genetic interaction network. Similar to Bandyopadhyay et al. (2008) , genetic interactions were shown to be strongly enriched within and between known protein interaction complexes. The key difference, however, was that these genetic interactions had been inferred from GWAS rather than generated using directed mutations.

Genetic Interaction-Based Approaches to Cancer Therapy

A prominent treatment for cancer is to kill proliferating cancer cells through DNA damage. Because DNA-damaging agents are also toxic to normal tissue, there has been a great deal of interest in developing DNA-damage sensitizers that act specifically on cancer cells via synthetic lethal interactions ( Michod & Widmann 2007 ). In effect, the goal of these studies is to identify and target proteins encoded by genes that are synthetic lethal with cancer-causing mutations. In pioneering work, two groups ( Luo et al. 2009 , Scholl et al. 2009 ) have reported promising results from screens focused on finding synthetic lethal relationships with the KRAS oncogene ( Figure 5 ).

A model of mitotic regulation by Ras . ( a ) BI-2536, a PLK1 inhibitor, attenuates tumor growth in colorectal cancer cells (DLD-1 cell line) in vivo. Representative images of tumors after treatment are shown. ( b ) A model in which oncogenic Ras introduces mitotic stress that can be exacerbated to produce lethality by interfering with kinetochore and APC/C (anaphase-promoting complex) function. Genes shaded green are RSL (Regulators of Sex-Limitation) genes, whereas yellow genes cause Ras-specific lethality when overproduced. Red dashed lines illustrate genetic connections between Ras and aspects of mitotic regulation that lead to mitotic stress. Adapted with permission from Luo et al. (2009) .

Many sensitizers have been or are currently being investigated. Most notably, much attention has been given to a new class of sensitizers known as PARP inhibitors ( Farmer et al. 2005 ). These drugs target an enzyme involved in the base excision repair pathway, which is synthetic lethal with the homologous recombination pathway genes BRCA1 and BRCA2 that are commonly mutated in breast cancer. In addition, farnesyltransferase inhibitors have reached phase III clinical trials, an inhibitor to the cell cycle checkpoint kinaseChk1 is in phase II, and diverse other compounds, such as ataxia telangiectasia mutated ( ATM ) kinase inhibitors, are under preclinical development. Significant opportunity remains to identify many other potential molecular targets for tumor sensitization, and to date, DNA damage response pathways appear to be a hotbed of such targets. Thus, in addition to the long-term goals of comprehensively mapping the genetic interaction network in different cells under various conditions, the systematic discovery of genetic interactions has the potential to profoundly change the treatment of cancer ( Mendes-Pereira et al. 2009 , Morgan et al. 2010 ).

SYSTEMS APPROACHES TO IDENTIFY DISEASE GENES

The search for disease-causing genes is a long-standing goal of human genetics. Despite several success stories [e.g., identification of the genetic basis of cystic fibrosis ( Rommens et al. 1989 ), Tay-Sachs ( Harding 1983 ), and Huntington’s disease ( Myers 2004 )], many diseases with quantifiably substantial genetic components continue to elude detailed genetic explanations ( Culverhouse et al. 2002 , Moore 2003 ). For this reason, systems approaches are playing an increasing role in this area through the computational integration of multiple types of genome-wide measurements ( Adler et al. 2006 , Ergün et al. 2007 , Franke et al. 2006 , Lage et al. 2007 , Mani et al. 2008b , Mullighan et al. 2007 , Oti et al. 2006 , Tomlins et al. 2005 , Yao et al. 2006 ). Several groups have promoted the idea that similar diseases are caused by mutations in different genes that are part of the same functional module ( Goh et al. 2007 , Oti & Brunner 2007 ). The approaches differ in the underlying data sets used, but most of them involve superimposing a set of candidate genes alongside a set of known disease genes on a physical or functional network ( Franke et al. 2006 , Lage et al. 2007 , Oti et al. 2006 ).

Other methods do not depend on prior knowledge of disease genes but instead infer molecular interaction networks to locate susceptibility genes. For example, Amit et al. (2009) used an RNAi perturbation strategy in mouse dendritic cells to reconstruct the transcriptional network downstream of the Toll-like receptors (TLRs), an important protein family in initiation of pathogen-specific immune responses ( Figure 6 ). Candidate regulators were chosen on the basis of a time course of mRNA expression measured after stimulation with pathogen-derived components. The regulators serve as a gene signature of the immune response in the presence of pathogens. In particular, they identified 144 candidate regulators whose expression changed in response to at least one stimulus. Next, each of the candidate regulators was perturbed (knocked down) by a library of validated lentiviral short hairpin RNAs. Gene expression profiles were gathered under each perturbation and used to infer the regulatory network. The final network included 24 core regulators, affecting the expression patterns of multiple targets, four of which were validated experimentally. They further identified 76 fine-tuners with fewer targets. Together these networks shed light on the regulatory dynamics of the immune response in mammals.

A systematic strategy for network reconstruction. ( a ) Cell state is measured using array-based mRNA expression profiles. ( b ) From these data, a set of putative regulators is selected. TF, transcription factor; CF, chromatin modifier factor; RNA bp, RNA-binding protein. ( c ) The network is perturbed with lentiviral short hairpin RNA (shRNA) against each regulator, followed by measurement of signature genes. ( d ) These shRNA profiling measurements are used to inform network reconstruction. Adapted with permission from Amit et al. (2009) .

Another approach for de novo identification of disease genes was developed by Wang et al. (2009) ,who dissected gene expression profiles to infer posttranslational modulators of the MYC transcription factor. Modulators affect a transcription factor at the level of phosphorylation, acetylation, and ubiquitination and are difficult to detect systematically using largescale methods ( Linding et al. 2007 ). However, key modulators were efficiently identified by computing an information-theoretic measure of correlation between the expression profile of MYC and its direct transcriptional targets, given the expression of a “third party” candidate modulator. Candidate modulators were selected for which the expression level was found to significantly influence the correlation between MYC and its targets. Using a similar information-theoretic measure, Mani et al. (2008a) constructed a network of B cell transcriptional interactions and interrogated it for cancer genes. A similar information-theoretic measure was used to find pairs of interactions in the network that gain or lose correlation when comparing a B cell lymphoma tumor with a reference B cell. They demonstrated that pathways enriched for such high and low correlations may be implicated in pro-oncogenic processes. As a specific example, their method recovered BCL2 and SMAD1 in follicular B cell lymphoma. Both of these are oncogenes known to cause cancer but that are not detected through a standard analysis of differential gene expression. Although this approach used expression measurements and is thus unable to capture the effects of posttranslational regulation, the framework can be easily extended to include measurements of protein level as such high throughput data become more commonly available.

