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bioRxiv2025

Modeling the Interplay of Sex Hormones in Cardiac Hypertrophic Signaling

Biological sex plays a crucial role in the outcomes of cardiac health and therapies. Sex hormones are known to strongly influence cardiac remodeling through intracellular signaling pathways, yet their underlying mechanisms remain unclear. To address this need, we developed and validated a logic-based systems biology model of cardiomyocyte hypertrophy that, for the first time, incorporates the effects of both estradiol (E2) and testosterone (T) alongside well-established hypertrophic stimuli (Strain, angiotensin II (AngII), and endothelin-1 (ET-1)). We qualitatively validated the model to literature data with 84% agreement. Quantitative validation was done by simulating the impact of the inputs (E2, T, Strain, AngII, and ET-1) on cardiac hypertrophy, captured as change in CellArea. We perturbed the validated model to examine the differential response to hypertrophy and identify changes in influential and sensitive downstream nodes for a male, pre-menopausal female, and post-menopausal female condition. Our results suggest that T has a greater impact on hypertrophy than E2. This model increases our understanding of the mechanisms through which sex hormones influence cardiac hypertrophy and can aid with developing more effective cardiac therapies for all patients. Author summaryDifferences between female and male hearts extend far beyond size and structure. Sex hormones estradiol and testosterone play key roles in sex-specific cardiac remodeling via intracellular pathways. Understanding how these sex hormones impact cardiac remodeling is critical for developing more effective, sex-specific approaches to cardiovascular care. Logic-based systems biology models have proven useful in quantifying and analyzing complex and intricate intracellular signaling network dynamics in various cell types. We leverage this method to develop a model of cardiomyocyte hypertrophy, which, for the first time, includes the effect of both estradiol and testosterone. Considering the combined influence of these hormones is important because both women and men have varying concentrations of these hormones throughout their lives. The model was developed and validated based on previously published studies. We then investigated differences in cardiomyocyte hypertrophy in pre- and post-menopausal women and men.

systems biology↗
bioRxiv2016

A hierarchical Bayesian framework for understanding the spatiotemporal dynamics of the intestinal epithelium

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cellular migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we develop a probabilistic, hierarchical framework. This framework provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine drawing reliable conclusions - uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. This allows the incorporation of features of both continuum tissue models and discrete cellular models. We perform model checks on both in-sample and out-of-sample datasets and use these checks to illustrate how to test possible model improvements and assess the robustness of our conclusions. This allows us to consider - and ultimately decide against - the need to retain finite-cell-size effects to explain a small misfit appearing in one set of long-time, out-of-sample predictions. Our approach leads us to conclude, for the present set of experiments, that a primarily proliferation-driven model is adequate for predictions over most time-scales. We describe each stage of our framework in detail, and hope that the present work may also serve as a guide for other applications of the hierarchical approach to problems in computational and systems biology more generally.\n\nAuthor SummaryThe intestinal epithelium serves as an important model system for studying the dynamics and regulation of multicellular populations. It is characterised by rapid rates of self-renewal and repair; failure of the regulation of these processes is thought to explain, in part, why many tumours occur in the intestinal and similar epithelial tissues. These features have led to a large amount of work on estimating rate parameters in the intestine. There still remain, however, large gaps between the raw data collected, the experimental interpretation of these data, and speculative mechanistic models for underlying processes. In our view hierarchical statistical modelling provides an ideal, but currently underutilised, method to begin to bridge these gaps. This approach makes essential use of the distinction between measurement, process and parameter models, giving an explicit framework for combining experimental data and mechanistic modelling in the presence of multiple sources of uncertainty. As we illustrate, the hierarchical approach also provides a suitable framework for addressing other methodological questions of broader interest in systems biology: how to systematically relate discrete and continuous mechanistic models; how to formally interpret and visualise statistical evidence; and how to express causal assumptions in terms of conditional independence.

