Gene Perturbation Experiments Research Articles

Single cell analysis reveals the roles and regulatory mechanisms of type-I interferons in Parkinson’s disease

The pathogenesis of Parkinson’s disease (PD) is strongly associated with neuroinflammation, and type I interferons (IFN-I) play a crucial role in regulating immune and inflammatory responses. However, the specific features of IFN in different cell types and the underlying mechanisms of PD have yet to be fully described. In this study, we analyzed the GSE157783 dataset, which includes 39,024 single-cell RNA sequencing results for five PD patients and six healthy controls from the Gene Expression Omnibus database. After cell type annotation, we intersected differentially expressed genes in each cell subcluster with genes collected in The Interferome database to generate an IFN-I-stimulated gene set (ISGs). Based on this gene set, we used the R package AUCell to score each cell, representing the IFN-I activity. Additionally, we performed monocle trajectory analysis, and single-cell regulatory network inference and clustering (SCENIC) to uncover the underlying mechanisms. In silico gene perturbation and subsequent experiments confirm NFATc2 regulation of type I interferon response and neuroinflammation. Our analysis revealed that microglia, endothelial cells, and pericytes exhibited the highest activity of IFN-I. Furthermore, single-cell trajectory detection demonstrated that microglia in the midbrain of PD patients were in a pro-inflammatory activation state, which was validated in the 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model as well. We identified transcription factors NFATc2, which was significantly up-regulated and involved in the expression of ISGs and activation of microglia in PD. In the 1-Methyl-4-phenylpyridinium (MPP+)-induced BV2 cell model, the suppression of NFATc2 resulted in a reduction in IFN-β levels, impeding the phosphorylation of STAT1, and attenuating the activation of the NF-κB pathway. Furthermore, the downregulation of NFATc2 mitigated the detrimental effects on SH-SY5Y cells co-cultured in conditioned medium. Our study highlights the critical role of microglia in type I interferon responses in PD. Additionally, we identified transcription factors NFATc2 as key regulators of aberrant type I interferon responses and microglial pro-inflammatory activation in PD. These findings provide new insights into the pathogenesis of PD and may have implications for the development of novel therapeutic strategies.

A transcriptomic atlas of acute stress response to low pH in multiple Issatchenkia orientalis strains.

Issatchenkia orientalis is a promising industrial chassis to produce biofuels and bioproducts due to its high tolerance to multiple environmental stresses such as low pH, heat, and other chemicals otherwise toxic for the most widely used microbes. Yet, little is known about specific mechanisms of such tolerance in this organism, hindering our ability to engineer this species to produce valuable biochemicals. Here, we report a comprehensive study of the mechanisms of acidic tolerance in this species via transcriptome profiling across variable pH for 12 different strains with different phenotypes. We found multiple regulatory mechanisms involved in tolerance to low pH in different strains of I. orientalis, marking potential targets for future gene editing and perturbation experiments.

Sex specific molecular networks and key drivers of Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD.MethodsWe integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models.ResultsGene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34.ConclusionsThese findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

Planarian Ovary Dissection for Ultrastructural Analysis and Antibody Staining.

Accessibility to germ cells allows the study of germ cell development, meiosis, and recombination. The sexual biotype of the freshwater planarian, Schmidtea mediterranea, is a powerful invertebrate model to study the epigenetic specification of germ cells. Unlike the large number of testis and male germ cells, planarian oocytes are relatively difficult to locate and examine, as there are only two ovaries, each with 5-20 oocytes. Deeper localization within the planarian body and lack of protective epithelial tissues also make it challenging to dissect planarian ovaries directly. This protocol uses a brief fixation step to facilitate the localization and dissection of planarian ovaries for downstream analysis to overcome these difficulties. The dissected ovary is compatible for ultrastructural examination by transmission electron microscopy (TEM) and antibody immunostaining. The dissection technique outlined in this protocol also allows for gene perturbation experiments, in which the ovaries are examined under different RNA interference (RNAi) conditions. Direct access to the intact germ cells in the ovary achieved by this protocol will greatly improve the imaging depth and quality and allow cellular and subcellular interrogation of oocyte biology.