Ergün et al. (2007) discovered key mediators in metastatic and nonrecurrent prostate cancers through the use of a regulatory interaction network constructed from a reference set of 1,144 microarray expression profiles spanning seven different cancer types. The known prostate cancer metastasis genes, androgenic receptor ( AR ) and other genes from the AR pathway, were recovered among the top modulators in metastatic samples but not in non-metastatic ones.

STEM CELL SYSTEMS BIOLOGY AND COMPUTATION OF CELL FATE

Cell fate decisions involve coordinated dynamic expression and regulatory control of hundreds of genes in response to both internal and external stimuli. To dissect the complex interplay among these regulatory pathways, recent studies in stem cell biology have begun to combine classical experimental techniques with emerging high-throughput experimental techniques such as screens for RNAi, genome-wide mRNA expression profiling, large-scale chromatin immunoprecipitation (ChIP), and mass spectrometry–based proteomics ( Chen et al. 2008 , Kidder et al. 2008 , Spooncer et al. 2008 ). How these vast amounts of data can be used to build a quantitative and predictive model of cell fate control is one of the key challenges in systems biology and stem cell research.

Numerous efforts have been devoted to characterizing the molecular components involved in self-renewal of embryonic stem (ES) cells and differentiation of stem cells along specific lineages. Owing to dramatic advances in genome-wide ChIP technology, the target genes of 20 key ES cell transcription factors, including NANOG ( Boyer et al. 2005 , Loh et al. 2006 , Mathur et al. 2008 ), OCT4 ( Boyer et al. 2005 , Loh et al. 2006 , Mathur et al. 2008 ), SOX2 ( Boyer et al. 2005 ), and other factors ( Boyer et al. 2005 , 2006 ; Cole et al. 2008 ; Jiang et al. 2008 ; Johnson et al. 2008 ; Kidder et al. 2008 ; Kim et al. 2008 ; Liu et al. 2008 ; Loh et al. 2006 ; Mathur et al. 2008 ), have now been identified. An ES cell transcriptional circuit has been assembled through integration of these separate ChIP studies, which cover approximately 50,250 putative protein-DNA interactions that have been identified specifically in ES cells ( MacArthur et al. 2009 ). Moreover, several studies have reported that epigenetic regulation of the key transcription factors by way of chromatin structure ( Bernstein et al. 2006 , Guenther et al. 2007 , Mikkelsen et al. 2007 ) or DNA methylation ( Fouse et al. 2008 , Lagarkova et al. 2006 , Shen et al. 2006 , Yeo et al. 2007 ) also contributes to the maintenance of pluripotence (reviewed by Bibikova et al. 2008 ). In addition to epigenetic marks, microRNAs (miRNAs) ( Marson et al. 2008 ) and signaling pathways ( Chen et al. 2008 ) have also been connected to the dynamic balance of ES transcriptional control.

Wang et al. (2006) reported a different take on stem cell systems biology; they assembled a high-quality protein-protein interaction network centered on the NANOG transcription factor in mouse ES cells. They used iterative immunoprecipitation experiments to pull down proteins that physically associate with NANOG , after which mass spectrometry was used to identify the components of the NANOG interactome. Interestingly, the NANOG interactome is highly enriched in the transcription factors of the core ES cell transcriptional circuit, and many of these factors also regulate the expression of other members of the NANOG protein-protein interaction network. This indicates that stem cell fate control is highly dynamic and involves combinatorial interactions between key transcription factors and the genes that encode them. Figure 7 shows the current model of this intrinsically complex but coordinated protein-protein and protein-DNA interaction network.

Core embryonic regulatory networks for cell fate decisions. ( a ) High-confidence protein-protein interactions between the transcription factor NANOG and NANOG-associated proteins. An iterative proteomics approach was adapted to identify proteins that physically associate with NANOG and NANOG-associated proteins by using affinity purification in conjunction with mass spectrometry ( Wang et al. 2006 ). ( b ) Transcription factor binding (protein-DNA) interactions from the data generated by various recent high-throughput chromatin immunoprecipitation (ChIP) experiments. Reproduced with permission from MacArthur et al. (2009) .

Müller et al. (2008) reconstructed an extended stem cell regulatory network using a computational approach to integrate publicly available gene expression profiles and protein interaction networks. They first clustered pluripotent, multipotent, and differentiated human cells on the basis of gene expression and identified a set of genes that are specifically upregulated in undifferentiated pluripotent cells (pluripotency-related genes). Next, using a previously compiled network of human protein-protein and protein-DNA interactions including those in the NANOG interactome ( Wang et al. 2006 ), a collection of subnetworks induced by these pluripotency-related genes was identified using a graph-theoretic algorithm ( Ulitsky & Shamir 2007 ). This collection of subnetworks, which the authors name PluriNet, contains mostly novel interactions; few have been well characterized in stem cells. Nonetheless, the collection seems to represent common cellular machineries shared by pluripotent cells (including ES cells, embryonal carcinomas, and induced pluripotent cells).

Another recent study has revealed a large map of transcription factor combinations that may point the way to understanding, and perhaps altering, cell fate decisions. Using the mammalian two-hybrid (M2H) system, Ravasi et al. (2010) generated a database of all pairwise protein-protein interactions among the majority (~1,200) of human transcription factors. From these data, they extracted an interaction network of 15 homeobox transcription factors for which the expression levels were strongly associated with tissue type. The homeobox network was also shown to be capable of stratifying the stem cell expression profiles that had been collected by Muller et al. (2008) into the germ layer from which each was derived (endoderm, mesoderm, ectoderm). It has long been appreciated that combinatorial transcription factor interactions play an important role in cell commitment to different tissue lineages; the work by Ravasi et al. (2010) maps out precisely what some of these combinations are.

All of the studies described above support the idea that pluripotency and self-renewal are under tight control by a dynamic and highly complex regulatory network involving protein-protein interactions, transcription factors, signaling pathways, miRNAs, and other epigenetic modifiers. Meanwhile, follow-up experiments are needed to test these inferred regulatory interactions and their effects on stem cell fate. Integration of large-scale RNAi perturbations with genome-wide ChIP experiments and subsequent gene expression profiling ( Ding et al. 2009 , Hu et al. 2009 ) has been shown to be useful in confirming a set of transcriptional interactions and their effects on ES cell fate regulation. A next step is to understand how different internal and external stimuli can affect the dynamics of the regulatory network in ES cells. The thorough understanding of such dynamics will enable human control over cell fate decisions and, ultimately, tissue engineering and regenerative medicine.

SYSTEMS BIOLOGY SOFTWARE

Considerable time and resources have been expended on developing computational tools for answering systems-level research questions. To effectively analyze systems data, a software tool must meet several requirements. First, it must handle genome-scale data sets. Second, the tool must not be restricted to a single data type but be able to integrate multiple measurements of a system. Third, the software should assist with mapping and modeling of networks and pathways from component data sets. Fourth, it should provide an intuitive interface and visual display of both the data and models.