Systems Biology↗
bioRxiv2025

EMMAi: fast enzyme-allocation constraints in GEMs for improved biomass prediction across carbon sources

Genome-scale metabolic models (GEMs) predict emergent phenotypes by modeling the metabolic networks encoded in genomes. While GEMs have significantly advanced systems biology, metabolic engineering, biomedicine, and environmental science, they require extensive time and resources for manual curation, which can limit their utility in rapidly evolving research landscapes. Recent findings suggest that manually curated reactions can sometimes reduce prediction accuracy, indicating that integrating additional biologically grounded constraints may better capture emergent phenotypes. One promising approach is the incorporation of enzyme allocation constraints, which has been shown to enhance the predictive accuracy in metabolic models. Enzymatically constrained GEMs (ecGEMs) rely on enzyme turnover rates (kcat) and protein molecular weights (MWs) to account for intracellular resource limitations by introducing an enzyme pool variable and assigning costs to reactions, thereby simulating enzymatic resource constraints. Tools such as GECKO, AutoPACMEN, and ECMpy provide computational pipelines for ecGEM generation. However, these pipelines are often limited by their reliance on experimentally measured kcat values or deep learning-predicted values, such as those generated by DLKcat, which face challenges in predicting kinetics for enzymes dissimilar to their training data. Additionally, these methods frequently require extensive manual curation of kcat values based on empirical data, a time-intensive process that hampers scalability and applicability to non-model organisms. To address these limitations, we introduce EMMAi (Enzyme-constrained Metabolic Models with AI), a pipeline that fully automates the incorporation of enzyme constraints into GEMs. Unlike existing pipelines, EMMAi exclusively utilizes kcat values predicted by UniKP, an AI framework with improved accuracy over DLKcat, particularly for enzymes not present in training datasets. UniKP achieves a 13% improvement in correlation for unseen enzymes, enabling EMMAi to deliver ecGEMs with enhanced prediction accuracy without manual curation requirements. We evaluated EMMAi by applying it to three GEMs: two manually curated models, iJO1366 (Escherichia coli str. K-12 substr. MG1655) and iMO1056 (Pseudomonas aeruginosa PAO1), and one draft GEM constructed and gap-filled using CarveMe. EMMAi-generated ecGEMs showed an average Pearson Correlation Coefficient (PCC) improvement of 0.27 for manually curated GEMs when compared to predicted and experimentally measured growth rates and Biolog readings. Notably, for the draft GEM of Pseudomonas aeruginosa PAO1, the PCC improved dramatically from -0.3 to 0.6. EMMAi demonstrates that automating the integration of enzyme allocation constraints using AI-predicted kinetic parameters significantly enhances the prediction accuracy of GEMs, even in the absence of manual curation. These results underscore EMMAis potential as a scalable, efficient, and accurate tool for advancing GEM-based research in systems biology, metabolic engineering, and beyond.

systems biology↗
bioRxiv2024

Identifying effective evolutionary strategies for uncovering reaction kinetic parameters under the effect of measurement noises

The transition from explanative modelling of fitted data to the predictive modelling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. On the other hand, we could not identify the parameters of convenience kinetics using any algorithm. Together, we find algorithms that are effective under marked measurement noise for specific reaction kinetics, as a step towards predictive modelling for systems biology endeavors.

systems biology↗
bioRxiv2026

A Benchmarking Study of Feature Screening Approaches Across Omics Classification Settings

In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by learning which biomolecules are highly predictive of a treatment, biological outcome, or phenotype. A major challenge of applying ML to high throughput omics is overcoming noise when sample size is limited and unbalanced with respect to tens of thousands of biomolecules measured. Thus, feature selection (the process of reducing the number of predictors) is both a critical and common step in the ML analysis pipeline. While much attention has been given to embedding and wrapping techniques for feature selection in the omics space, filter-based methods for model-free feature selection have appealing theoretical properties. This manuscript evaluates sure screening, a class of filter-based feature selection methods which provide analytical guarantees for true feature set retention. Here, we cover existing feature screening methods based on the sure screening principal, available software, methods to improve feature screening, and contextualize feature screening in the larger discussion of feature selection for omics data analysis. Additionally, a suite of model-free sure screening approaches is applied and compared for several omics biomedical applications in a ML classification context. We identified BcorSIS as the most effective and computationally efficient screening method across various omics datasets, consistently outperforming others like CSIS and DCSIS in runtime. Author SummaryA common goal when analyzing high throughput omics data is to identify key biomolecules which may be important in complex biological systems. This type of large-scale analysis is critical, as it can significantly reduce the set of total biomolecules which are considered for more detailed, targeted analysis. Additionally, biomolecules which are highly predictive of specific phenotypes are useful markers to guide treatment. For instance, identifying which biomolecules are predictive of type 1 diabetes could improve early diagnosis, which may improve patient prognosis. However, modern instrumentation can often detect thousands or tens of thousands of biomolecules, and studies typically have a disproportionately limited number of samples. Many measured biomolecules, or features, are often noisy and uninformative, and overcoming this noise to identify an informative set of biomolecules can be challenging for machine learning (ML) models. Fortunately, there are many feature selection strategies which can intelligently reduce the number of "uninformative" biomolecules an ML model needs to overcome. In this work, sure screening, which is a class of feature selection methods that retains the true feature set under certain conditions, is evaluated in the context of omics data analyses. The pros and cons of various sure screening methods, including software availability, are summarized, and their use in the larger feature selection literature is contextualized. Additionally, we benchmark performance by applying a suite of sure screening approaches on several real omics datasets generated to understand the progression of type 1 diabetes.