Classification of Cancer Types Using Graph Convolutional Neural Networks.

Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently. In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific. Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.

Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control

The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small nonzero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance . To estimate , we focus on a simple estimation approach, the iterated empirical Bayes estimation. We conduct a detailed analysis of the properties of the iterated EB estimate and provide theoretical guarantee of its good performance under mild conditions. We provide simulations comparing the new modeling approach with existing methods, and the new approach shows more stable and better performance under different situations. We also apply it to a real dataset from gene knock-down experiments and obtained better results compared with the original two-group model testing for nonzero effects.

A common molecular logic determines embryonic stem cell self-renewal and reprogramming.

During differentiation and reprogramming, new cell identities are generated by reconfiguration of gene regulatory networks. Here, we combined automated formal reasoning with experimentation to expose the logic of network activation during induction of naïve pluripotency. We find that a Boolean network architecture defined for maintenance of naïve state embryonic stem cells (ESC) also explains transcription factor behaviour and potency during resetting from primed pluripotency. Computationally identified gene activation trajectories were experimentally substantiated at single‐cell resolution by RT–qPCR. Contingency of factor availability explains the counterintuitive observation that Klf2, which is dispensable for ESC maintenance, is required during resetting. We tested 124 predictions formulated by the dynamic network, finding a predictive accuracy of 77.4%. Finally, we show that this network explains and predicts experimental observations of somatic cell reprogramming. We conclude that a common deterministic program of gene regulation is sufficient to govern maintenance and induction of naïve pluripotency. The tools exemplified here could be broadly applied to delineate dynamic networks underlying cell fate transitions.

Capturing functional long non-coding RNAs through integrating large-scale causal relations from gene perturbation experiments

Characterizing functions of long noncoding RNAs (lncRNAs) remains a major challenge, mostly due to the lack of lncRNA-involved regulatory relationships. A wide array of genome-wide expression profiles generated by gene perturbation have been widely used to capture causal links between perturbed genes and response genes. Through annotating >600 gene perturbation profiles, over 354,000 causal relationships between perturbed genes and lncRNAs were identified. This large-scale resource of causal relations inspired us to develop a novel computational approach LnCAR for inferring lncRNAs' functions, which showed a higher accuracy than the co-expression based approach. By application of LnCAR to the cancer hallmark processes, we identified 38 lncRNAs involved in distinct carcinogenic processes. The “activating invasion & metastasis” related lncRNAs were strongly associated with metastatic progression in various cancer types and could act as a predictor of cancer metastasis. Meanwhile, the “evading immune destruction” related lncRNAs showed significant associations with immune infiltration of various immune cells and, importantly, can predict response to anti-PD-1 immunotherapy, suggesting their potential roles as biomarkers for immune therapy. Taken together, our approach provides a novel way to systematically reveal functions of lncRNAs, which will be helpful for further experimental exploration and clinical translational research of lncRNAs.

SRSF9 selectively represses ADAR2-mediated editing of brain-specific sites in primates.

Adenosine-to-inosine (A-to-I) RNA editing displays diverse spatial patterns across different tissues. However, the human genome encodes only two catalytically active editing enzymes (ADAR1 and ADAR2), suggesting that other regulatory factors help shape the editing landscape. Here, we show that the splicing factor SRSF9 selectively controls the editing of many brain-specific sites in primates. SRSF9 is more lowly expressed in the brain than in non-brain tissues. Gene perturbation experiments and minigene analysis of candidate sites demonstrated that SRSF9 could robustly repress A-to-I editing by ADAR2. We found that SRSF9 biochemically interacted with ADAR2 in the nucleus via its RRM2 domain. This interaction required the presence of the RNA substrate and disrupted the formation of ADAR2 dimers. Transcriptome-wide location analysis and RNA sequencing revealed 1328 editing sites that are controlled directly by SRSF9. This regulon is significantly enriched for brain-specific sites. We further uncovered a novel motif in the ADAR2-dependent SRSF9 binding sites and provided evidence that the splicing factor prevents loss of cell viability by inhibiting ADAR2-mediated editing of genes involved in proteostasis, energy metabolism, the cell cycle and DNA repair. Collectively, our results highlight the importance of SRSF9 as an editing regulator and suggest potential roles for other splicing factors.