A number of software packages have been developed to address these requirements. Typically, these packages view the landscape of biological data as belonging to either of two categories: ( a ) data pertaining to molecular components and their states, and ( b ) data pertaining to molecular interactions. In what follows, we give a sampling of some of these robust integrative software tools available for systems biology research. Some bioinformatics software is intended for those with an in-depth knowledge of computer science; we focus instead on software tools that are geared toward cell biologists.

Cytoscape is a free bioinformatics environment for integration, visualization, and query of biological networks ( Figure 8 ). Cytoscape’s core software component provides functionality for data import and export, integration of molecular states with molecular interactions, network and integrated data visualization, and data filtering and query tools. Cytoscape’s VizMapper enables attribute-to-visual mappings, which control visual aspects of nodes and edges (e.g., shape, color, size) based on their molecular states (called node attributes). Such mappings allow overlay of multiple data types in a network context.

Graphical user interface of Cytoscape. Each window showcases a different analysis or visualization of protein interaction networks and integrated data.

Cytoscape is developed in Java and disseminated under an open source license (the GNU Lesser General Public License, a permissive software license published by the Free Software Foundation). It has been integrated with many other software tools, including stand-alone applications (e.g., geWorkbench, http://wiki.c2b2.columbia.edu/workbench/index.php ), Web sites such as a network image generator (e.g., Harvard Gene Functional Annotation Prediction Browser, http://func.med.harvard.edu/site/yeast/ ), and major network and pathway databases, including the Biomolecular Interaction Network Database (BIND, http://www.bind.ca/ ), Reactome ( http://www.reactome.org/ ), the Database of Interacting Proteins ( http://dip.doe-mbi.ucla.edu/ ), the Michigan Molecular Interactions database (MiMI http://mimi.ncibi.org ), and Pathway Commons ( http://pathwaycommons.org ). Commercial software companies have also used Cytoscape, including Oracle, Agilent GeneSpring and GeneGO (see below).

The Cytoscape core is extended through a straightforward plugin architecture, which allows rapid prototyping and development of advanced computational analyses and features. The active involvement in Cytoscape plugin development by many third-party programmers attests to the success of Cytoscape as an open source bioinformatics computing environment. Since 2004 (Cytoscape v2.0–v2.6), more than 74 publicly available plugins have been developed, 46 of which have maintained full compatibility with the latest Cytoscape releases (v2.5 or v2.6) ( Cline et al. 2007 , Shannon et al. 2003 ).

NAViGaTOR, another open source network visualization package, is an alternative to Cytoscape. Its use of hardware-based graphics accelerators using Open Graphics Library (openGL) allows fast rendering and visualization of extremely large networks. Interesting options include the ability to visualize graphs using both 2D and 3D views and the ability to collapse nodes into a single “meta node.” NAViGaTOR supports an application programming interface (API) for future plug-ins as well as a variety of data formats. It boasts a lasso selection option and a book marking feature to facilitate manual layout and other operations on a network ( Brown et al. 2009 ).

VisANT is a lightweight network visualization tool able to run as a browser-based applet or as a standalone Java program. Of particular interest is its name resolution feature, which attempts to map all nodes in the network to distinct gene names such that two proteins coded by a single gene are always mapped as one entity. This name-mapping feature is one of the most easy to use and streamlined of any software package we review here; it is well designed for the common case with scalability in mind. For large data sets, VisANT has been tested at representing more than 200,000 nodes on a machine with 1 Gb of random-access memory (RAM). Another interesting feature is the representation of metagraphs, whereby a single node can contain a subgraph. VisANT is also integrated with an online database featuring more than 450,000 interactions in dozens of organisms ( Hu et al. 2008 ).

Cell Designer is a structured diagram editor for drawing gene regulatory and biochemical networks ( Figures 9 and ​ and10). 10 ). Users can browse or modify networks as process diagrams ( Kitano et al. 2005 ) and store the networks in systems biology markup language ( Hucka et al. 2003 ), a standard for representing models of biochemical and gene regulatory networks. A unique feature of Cell Designer is that networks are able to link with simulations. Users can view the dynamics of a network under the input parameters through an intuitive graphical interface. Cell Designer is implemented in Java and thus supports various operating systems. The recent releases integrate several simulation/analysis software packages ( Funahashi et al. 2008 ).

Screenshot of Cell Designer when drawing a network as process diagrams.

Screenshot of Cell Designer when stimulating a network model given different input parameters.

Another open-source option is Pathway Assist. The focus of this tool is an automated natural language processing-based information extraction system for protein-protein and gene-gene functional interactions. Pathway Assist also provides a native curated database of protein interactions and cellular pathways. Its text-mining tool can extract biological interactions by reading digital text documents (e.g., biomedical journal articles and abstracts). It efficiently scans sentences, searching for co-occurrences of biological terms and connecting verbs (e.g., the keywords “binds,” “inhibits,” “modulates” or “phosphorylates”) between the co-occurring terms. The bundled database contains at present approximately 500,000 biological interactions among more than 50,000 proteins from several organisms extracted from the current literature ( Nikitin et al. 2003 ). It is available for download upon request.

Finally, two commercial packages are also available—GeneGO ( Nikolsky et al. 2005 ) and Ingenuity—both of which offer a comprehensive product aimed at industry and academia.

SUMMARY AND CONCLUSIONS

In this review, we have visited four nascent and emerging areas in the field of systems biology. An overarching principle, and one we have tried to highlight throughout, is that systematic measurement techniques coupled with the use of network models lead to the discovery of novel biology and medicine. Although one can implement this paradigm in several ways, we have attempted to point out some of the exemplars that have led to big wins in the study of development and disease.

One of the main challenges systems biology will face over the next decade is in breaking the divide between classical and high-throughput methods. Its role is not to replace any of the classical techniques from biochemistry or genetics but to provide a set of organizing principles that integrate these methods ( Figure 2 ). The way forward is undoubtedly through close integration of multiple disciplines to crack the biological system using every means possible.

Supplementary Material

Supplemental methods section.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

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Collection  12 March 2023

Top 100 in Cell and Molecular Biology - 2022

This collection highlights our most downloaded* cell and molecular biology papers published in 2022. Featuring authors from around the world, these papers showcase valuable research from an international community.

You can also view the top papers across various subject areas here .

*Data obtained from SN Insights, which is based on Digital Science's Dimensions.