bioinformatics↗
bioRxiv2023

Bayesian model discovery for reverse-engineering biochemical networks from data

The reverse engineering of gene regulatory networks based on gene expression data is a challenging inference task. A related problem in computational systems biology lies in identifying signalling networks that perform particular functions, such as adaptation. Indeed, for many research questions, there is an ongoing search for efficient inference algorithms that can identify the simplest model among a larger set of related models. To this end, in this paper, we introduce SLIO_SCPLOWNC_SCPLOWG, a Bayesian sparse likelihood-free inference method using Gibbs sampling. We demonstrate that SLIO_SCPLOWNC_SCPLOWG can reverse engineer stochastic gene regulatory networks from single-cell data with high accuracy, outperforming state-of-the-art correlation-based methods. Furthermore, we show that SLIO_SCPLOWNC_SCPLOWG can successfully identify signalling networks that execute adaptation. Sparse hierarchical Bayesian inference thus provides a versatile tool for model discovery in systems biology and beyond.

systems biology↗
bioRxiv2018

Generating an artificial nest building pufferfish in a cellular automaton through behavior decomposition

A species of pufferfish builds fascinating circular nests on the sea floor to attract mates. This project simulates the nest building behavior in a cellular automaton using the Morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. By considering the biological neural network of the pufferfish as a black box, decomposing only its external behavior, an artificial counterpart can be generated. In this way a complex biological system producing a behavior can be filtered into a system containing only functions that are essential to reproduce the behavior. The derived system not only has intrinsic value as an artificial entity but also might help to ascertain how the biological system produces the behavior.

synthetic biology↗
bioRxiv2022

Evaluating the Robustness of Connectivity Methods to Noise for In Silico Drug Repurposing Studies

Drug repurposing is an approach to identify new therapeutic applications for existing drugs and small molecules. It is a field of growing research interest due to its time and cost effectiveness as compared with de novo drug discovery. One method for drug repurposing is to adopt a systems biology approach to associate molecular signatures of drug and disease. Drugs which have an inverse relationship with the disease signature may be able to reverse the molecular effects of the disease and thus be candidates for repurposing. Conversely, drugs which mimic the disease signatures can inform on potential molecular mechanisms of disease. The relationship between these disease and drug signatures are quantified through connectivity scores. Identifying a suitable drug-disease scoring method is key for in silico drug repurposing, so as to obtain an accurate representation of the true drug-disease relationship. There are several methods to calculate these connectivity scores, notably the Kolmogorov-Smirnov (KS), Zhang and eXtreme Sum (XSum). However, these methods can provide discordant estimations of the drug-disease relationship and this discordance can affect the drug-disease indication. Using the gene expression profiles from the Library of Integrated Network-Based Cellular Signatures (LINCS) database, we evaluated the methods based on their drug-disease connectivity scoring performance. In this first-of-its-kind analysis, we varied the quality of disease signatures by using only highly differential genes or by the inclusion of non-differential genes. Further, we simulated noisy disease signatures by introducing varying levels of noise into the gene expression signatures. Overall, we found that there was not one method that outperformed the others in all instances, but the Zhang method performs well in a majority of our analyses. Our results provide a framework to evaluate connectivity scoring methods, and considerations for deciding which scoring method to apply in future systems biology studies for drug repurposing.