Capturing Functional Long Non-Coding RNAs Through Integrating Large-Scale Causal Relations from Gene Perturbation Experiments

Characterizing functions of long noncoding RNAs (lncRNAs) remains a major challenge, mostly due to the lack of lncRNA-involved regulatory relationships. A wide array of genome-wide expression profiles generated by gene perturbation have been widely used to capture causal links between perturbed genes and response genes. Through annotating more than 600 gene perturbation profiles, over 354,000 causal relationships between perturbed genes and lncRNAs were identified. This large-scale resource of causal relations inspired us to develop a novel computational approach LnCAR for inferring lncRNAs' functions, which showed a higher accuracy than the co-expression based approach. By application of LnCAR to the cancer hallmark processes, we identified 38 lncRNAs involved in distinct carcinogenic processes. The activating invasion & metastasis related lncRNAs were strongly associated with metastatic progression in various cancer types and could act as a predictor of cancer metastasis. Meanwhile, the evading immune destruction related lncRNAs showed significant associations with immune infiltration of various immune cells and, importantly, can predict response to anti-PD-1 immunotherapy, suggesting their potential roles as biomarkers for immune therapy. Taken together, our approach provides a novel way to systematically reveal functions of lncRNAs, which will be helpful for further experimental exploration and clinical translational research of lncRNAs. Funding: This work was supported in part by the National High Technology Research and Development Program of China [863 Program, Grant Nos. 2014AA021102], the National Program on Key Basic Research Project [973 Program, Grant Nos. 2014CB910504], the National Natural Science Foundation of China [Grant Nos. 91439117, 61473106, 61573122], the China Postdoctoral Science Foundation (2016M600260), Wu lien-teh youth science fund project of Harbin medical university [Grant Nos. WLD-QN1407], Special funds for the construction of higher education in Heilongjiang Province [Grant Nos. UNPYSCT-2016049], and the Heilongjiang Postdoctoral Foundation (LBH-Z16098). Declaration of Interest: We declare that we have no competing financial interests regarding this work. There are no relationships with industry.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

High‐content imaging using automated microscopy and computer vision allows multivariate profiling of single‐cell phenotypes. Here, we present methods for the application of the CISPR‐Cas9 system in large‐scale, image‐based, gene perturbation experiments. We show that CRISPR‐Cas9‐mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image‐based phenotyping. We developed a pipeline to construct a large‐scale arrayed library of 2,281 sequence‐verified CRISPR‐Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine‐learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in‐depth characterization of gene perturbation effects. This approach enables genome‐scale image‐based multivariate gene perturbation profiling using CRISPR‐Cas9.

Correction Notice: Strategies for Performing Dynamic Gene Perturbation Experiments in Flowers

Correction Notice: Strategies for Performing Dynamic Gene Perturbation Experiments in Flowers

Causal Inference by using Invariant Prediction: Identification and Confidence Intervals

SummaryWhat is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (e.g. various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence intervals for the causal relationships in quite general scenarios. We examine the example of structural equation models in more detail and provide sufficient assumptions under which the set of causal predictors becomes identifiable. We further investigate robustness properties of our approach under model misspecification and discuss possible extensions. The empirical properties are studied for various data sets, including large-scale gene perturbation experiments.

Methods for causal inference from gene perturbation experiments and validation

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae. The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.

Gene network analysis of Arabidopsis thaliana flower development through dynamic gene perturbations.

Understanding how flowers develop from undifferentiated stem cells has occupied developmental biologists for decades. Key to unraveling this process is a detailed knowledge of the global regulatory hierarchies that control developmental transitions, cell differentiation and organ growth. These hierarchies may be deduced from gene perturbation experiments, which determine the effects on gene expression after specific disruption of a regulatory gene. Here, we tested experimental strategies for gene perturbation experiments during Arabidopsis thaliana flower development. We used artificial miRNAs (amiRNAs) to disrupt the functions of key floral regulators, and expressed them under the control of various inducible promoter systems that are widely used in the plant research community. To be able to perform genome-wide experiments with stage-specific resolution using the various inducible promoter systems for gene perturbation experiments, we also generated a series of floral induction systems that allow collection of hundreds of synchronized floral buds from a single plant. Based on our results, we propose strategies for performing dynamic gene perturbation experiments in flowers, and outline how they may be combined with versions of the floral induction system to dissect the gene regulatory network underlying flower development.

Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes.

Genome-wide transcriptome profiling after gene perturbation is a powerful means of elucidating gene functional mechanisms in diverse contexts. The comprehensive collection and analysis of the resulting transcriptome profiles would help to systematically characterize context-dependent gene functional mechanisms and conduct experiments in biomedical research. To this end, we collected and curated over 3000 transcriptome profiles in human and mouse from diverse gene perturbation experiments, which involved 1585 different perturbed genes (microRNAs, lncRNAs and protein-coding genes) across 1170 different cell lines/tissues. For each profile, we identified differential genes and their associated functions and pathways, constructed perturbation networks, predicted transcription regulation and cancer/drug associations, and assessed cooperative perturbed genes. Based on these transcriptome analyses, the Gene Perturbation Atlas (GPA) can be used to detect (i) novel or cell-specific functions and pathways affected by perturbed genes, (ii) protein interactions and regulatory cascades affected by perturbed genes, and (iii) perturbed gene-mediated cooperative effects. The GPA is a user-friendly database to support the rapid searching and exploration of gene perturbations. Particularly, we visualized functional effects of perturbed genes from multiple perspectives. In summary, the GPA is a valuable resource for characterizing gene functions and regulatory mechanisms after single-gene perturbations. The GPA is freely accessible at http://biocc.hrbmu.edu.cn/GPA/.

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

BackgroundDecoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.ResultsWe developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes.The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10−100), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately −9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (−9435 to −8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.ConclusionWe report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0460-0) contains supplementary material, which is available to authorized users.

Control of Reproductive Floral Organ Identity Specification inArabidopsisby the C Function Regulator AGAMOUS

The floral organ identity factor AGAMOUS (AG) is a key regulator of Arabidopsis thaliana flower development, where it is involved in the formation of the reproductive floral organs as well as in the control of meristem determinacy. To obtain insights into how AG specifies organ fate, we determined the genes and processes acting downstream of this C function regulator during early flower development and distinguished between direct and indirect effects. To this end, we combined genome-wide localization studies, gene perturbation experiments, and computational analyses. Our results demonstrate that AG controls flower development to a large extent by controlling the expression of other genes with regulatory functions, which are involved in mediating a plethora of different developmental processes. One aspect of this function is the suppression of the leaf development program in emerging floral primordia. Using trichome initiation as an example, we demonstrate that AG inhibits an important aspect of leaf development through the direct control of key regulatory genes. A comparison of the gene expression programs controlled by AG and the B function regulators APETALA3 and PISTILLATA, respectively, showed that while they control many developmental processes in conjunction, they also have marked antagonistic, as well as independent activities.

Barcoded DNA-Tag Reporters for Multiplex Cis-Regulatory Analysis

Cis-regulatory DNA sequences causally mediate patterns of gene expression, but efficient experimental analysis of these control systems has remained challenging. Here we develop a new version of “barcoded" DNA-tag reporters, “Nanotags" that permit simultaneous quantitative analysis of up to 130 distinct cis-regulatory modules (CRMs). The activities of these reporters are measured in single experiments by the NanoString RNA counting method and other quantitative procedures. We demonstrate the efficiency of the Nanotag method by simultaneously measuring hourly temporal activities of 126 CRMs from 46 genes in the developing sea urchin embryo, otherwise a virtually impossible task. Nanotags are also used in gene perturbation experiments to reveal cis-regulatory responses of many CRMs at once. Nanotag methodology can be applied to many research areas, ranging from gene regulatory networks to functional and evolutionary genomics.

A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

BackgroundComplex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments.ResultsTo facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9.ConclusionsThis work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.