HS3ST2 expression induces the cell autonomous aggregation of tau

• M. B. Huynh
• N. Rebergue
• D. Papy-Garcia

Therapeutic enzyme engineering using a generative neural network

• Andrew Giessel
• Athanasios Dousis
• Stuart Licht

Cinnamon extract improves abnormalities in glucose tolerance by decreasing Acyl-CoA synthetase long-chain family 1 expression in adipocytes

• Tsubame Nishikai-Shen
• Tomomi Hosono-Fukao
• Taiichiro Seki

Different methods of detaching adherent cells and their effects on the cell surface expression of Fas receptor and Fas ligand

• Ting-Yu Lai
• Chih-Yuan Lee

Folic acid depletion as well as oversupplementation helps in the progression of hepatocarcinogenesis in HepG2 cells

• Renuka Sharma
• Taqveema Ali
• Jyotdeep Kaur

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble

• Martina Audagnotto
• Werngard Czechtizky
• Johan Ulander

LIM Tracker: a software package for cell tracking and analysis with advanced interactivity

• Hideya Aragaki
• Katsunori Ogoh
• Kazuhiro Aoki

Anti-nucleocapsid antibodies enhance the production of IL-6 induced by SARS-CoV-2 N protein

• Emi E. Nakayama
• Ritsuko Kubota-Koketsu
• Tatsuo Shioda

Prediction of protein–protein interaction using graph neural networks

• Kanchan Jha
• Sriparna Saha
• Hiteshi Singh

Liver injury in non-alcoholic fatty liver disease is associated with urea cycle enzyme dysregulation

• Rocío Gallego-Durán
• Javier Ampuero
• Manuel Romero-Gómez

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

• Alexander Perminov
• Rajiv Berry

Reduced expression of mitochondrial complex I subunit Ndufs2 does not impact healthspan in mice

• Gregory S. McElroy
• Ram P. Chakrabarty
• Navdeep S. Chandel

LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction

• Zichen Wang
• Steven A. Combs
• Peter M. Clark

Optimization of methods for the accurate characterization of whole blood neutrophils

• Ashley N. Connelly
• Richard P. H. Huijbregts

Confocal microscopy analysis reveals that only a small proportion of extracellular vesicles are successfully labelled with commonly utilised staining methods

• Genevieve E. Melling
• Ross Conlon
• David R. F. Carter

Endothelial inflammation and neutrophil transmigration are modulated by extracellular matrix composition in an inflammation-on-a-chip model

• Rebecca B. Riddle
• Karin Jennbacken
• Matthew T. Harper

Engineering and validation of a dual luciferase reporter system for quantitative and systematic assessment of regulatory sequences in Chinese hamster ovary cells

• Yagmur Toktay
• Bengisu Dayanc
• Serif Senturk

Capsaicin directly promotes adipocyte browning in the chemical compound-induced brown adipocytes converted from human dermal fibroblasts

• Yukimasa Takeda

Antidiabetic activity of avocado seeds ( Persea americana Mill.) in diabetic rats via activation of PI3K/AKT signaling pathway

• Oluwafemi Adeleke Ojo
• Jennifer Chidubem Amanze
• Adebola Busola Ojo

Low dose DMSO treatment induces oligomerization and accelerates aggregation of α-synuclein

• Lasse Reimer
• Caroline Haikal
• Poul Henning Jensen

Astrocytes deficient in circadian clock gene Bmal1 show enhanced activation responses to amyloid-beta pathology without changing plaque burden

• Celia A. McKee
• Erik S. Musiek

Dual-expression system for blue fluorescent protein optimization

• Stavrini Papadaki
• Xinyue Wang
• Kiryl D. Piatkevich

Design and simulation of the liposomal model by using a coarse-grained molecular dynamics approach towards drug delivery goals

• Jalil Parchekani
• Abdollah Allahverdi
• Hossein Naderi-Manesh

Metabolites of soil microorganisms modulate amyloid β production in Alzheimer’s neurons

• Takayuki Kondo
• Tsuyoshi Yamamoto
• Haruhisa Inoue

Transposase-CRISPR mediated targeted integration (TransCRISTI) in the human genome

• Mahere Rezazade Bazaz
• Mohammad M. Ghahramani Seno
• Hesam Dehghani

Super-resolution visualization of chromatin loop folding in human lymphoblastoid cells using interferometric photoactivated localization microscopy

• Zofia Parteka-Tojek
• Jacqueline Jufen Zhu

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Advice to a Young Mathematical Biologist

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• Published: 09 April 2024
• Volume 86 , article number  52 , ( 2024 )

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This paper offers advice to early-mid career researchers in Mathematical Biology from ten past and current Presidents of the Society for Mathematical Biology. The topics covered include deciding if a career in academia is right for you; finding and working with a mentor; building collaborations and working with those from other disciplines; formulating a research question; writing a paper; reviewing papers; networking; writing fellowship or grant proposals; applying for faculty positions; and preparing and giving lectures. While written with mathematical biologists in mind, it is hoped that this paper will be of use to early and mid career researchers across the mathematical, physical and life sciences, as they embark on careers in these disciplines.

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1 Introduction

Early-mid career researchers in Mathematical Biology face a particular set of challenges. As they develop in their career, a number of skills need to be learnt, most of which are not taught in a typical undergraduate degree. In this paper, ten leading mathematical biologists—all current or former Presidents of the Society for Mathematical Biology (SMB)—share their advice on a number of areas of particular interest to early and mid career researchers. While written with mathematical biologists in mind, much of the advice presented here is of relevance to any researcher working in the life, physical or mathematical sciences. It is hoped that this paper will prove a valuable resource to early and mid career researchers as they make the first steps in their academic journey, providing a helping hand from those who have trodden the road before them.

The idea for this paper occurred to me following the excellent past Presidents’ panel discussion, organised by Prof. Heiko Enderling, at the 2023 SMB conference, held at The Ohio State University in Columbus, Ohio. This was an inspiring session, with many useful insights shared by some of the greats in the field. It struck me that it would be good to capture the insights from some of these researchers in a permanent way, and that this would be of particular interest and benefit to early/mid career researchers.

All of the living past and current SMB Presidents were contacted, and to those who were able to contribute, a series of questions was posed, inviting their top tips and advice in a number of areas relevant to early/mid career researchers. These questions consisted of a subset of ten specific topics, together with two, more general questions, which were posed to all contributors. Responses were then compiled, ordered and edited to provide coherent guidance in each area.

The advice offered here is not intended to be exhaustive. Rather, it is hoped that this will be a starting point, bringing together guidance on a range of topics into a single place, leaving the reader to explore specific areas in greater depth as desired. As with any advice, it is left to the reader to follow or leave at their discretion.

The title of this article is a homage to Prof. Sir Peter Medawar’s book ‘Advice To A Young Scientist’ (Medawar, 1979 ) and to the later multi-author chapter ‘Advice to a Young Mathematician’ in The Princeton Companion to Mathematics (Atiyah et al., 2008 ); both of which are recommended. To the best of my knowledge, this is the first paper to offer guidance specifically to early/mid career mathematical biologists.