systems biology↗
bioRxiv2020

Computational verification of large logical models - application to the prediction of T cell response to checkpoint inhibitors

At the crossroad between biology and mathematical modelling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalisation and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation", enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behaviour of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.

systems biology↗
bioRxiv2022

A Data-Driven Optimization Method for Coarse-Graining Gene Regulatory Networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

systems biology↗
bioRxiv2015

Layering genetic circuits to build a single cell, bacterial half adder

Gene regulation in biological systems is impacted by the cellular and genetic context-dependent effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the limitations of engineering biology from an architectural point of view, with the aim of compiling a set of engineering solutions for overcoming failure modes during the development of complex, synthetic genetic circuits. Using a synthetic biology approach that is supported by computational modelling and rigorous characterisation, AND, OR and NOT biological logic gates were layered in both parallel and serial arrangements to generate a repertoire of Boolean operations that include NIMPLY, XOR, half adder and half subtractor logics in single cell. Subsequent evaluation of these near-digital biological systems revealed critical design pitfalls that triggered genetic context dependent effects, including 5 UTR interference and uncontrolled switch-on behaviour of {sigma}54 promoter. Importantly, this work provides a representative case study to the debugging of genetic context dependent effects through principles elucidated herein, thereby providing a rational design framework to program single prokaryotic cell with diversified digital operations.

Synthetic Biology↗
bioRxiv2026

Bias correction for integrated climate projection modeling

Projections of future conditions from Earth systems models (ESMs) are necessary to understand and predict effects of changing environmental conditions on biological systems. Such projections suffer from biases, or mismatches between model output and observations. While adjusting or bias-correcting model output is common, many methods exist with little understanding of their effects on forecasts of biological change. Here, we explore the bias-correction process and its effects on downstream predictive biological models. As an example, we use the Bering 10K, a downscaled ESM for a productive and economically important subarctic ecosystem. We first characterize existing biases for three categories of variables exhibiting different scales and challenges: bottom temperature, sea ice, and net primary production. We then apply eight bias-correction approaches to six indices generated from the three categories and quantify sources of uncertainty in the trajectories of these ecosystem variables. Finally, we demonstrate how different bias-correction approaches affect downstream biological models using three case studies: 1) fish thermal spawning habitat suitability, (2) predicted zooplankton abundance, and (3) match-mismatch of phytoplankton and zooplankton bloom timing. We find that biases manifest in absolute values over time and in the timing of seasonal events. Time series of all six indices differed depending on bias-correction method, differences that were propagated to downstream biological models. For a given year and simulation, depending on method, thermal spawning habitat suitability and zooplankton abundance differed up to 149% and 151%, and match-mismatch increased or did not change. Our work highlights that bias correction reduces mismatches between observations and model output but choosing an approach requires careful consideration as to not amplify and propagate bias in downstream biological models. To that end, we identify best practices for bias correcting global or regional ESMs, including a decision tree to help improve forecasts of the effects of climate change on biological systems.

ecology↗
bioRxiv2017

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

bioinformatics↗
bioRxiv2016

Gene expression models based on a reference laboratory strain are bad predictors of Mycobacterium tuberculosis complex transcriptional diversity.

Species of the Mycobacterium tuberculosis complex (MTBC) kill more people every year than any other infectious disease. As a consequence of its global distribution and parallel evolution with the human host the bacteria is not genetically homogeneous. The observed genetic heterogeneity has relevance at different phenotypic levels, from gene expression to epidemiological dynamics. However current systems biology datasets have focused in the laboratory reference strain H37Rv. By using large expression datasets testing the role of almost two hundred transcription factors, we have constructed computational models to grab the expression dynamics of Mycobacterium tuberculosis H37Rv genes. However, we have found that many of those transcription factors are deleted or likely dysfunctional across strains of the MTBC. In accordance, we failed to predict expression changes in strains with a different genetic background when compared with experimental data. The results highlight the importance of designing systems biology approaches that take into account the tubercle bacilli, or any other pathogen, genetic diversity if we want to identify universal targets for vaccines, diagnostics and treatments.

systems biology↗
bioRxiv2020

Learning to Encode Cellular Responses to Systematic Perturbations with Deep Generative Models

