Model Of Gene Regulation Research Articles

Dynamical Mechanisms for Gene Regulation Mediated by Two Noncoding RNAs in Long-Term Memory Formation.

Noncoding RNAs such as miRNAs and piRNAs have long-lasting effects on the regulation of gene expression involved in long-term synaptic changes. To characterize gene regulation mediated by small noncoding RNAs associated with long-term memory in Aplysia, we consider two noncoding RNAs stimulated by 5-HT into a gene regulatory network motif model, including miR-124 that binds to and inhibits the mRNA of CREB1 and piR-F that facilitates serotonin-dependent DNA methylation to lead to repression of CREB2. Codimension-1 and -2 bifurcation analyses of 5-HT regulating both miR-124 and piR-F and a negative feedback strength for oscillation reveal rich dynamical properties of bistability and oscillations robust to variations in all other parameters. More importantly, we verify three stimulus protocols of 5-HT in experiments by our model and find that application of five pulses of 5-HT leads to a transient decrease of miR-124 but increase of piR-F concentrations, which matters sustained high level of CREB1 concentration associated with long-term memory. Furthermore, we perform bifurcation analyses for the concentrations of miR-124 and piR-F as two parameters to explore dynamical mechanisms underlying the epigenetic regulation in long-term memory formation. This study provides insights into revealing regulatory roles of epigenetic changes in gene expression involving noncoding RNAs associated with synaptic plasticity.

Proteomic/transcriptomic analysis of erythropoiesis.

Erythropoiesis is a hierarchical process by which hematopoietic stem cells give rise to red blood cells through gradual cell fate restriction and maturation. Deciphering this process requires the establishment of dynamic gene regulatory networks (GRNs) that predict the response of hematopoietic cells to signals from the environment. Although GRNs have historically been derived from transcriptomic data, recent proteomic studies have revealed a major role for posttranscriptional mechanisms in regulating gene expression during erythropoiesis. These new findings highlight the need to integrate proteomic data into GRNs for a refined understanding of erythropoiesis. Here, we review recent proteomic studies that have furthered our understanding of erythropoiesis with a focus on quantitative mass spectrometry approaches to measure the abundance of transcription factors and cofactors during differentiation. Furthermore, we highlight challenges that remain in integrating transcriptomic, proteomic, and other omics data into a predictive model of erythropoiesis, and discuss the future prospect of single-cell proteomics. Recent proteomic studies have considerably expanded our knowledge of erythropoiesis beyond the traditional transcriptomic-centric perspective. These findings have both opened up new avenues of research to increase our understanding of erythroid differentiation, while at the same time presenting new challenges in integrating multiple layers of information into a comprehensive gene regulatory model.

Estimating Equations for Density Dependent Markov Jump Processes

Reaction networks are important tools for modeling a variety of biological phenomena across a wide range of scales, for example as models of gene regulation within a cell or infectious disease outbreaks in a population. Hence, calibrating these models to observed data is useful for predicting future system behavior. However, the statistical estimation of the parameters of reaction networks is often challenging due to intractable likelihoods. Here we explore estimating equations to estimate the reaction rate parameters of density dependent Markov jump processes (DDMJP). The variance–covariance weights we propose to use in the estimating equations are obtained from an approximating process, derived from the Fokker–Planck approximation of the chemical master equation for stochastic reaction networks. We investigate the performance of the proposed methodology in a simulation study of the Lotka–Volterra predator–prey model and by fitting a susceptible, infectious, removed (SIR) model to real data from the historical plague outbreak in Eyam, England.

Auxin Response by the Numbers.

Auxin is fundamental to the growth and development of land plants, and acts in large part through the control of gene activity. Genetic and biochemical analysis of the nuclear auxin signaling pathway (NAP) has led to the establishment of a generic model for auxin-dependent gene regulation. To understand how this dynamic system operates in living cells, quantitative data are needed. For this, the liverwort Marchantia polymorpha provides a useful model system. Its limited number of NAP components, combined with experimental approaches to determine concentrations, binding affinities, and turnover rates, will enable a new, quantitative view on the mechanisms that allow auxin to control plant growth and development.

Quantifying the Stability of Coupled Genetic and Epigenetic Switches With Variational Methods.