In what follows, we cover ten specific topics: ‘Deciding if a career in academia is right for you’, ‘Finding and working with a mentor’, ‘Building collaborations and working with those from other disciplines’, ‘Formulating a research question’, ‘Writing a paper’, ‘Reviewing papers’, ‘Networking’, ‘Writing fellowship or grant proposals’, ‘Applying for faculty positions’ and ‘Preparing and giving lectures’; together with two general topics: ‘What do you wish you had known when you were an early-mid career researcher?’ and ‘Some final words of advice’. These sections can be read in any order or in isolation, depending on the needs and interests of the reader.

2 Deciding if a Career in Academia is Right for You

Many of us may wonder if the academic path is the right one for us. This question might occur when deciding whether or not to pursue a doctorate, to apply for postdoctoral or faculty positions, or even whether to remain in academia, having obtained a permanent position. Whatever your stage, the following advice may be helpful to bear in mind.

Make a list of things that are important to you, what you want to accomplish in your professional life and what will make you happy going to work every day for the rest of your life.

If you are self-driven, have lots of questions, like to work and meet with people, and like to share your work in different venues (e.g. papers and presentations), you could consider a career in academia.

A career in academia is not easy.

You need to consider what kind of academic you would like to be: more research focused, or more teaching focused. Do you want to have a large or small group, or work at a large or small school?

You also need to consider that there is a lot more to an academic job than what you may have experienced during your undergrad/PhD/postdoc. Talk to PhD students, postdoctoral fellows and faculty to find out what they do from day to day, and get a sense of what the job entails. Learn what they like about their roles and what they wish was different.

Talk to many professionals outside academia about their experiences.

3 Finding and Working with a Mentor

The concept of a mentor is a familiar one, both historically and in popular culture: Plato had Socrates, Luke Skywalker had Obi-Wan Kenobi, Bertrand Russell had Alfred North Whitehead and Frodo had Gandalf. Though familiar, finding and developing such a relationship can be difficult. Here are some expert tips on how to navigate this area.

The mentor is probably the most important part of your academic career.

Finding a mentor

You can find a mentor in many different ways:

Get to know the faculty in your research institution;

Talk to people at conferences;

Participate in mentoring programs.

Have a one-on-one conversation about ideas and what the potential mentor looks for.

Identify what YOU need from a mentor. Make sure that you communicate your needs to a prospective mentor and evaluate if they can help you in your academic journey.

Be honest about your interests.

The best science is not necessarily done by the mentor that best serves your needs, though make sure the research approach of the mentor excites you.

Try to visit and meet members of the potential mentor’s group, talking with former/current students/collaborators/mentees. This is important, not least because it will enable you to check the potential mentor’s reputation. This will also help you to evaluate if their mentorship style is right for you.

Take into account the breadth of the institution, and especially the department, and the potential to interact with others outside the group.

Working with a mentor

Expect the relationship to develop and change over time.

Mentorship can be developed very naturally—through discussion at conferences and workshops, and then some emails in between.

Make time for the relationship to develop in social contexts in connection with or outside of research discussions (e.g. coffee/tea/beer etc. time).

Do not agree to work on a project if it does not align with your interests, but be open to suggestions of new projects or research questions/approaches.

4 Building Collaborations and Working with Those from Other Disciplines

Given the intrinsic interdisciplinarity of mathematical biology, the ability to build and grow fruitful collaborations is key to developing biologically faithful and impactful models. This is not something that is usually taught at the undergraduate level, but rather is learned on-the-job, with a degree of trial-and-error(/-improvement). While this is a rite-of-passage that all mathematical biologists must pass through—and, indeed, a lifelong learning process—here are few tips to smooth the way.

Listen carefully to lectures on topics from other disciplines, and read review papers carefully to identify what questions motivate that discipline/topic. Ask yourself in what way you could contribute to answering such questions using your skill-set.

Learn a lot about the subject matter. Attend experiments when they are being done.

Follow your heart and make the effort to work with people who you find interesting and exciting.

Find someone who is open to theoretical approaches and who is a person with whom you get along really well.

It can take a while to build a good collaboration, so be patient, and invest in a few possible directions. Usually one or another will eventually pan out.

Trust that your collaborators know what they are talking about.

Ask a lot of stupid questions, balancing keeping expectations low with occasional moments of surprising brilliance.

Be clear about shared responsibilities.

Be willing to suppress your ego. Remember that what makes the work interesting is the experiments rather than the theory.

Learn the jargon of the biological discipline(s) relevant to your research.

Explain your ideas in plain English. Do not expect potential collaborators to know or be familiar with mathematical jargon or methods.

Explain what your methods could do to help test hypotheses or to analyse data, or to help with the design of experiments.

Try to get in a situation where you can help design the experiments to provide data needed for analysis.

Biological experiments usually cost a lot of money and take a lot of time. Do not expect that a collaborator will immediately agree to do your favourite experiment. (Sometimes, you have to make-do with data from the literature.)

Be willing to pay any students who may work on the theory and perhaps other costs associated with doing the experiments. Working on joint grants is one way to do this but that takes patience.

5 Formulating a Research Question

As any Douglas Adams fan will know, the key to making discoveries lies in asking the right questions. The following advice may be helpful in deciding upon a research topic and what question(s) to ask.

Find a problem that really interest you, about which you are passionate and want to know the answer, and do not care what others think.

Be driven by the research question, not by the methods you will use.

Find a topic that will potentially expand the field, not something that is just incremental.

There are many kinds of research questions: explaining a puzzling data set; testing a hypothesis for some mechanism; finding some optimal strategy; making a long-term prediction. Each case would imply a different strategy.

To find new interesting quantitative questions, read a number of recent review papers on the topic of your choice. Find sentences such as ‘The mechanism for this observed behaviour is poorly understood’, and look for key areas where a knowledge-gap is identified. Be sure that these questions are not just experimental ones. Be sure that some facts are known and/or some data is available on which to construct your model, for example.

Be open to approach by colleagues from other disciplines. Listen to their ideas and motivation, and assess whether your skills could be useful, or whether other colleagues have just the right tools to be helpful.

6 Writing a Paper

Most mathematical biologists begin by taking an undergraduate degree in mathematics, spending the bulk of their time working through a series of problem sheets. As such, when they come to do a doctorate and begin writing their first paper, it may be some years since they were required to write at any length. Further, the process of writing an academic article is unlike that of writing a secondary/high school essay. The following advice should be of help in providing a possible approach to writing papers, while also highlighting some common pitfalls.

Some general points

Do your literature review well: you do not want to submit a manuscript that is missing important references.

Spend time critically reviewing your results. Do they make sense? What are some questions that reviewers might have? Are any results difficult to understand?

Do not make the paper too long. Figure out what you want to say in a direct way.

A possible approach

Let us assume you have wrapped up an original piece of research and you are ready to write your first paper. The first step is to get your work organized in a logical, convincing fashion. You have probably already done this in preparing for your committee meetings, student presentations and poster sessions. A good MS PowerPoint presentation is a great place to start.