Components of cellular signaling systems are organized as hierarchical networks, and perturbing different components of the system often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology. Here, we investigated the capability of deep generative models (DGMs) for modeling signaling systems and learning representations for transcriptomic profiles derived from cells under diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data. Both models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately depict how cellular signals are encoded. The resulting representations have broad applications in systems biology, such as studying the mechanism-of-action of drugs.

bioinformatics↗
bioRxiv2018

Profiling gene expression of the host response to a Plasmodium vivax irradiated sporozoite immunization and infectious challenge

The development of vaccines that provide sterile protection against human malaria is a major global public health priority requiring a better understanding of the mechanisms involved in natural and vaccine-induced sterile immunity. RNAseq was used to profile gene expression of peripheral blood samples from 12 Duffy positive (Fy+) (Plasmodium vivax susceptible) volunteers enrolled in a phase 2 trial who were vaccinated with radiation attenuated P. vivax sporozoites (RAS; 5 were protected, 7 not) and from 5 Fy- (P. vivax resistant) volunteers exposed to mosquitoes harboring live non-attenuated sporozoites. Blood samples were obtained before and after immunization as well as after controlled infection with live P vivax sporozoites. The most profound changes in gene expression were observed between baseline and post-challenge, with 97 distinct signatures differentiating protected and not protected Fy+ individuals. Differentiation was also observed between Fy- and Fy+ protected individuals, notably with downregulation of multiple inflammatory responses as well as extracellular matrix-related gene activity. Analysis of transcriptional modules shows that both B-cell and T-cell signaling are reduced while cell cycle regulation, interferon response, and other informative signatures are elevated in individuals who are not protected against malaria. An asymptomatic individual had an intermediate profile indicative of differential transcription associated with pathology and symptomology. Systems biology thus provides insight into how whole malaria-attenuated sporozoites prime the immune system to protect against malaria, as well as the transcription responses that are associated with sterile protection.\n\nAUTHOR SUMMARYTranscriptomic analysis has shed considerable light on the molecular mechanisms of vaccine efficacy against a variety of viral pathogens, but has not yet been used in relation to parasite vaccinology. Here we describe whole peripheral blood gene expression profiling to contrast the contributions of myeloid and lymphoid immune responses to vaccine-induced and natural protection against Plasmodium vivax- mediated malaria. Two modes of modular bioinformatic analyses are applied to RNAseq data from pre- and post-vaccination samples from 19 volunteer participants in a clinical trial investigating the efficacy of an irradiated sporozoite vaccine. During active infection, both B- and T-cell activity are reduced in individuals who are not protected by vaccination relative to those who are protected. Lack of the Fy antigen prevents entry of parasite into red blood cells, thereby providing natural clinical immunity, but we also document differences in T-cell and neutrophil activity in these individuals relative to those protected by irradiation of the parasite. One individual who was asymptomatic for malaria but had blood parasite despite vaccination showed an intermediate response indicating how systems biology approaches can distinguish gene expression associated with pathology and infection.

genomics↗
bioRxiv2020

Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

Enhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.Significance Enhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗
bioRxiv2023

Computational modeling of DLBCL predicts response to BH3-mimetics.

In healthy cells, pro- and anti-apoptotic BCL2 family and BH3-only proteins are expressed in a delicate equilibrium. In contrast, this homeostasis is frequently perturbed in cancer cells due to the overexpression of anti-apoptotic BCL2 family proteins. Variability in the expression and sequestration of these proteins in Diffuse Large B cell Lymphoma (DLBCL) likely contributes to variability in response to BH3-mimetics. Successful deployment of BH3-mimetics in DLBCL requires reliable predictions of which lymphoma cells will respond. Here we show that a computational systems biology approach enables accurate prediction of the sensitivity of DLBCL cells to BH3-mimetics. We found that fractional killing of DLBCL, can be explained by cell-to-cell variability in the molecular abundances of signaling proteins. Importantly, by combining protein interaction data with a knowledge of genetic lesions in DLBCL cells, our in silico models accurately predict in vitro response to BH3-mimetics. Furthermore, through virtual DLBCL cells we predict synergistic combinations of BH3-mimetics, which we then experimentally validated. These results show that computational systems biology models of apoptotic signaling, when constrained by experimental data, can facilitate the rational assignment of efficacious targeted inhibitors in B cell malignancies, paving the way for development of more personalized approaches to treatment.

systems biology↗