Next, consider the audience you want to reach. Defining your audience will dictate what journal to submit to and also what background information you need to include in the introduction.

Write an outline, using the standard format of a scientific publication.

Title: start with a working title; it may change later.

Abstract: write this last!

Introduction: make an outline, with your target audience in mind.

arrange your research in a logical fashion;

sketch your figures in some detail (and write cogent legends);

consider what tables you will need;

push some results to ‘Supplementary Material’ to stress the main points.

Discussion: make notes along the way, but write this part later.

Now that you are ready to start writing, keep the following Four Cs in mind.

Correct. Everything you write must be scientifically correct, to the best of your knowledge. Check each sentence and every equation. Make sure you have provided the correct parameter values for all your calculations.

Clear. Now that everything is correct, you must communicate your results clearly to your audience. You do not have to tell people what DNA means, but do not skip over important things that the reader needs to know. It is helpful here to get someone else’s point of view—on joint authored papers, it is the responsibility of all authors to make sure that what is written is clear. Some important points to note:

Often papers are not structured in a logical way, and read like a stream of consciousness. Look at the logical structure of your flow of ideas to make sure that your argument will make sense to your readers.

In this regard, basic grammar rules are important, especially coherent paragraphs with topical sentences. Do not let your paragraphs get too long; most long paragraphs can be broken into two or more separate ideas.

Watch how you use pronouns—they can be dangerous. You may know what your pronoun is referring to, but your reader may not. When a reader comes across a pronoun, he/she typically assumes that the pronoun refers to the last noun mentioned in the previous sentence. If the reader has to look further back, he/she will likely get lost. The simple fix is to repeat the noun, so it is absolutely clear what you are talking about.

Another mistake of non-English writers is overloading the subject of a sentence, using too many modifiers for a noun, or other nouns as modifiers of the main noun. It can be difficult for the reader to figure out what the noun of the sentence is, and which words are modifiers. The simple fix is to use prepositional phrases and dependent clauses to expand on a noun, rather than going beyond a few adjectives. For example, ‘the budding yeast cell cycle spindle assembly checkpoint’ should be ‘the spindle assembly checkpoint of the budding-yeast cell cycle’. Another good example of an ‘overloaded noun’ of a sentence is: ‘Initiation and progression of the cell cycle are considered to occur in response to the timely ordered transcriptional, post-transcriptional, and posttranslational regulation of the cell cycle (cyclin/cyclin dependent kinase [CDK]) machinery components ’. The italicised phrase is the object of the passive verb construction ‘are considered to occur in response to’. The object is ‘components’ and the preceding words all modify ‘components’. It would be clearer to write: ‘Progression through the cell cycle is thought to be based on the temporally ordered activation of cyclin/cyclin dependent kinases (CDKs), which are regulated by a complex molecular network of transcriptional, post-transcriptional, and post-translational controls’.

Concise. After you are sure your text is correct and clear, then go through it carefully to get rid of annoying repetitions that may have crept in. Pare things down to a minimum without destroying clarity. State your main points several times (in the Abstract, Results and Discussion); as for everything else, just say it once.

Compelling. Finally, polish up the writing. Use MS Word’s thesaurus to find exactly the right word to get your idea across. Make the paper easy/pleasant/attractive to read, so people will recommend it to others.

7 Reviewing Papers

Reviewing your first paper can feel like a daunting task, with a weight of responsibility to make an accurate and fair assessment. The following tips should prove useful both to first time reviewers, and to those with some experience under their belts.

Only accept reviews for manuscripts you are competent to assess.

Make sure you are familiar with other research in the field, so you know how novel the work is.

Do not take on another review if you already have one.

Negotiate with the editor a timeline that suits you and not just them.

Do not allow deadlines to make you do a superficial job.

Try to be fair and write the kind of review you would like to receive.

Read the introduction and discussion first, to get a feel for what the authors want you to get from the paper, then read the whole manuscript to see if the results match with this.

Do not question the motives but focus on the results.

Do not be sucked in by overhype.

Always ask for codes to be shared if they are not already.

8 Networking

Our scientific research is not conducted in isolation, but rather as part of a community. As such, developing relationships with fellow scientists and mathematicians is an important part of any mathematical biologist’s career. Indeed, the contacts we make now could be our future collaborators, reviewers or employers. We often use the word ‘networking’ to denote the practice of making and developing these relationships, particularly in the context of conferences. While most would agree that networking is important, many of us are unsure of how best to go about it. This problem is especially acute for early and mid career researchers, who may wish to speak with senior researchers, but are unsure of how to introduce themselves, or manage the conversation. Here is some advice on how to approach it.

Study the conference program before the meeting. Identify 4–6 people with whom you might be interested in meeting. These include people that are senior to you and also people that may be more junior. Email them ahead of time and schedule meetings during coffee or lunch breaks early in the conference.

Do your homework before approaching a specific scientist. If you have some knowledge of their research, then a simple introduction can be had through a compliment or question about a specific piece of work. All scientists love to discuss their research, so if you have a question or insight to share they almost always want to hear it.

Find an appropriate time to approach someone and be polite. A good time to introduce yourself might be at a reception or poster session; another meeting can always happen after the initial introduction.

Go to poster sessions, or better yet, present a poster. Poster sessions are a great networking opportunity.

Go to after program events (e.g. dinner, drinks and hikes). The best networking happens off campus.

Ask a mentor, or another scientist who knows the researcher you would like to meet to introduce you and help break the ice.

9 Writing Fellowship or Grant Proposals

Writing good fellowship and grant proposals is something of an art form in itself. As an early/mid career researcher writing your first proposal, it is easy to feel bewildered, not knowing quite where to start. It is hoped that the following guidance will set you in the right direction.

Know your audience. Grant proposals are diverse and depend on the specific call in regard to what is required, what the review procedures will be and who will be the reviewers; therefore, always read the specific call/request for proposals carefully, so that you know what is expected and what the deadlines are.

If appropriate, discuss your proposal with the specific program officer / agency’s program manager, if there is one, to be certain that what you are proposing fits the guidelines for support. They can often give good advice on what will be received well versus what will not be. Ask if the proposal will be reviewed by more than one group.

Follow faithfully any guidelines that are given by the funding body, e.g. if you are asked to write the proposal in 12pt Arial font.

Ask a successful grant writer to share some of their previous grants—the structure and level of detail as well as visual support for a proposal varies greatly and needs to be tailored to the specific call.

Collaborate with someone who has been successful in obtaining support in the past from the agency.

Try to plan ahead so that you have time to share a draft of your proposal with your peers or mentor for feedback.

Make sure you have an exciting and innovative idea in the first place! Remember that the person(s) reviewing your fellowship application / grant proposal will probably have many others as well, so it is important to ‘grab the reviewer’s attention’ from the outset. Aim to write a factual but stimulating first paragraph which will make the reviewer want to read on and find out about the exciting project you are proposing.

Ensure also that your idea is appropriate, carefully stating the goals of the proposed work somewhere near the start of the proposal.

Write passionately from the heart and be ‘achievably ambitious’.

Justify any claims you make and give as good an argument as you can that what you are about to do can be achieved.

Do not try to cram every possible thing you can think of into the proposal; rather, be focused and have a good timeline with appropriate milestones.

Most grants are scored badly because the reviewer could not understand what you really wanted to do. Far fewer fail because of a flawed idea, so make a big effort in articulating your ideas as clearly as possible; visual support can really help e.g. cartoons, schematics and graphs.

Emphasize why you are the appropriate person to do the work.

Almost nobody is successful with the first iteration of a grant, so it is good to submit to a call on the first round and then resubmit on subsequent rounds, integrating reviewer feedback.

For more on this topic, see ‘Notes on Writing and Getting Grants’ by Lou Gross: lgross.utk.edu/grantwriting.txt .

10 Applying for Faculty Positions

Many early/mid career researchers may be relatively inexperienced in writing job applications, or be unsure of how best to present themselves to potential employers. The following advice is given with faculty applications in mind, though many of the tips are also relevant to applications for postdoctoral positions.

Do not apply for a job you do not want—you might get it.

Publish your work when it is ripe, even if it is not perfect.

Collaborate, but be sure to establish your own identity.

Think about who you are: a fox or a hedgehog? This reference comes from a 1953 book by the philosopher Isaiah Berlin, in which he quotes the Ancient Greek poet Archilochus as saying that ‘ the fox knows many things, but the hedgehog knows one big thing ’ (Berlin 1953 ). In the context of mathematical biology, think about whether you see your research as centring around one topic, or as touching on many topics, perhaps with a more abstract common theme. Both are valid ways to work, but it is good to think about who you are, to avoid getting pushed or pulled in directions that might not fit.

Make sure your CV is up-to-date and is written well.

Do not try to exaggerate anything, e.g. do not list lots of unpublished papers.

Cover letter

Read the job advertisement carefully and write a relevant, engaging cover letter, outlining your background, your current research interests, your future research plans and your teaching philosophy / teaching experience.

Be explicit as to why you are appropriate for the position. Spend some time finding out about the department and the university in general, and aim to include in your cover letter how you feel you could fit in and connect with the teaching and research that is going on in the department and also potential collaborations elsewhere in the university (e.g. departments of biology / life sciences and medicine).

Be enthusiastic.

Statement of research interests

Summarize in one paragraph the main results from your prior work.

Lay out a research plan, possibly with several different components. Think of this as a research plan for the initial 5–10 years of your career. Where do you want to be, what ‘big’ questions do you want to work on and how do the smaller ones fit into this?

Statement of teaching interests

Summarize what your teaching experience has been.

Give a bit of your teaching philosophy and provide examples of how you have applied it (e.g. projects you developed/used in a course you taught, or implementation of computer-based examples).

State your teaching objectives over the next 5 years—what courses and seminars you might like to teach/develop, what texts you might be interested in developing. Tie this in to particular courses the university provides.

Make sure you have good referees who will provide strongly supportive but not hyperbolic references.

Make it as easy as possible for your referees to write a letter for you—give them all the material you are sending out, explaining how to address letters and providing the links to the adverts for positions you are applying to.

Make sure the referees know which jobs are the ones of most interest to you.

Perhaps ask your referees to contact (email or phone) anyone they know at your top choice positions to alert them to your application.

For more on this topic, see ‘Applying for a job, haggling for a job, and keeping a job’ by Lou Gross: lgross.utk.edu/gettingjobs.postdocs.mbi06.txt .

11 Preparing and Giving Lectures

The average early/mid career researcher will have attended hundreds of lectures during their undergraduate studies; some of them better than others. While many PhD students will get experience of leading or assisting with tutorials and problems classes, opportunities for lecturing experience arise less frequently. The following guidance should be of help to postdoctoral researchers and new faculty preparing to give their first lectures.

Find the lecturing style that you are most comfortable with e.g. ‘chalk and talk’, slides, iPad/Tablet etc., and practise at it.

Do not practise too much—talks can sound really canned with too much practice. Put another way, too much practice can stand in the way of ‘presence’ during a talk, thinking a little on your feet and taking a few chances.

Prepare your notes in advance and try to connect with external material e.g. books, research articles, online videos etc.

Think about your main point during your pre-lecture preparation.

Your lecture has to fit your audience. Do not attempt to give the same lecture to biologists and to mathematicians.

Optimise your slides: a maximum of 20 words per slide, brief bullet points, self-contained and easy to follow.

Do not include something on a slide if you do not want to talk about it.

Go to the lecture theatre before you start the course and work out where everything is so that you can begin the first lecture without any glitches or delays.

Try to be enthusiastic and passionate in your delivery and to ENJOY giving the lecture.

Never forget that it is about the material, not about you.

Consider introducing your talk with interesting scientific questions, and returning to those at the end to show that you ‘solved them’. Merely reproducing a behaviour with a model is not very interesting unless you can show new insights or novel predictions.

Aim to engage the students rather than just lecture for one hour e.g. stop regularly and ask questions, ask the students to suggest ways to complete a piece of algebra or offer the answer to a problem.

Provide plenty of motivation and background for the audience to understand the main ideas. Be sure to emphasize the significance and goals.

Give plenty of worked examples in the class which underpin any piece of theory you deliver.

Be sure to EXPLAIN everything. Your audience will appreciate that.

Make the lecture interesting. Use some colour, make fonts nice and large, consider some humour if possible, once you gain confidence.

Make a deliberate mistake now and again—this can encourage the students to engage and when they get the correct answer it gives them confidence. It also shows them that you are not infallible!

Never go over time.

12 What Do You Wish You Had Known When You were an Early-mid Career Researcher?

In addition to asking our seasoned professionals for guidance on specific questions, their advice to early and mid career researchers was also sought at a more general level, as recounted in this section and that which follows. First, in this section, we explore the hard-earned knowledge that our experts wish they had possessed when they were early/mid career researchers.

Seeking advice

Do not be shy about getting advice, particularly on grant proposals.

Understand how the system at your institution works, who to go to for advice/assistance and how to work around arcane rules that constrain your ability to advance your research and teaching.

Career planning

Think a few years ahead but do not let long-term planning stand in the way.

Early in your career, it is common not to know what you really want and that is OK, since you have not experienced enough yet.

‘When I started as a graduate student, I had a very specific plans about what I wanted to study: quantum chemistry. Like most mathematical biologists, I never intended to be one! I stumbled onto the field through my professors and mentors. So keep your eyes open, see what catches your interest, see where new research areas are opening up and where you can make a contribution. Be flexible, find your place in the world and have fun!’

Think strategically about what you will gain from a specific position and how it might lead you to new opportunities in the future.

‘Failure’ and rejections

Be ready to accept rejections and how to move on effectively from these, such as re-applying for grants to either the same agency which initially rejected it or to try someplace else.

Do not take failure personally; academia is a constant source of failure, whether it is papers, grants or even your science. Failure is the only way we can learn; of course it still stings, but know that this is a universal pain we all feel as scientists, and it is also temporary, as it will drive resubmissions, rewriting, reframing and ultimately success.

Lack of a job offer, or interview, may just be due to various political factors in a given department/unit that have nothing to do with your excellence. Therefore, do not let such ‘rejections’ affect your morale and work.

Do not skip your postdoc; exploit every second of it. It is a rare time in your scientific career that you will never have again—both scientific freedom and no financial concerns.

Have fun! Most research ideas come outside the laboratory; on a walk, while exercising, or while having dinner with friends. A lot of great ideas start out on a napkin.

If you are not excited about a problem, the work is not going to be worthwhile.

You can work on anything you want to, independent of your field, as long as you are willing to learn the new area.

Keep doing good work, even when the job-market looks bleak. Eventually this will pay off.

Take the time to learn new skills.

Do the hard work yourself.

‘I wish I had known’:

LaTeX —‘I wrote my PhD thesis using troff’ ( wikipedia.org/wiki/Troff ).

More numerical analysis.

The Sobolev Embedding Theorem (just kidding!).

Sharing your work

Put real effort into making your science as accessible as possible—the more people who understand it, the better it will be cited and shared.

Grab any opportunity you can to present your work, even if you find it difficult. It will help you understand your own work better and expose the community to what you are doing and critically provide valuable feedback.

Open science is a golden opportunity to share your work before it is published, embrace it. Share your papers on preprint servers (e.g. bioRxiv and arXiv), and your code and data on public repositories (e.g. GitHub).

Collaboration and networking

Work with people you like, in labs that are happy and have a good community ethic. Do not try to work with people simply because of their prestige.

Use administrative roles to build collaborations.

Networking with others (in your unit and at conferences) is very important. Consider sending your e-publications to the top researchers with a short email. Many are busy, so may not answer, but some will.

Do not be shy at a conference. Schedule meetings ahead of time to make sure you are not alone during coffee breaks.

Maintain contact with those you have met who might help your career advance in the future.

Organising your time

‘I did not realize how much time I would spend in service-related activities. I sit on many committees. My service takes about one full day per week.’

‘I did not realize how much grant writing I would be doing. I had to learn how to write grants for many different reviewing bodies. This can take time, but can also be helpful in that you then understand how to talk about your research with many different audiences.’

Teaching and mentorship activities can occupy much of your time. Make sure that you structure your week so that you have research blocks that are long enough for you to remember what you are doing, and get some work done to review and advance your projects.

Get home in time for dinner with your family.

13 Some Final Words of Advice

In this last section, we offer some final words of advice, not covered by the previous sections.

Community, collaboration and care

Collaborate broadly and build your network of collaborators in ways that stretch your research to fields that might be far from your formal education.

Team science is truly a gift for mathematical biology. It is being embraced across many different disciplines and is a golden opportunity to work across fields with creative teams, where the team is far more powerful scientifically than any of the individuals. If you can work with a team, jump at the chance.

Develop a community around you, but do not feel that you need to collaborate with everyone. Deliberately keep some experts in your field at ‘arm’s-length’ as you will need people to review your file at tenure and promotion, for grants, and your manuscripts for publication.

Care about your community—take time to contribute, to nurture and enrich your community as it will not continue without it.

Make time for self-care; something outside of science even if it is with scientists. It is important to recharge your creative and non-creative batteries and that cannot happen if you use them all the time.

Most scientists are good people even if they may ask difficult questions and appear intimidating—they are a scientist just like you and care about similar things.

Always be honest, even if it means admitting mistakes, being truthful will always pay dividends in the end.

Work on what you want, not on what other people think you should.

Enjoy yourself, have fun, work on problems that you are really interested in and passionate about.

Aim high. Always ask ‘could my work be better?’ Do not settle for the first result and hurry to publish—do your due diligence and make that sure every piece of work has the highest impact it can.

Mathematical biology is a subfield of biology. Talk to biologists as often as you can. Let their questions guide your research.

Do not be afraid of data. Indeed, look at the data! You may find something that you did not expect that is more interesting than what you did expect.

Understand what it means to calibrate and validate a mathematical model. Not every curve that fits data makes a model plausible and it does not guarantee predictive power (if that is what you are aiming for).

Do not be a one-trick pony. It will help your career if you become the go-to person in the world on a particular topic, but do not constrain yourself to this area. Look for side-projects that may be well outside this area of focus.

Be willing to take risks and try out new/alternative things. It is only by failing that we discover what does not work and this helps put us on another track that perhaps will work. Do not be afraid to ‘fail’. The following quote from John Backus (who invented FORTRAN) illustrates this point:

‘ I, myself, have had many failures and I’ve learned that if you are not failing a lot, you are probably not being as creative as you could be—you aren’t stretching your imagination. You need the willingness to fail all the time. You have to generate many ideas and then you have to work very hard only to discover that they don’t work. And you keep doing that over and over until you find one that does work. ’ — mathshistory.st-andrews.ac.uk/Biographies/Backus/quotations/

Communication

Learning to communicate in writing and orally is just as important as doing advanced research. Your funding and the respect you achieve will depend on your ability to explain your work and convince others that it is significant.

Work to build your vocabulary to be able to communicate with experts in fields quite different from your own.

Get some formal training from science communication experts to assist you in being able to discuss your work with non-scientists and journalists. Do not be bashful about tooting your own horn.

For more on careers in academia, see ‘Careers in Academia: How to Enhance your Chances for Success’ by Lou Gross: lgross.utk.edu/eeb504Spring2021.html .

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Acknowledgements

This paper would not have been possible were it not for the sagacious advice, generously offered by the following current and former SMB Presidents: Prof. Frederick R. Adler (University of Utah); Prof. Alexander R. A. Anderson (H. Lee Moffitt Cancer Center & Research Institute); Prof. Mark A. J. Chaplain (University of St Andrews); Prof. Leah Edelstein-Keshet (University of British Columbia); Prof. Heiko Enderling (The University of Texas MD Anderson Cancer Center); Prof. Leon Glass (McGill University); Prof. Louis J. Gross (University of Tennessee); Prof. Jane M. Heffernan (York University); Prof. Simon A. Levin (Princeton University); and Prof. John J. Tyson (Virginia Polytechnic Institute and State University). PAR acknowledges financial support from the University of Birmingham Dynamic Investment Fund.

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Roberts, P.A. Advice to a Young Mathematical Biologist. Bull Math Biol 86 , 52 (2024). https://doi.org/10.1007/s11538-024-01269-1

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