The Waddington landscape provides an intuitive metaphor to view development as a ball rolling down the hill, with distinct phenotypes as basins and differentiation pathways as valleys. Since, at a molecular level, cell differentiation arises from interactions among the genes, a mathematical definition for the Waddington landscape can, in principle, be obtained by studying the gene regulatory networks. For eukaryotes, gene regulation is inextricably and intimately linked to histone modifications. However, the impact of such modifications on both landscape topography and stability of attractor states is not fully understood. In this work, we introduced a minimal kinetic model for gene regulation that combines the impact of both histone modifications and transcription factors. We further developed an approximation scheme based on variational principles to solve the corresponding master equation in a second quantized framework. By analyzing the steady-state solutions at various parameter regimes, we found that histone modification kinetics can significantly alter the behavior of a genetic network, resulting in qualitative changes in gene expression profiles. The emerging epigenetic landscape captures the delicate interplay between transcription factors and histone modifications in driving cell-fate decisions.

A systems-level gene regulatory network model for Plasmodium falciparum.

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

Branch prioritization motifs in biochemical networks with sharp activation

<abstract><p>The Precursor Shutoff Valve (PSV) has been proposed as a motif in biochemical networks, active for example in prioritization of primary over secondary metabolism in plants in low-input conditions. Another branch prioritization mechanism in a biochemical network is a difference in thresholds for activation of the two pathways from the branch point. It has been shown by Adams and colleagues that both mechanisms can play a part in a model of plant metabolism involving Michaelis-Menten kinetics <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup>. Here we investigate the potential role of these two mechanisms in systems with steeper activation functions, such as those involving highly cooperative reactions, by considering the limit of infinitely steep activation functions, as is done in Glass networks as models of gene regulation. We find that the Threshold Separation mechanism is completely effective in pathway prioritization in such a model framework, while the PSV adds no additional benefit, and is ineffective on its own. This makes clear that the PSV uses the gradual nature of activation functions to help shut off one branch at low input levels, and has no effect if activation is sharp. The analysis also serves as a case study in assessing behaviour of sharply-switching open systems without degradation of species.</p></abstract>

Fuzzy Linguistic Modeling of the Regulation of Drosophila Segmentation Genes

Understanding the molecular mechanisms involved in gene regulation is one of the basic issues of systems biology. Conventional approaches to modeling gene regulation often fail to distinguish between the model properties that adequately reflect the characteristics of a biological system and those associated with the nature of the model. A logical approach was developed for modeling the regulation of gene expression, which provides maximal further use of the available empirical data and minimal use of a priory hypotheses. Rules of gene regulation were formulated in the form of linguistic conditions, that is, in a language similar to human languages. Fuzzy linguistic modeling (FLM) was used as a method that more accurately reflects the actual data. This study is the first to employ FLM in constructing models of the activities of cis-regulatory modules (CRMs) and to apply the method to the problems related to expression of genes involved in biochemical patterning of embryos. Essential details were thus clarified for the mechanisms by which primary morphological gradients are read by a combination of two or more cis-regulatory modules of the same gene.

Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and has long been among the top three cancers that cause the most deaths worldwide. Therapeutic options for HCC are limited due to the pronounced tumor heterogeneity. Thus, there is a critical need to study HCC from a systems point of view to discover effective therapeutic targets, such as through the systematic study of disease perturbation in both regulation and metabolism using a unified model. Such integration makes sense for cancers as it links one of the dominant physiological features of cancers (growth, which is driven by metabolic networks) with the primary available omics data source, transcriptomics (which is systematically integrated with metabolism through the regulatory-metabolic network model). Here, we developed an integrated transcriptional regulatory-metabolic model for HCC molecular stratification and the prediction of potential therapeutic targets. To predict transcription factors (TFs) and target genes affecting tumorigenesis, we used two algorithms to reconstruct the genome-scale transcriptional regulatory networks for HCC and normal liver tissue. which were then integrated with corresponding constraint-based metabolic models. Five key TFs affecting cancer cell growth were identified. They included the regulator CREB3L3, which has been associated with poor prognosis. Comprehensive personalized metabolic analysis based on models generated from data of liver HCC in The Cancer Genome Atlas revealed 18 genes essential for tumorigenesis in all three subtypes of patients stratified based on the non-negative matrix factorization method and two other genes (ACADSB and CMPK1) that have been strongly correlated with lower overall survival subtype. Among these 20 genes, 11 are targeted by approved drugs for cancers or cancer-related diseases, and six other genes have corresponding drugs being evaluated experimentally or investigationally. The remaining three genes represent potential targets. We also validated the stratification and prognosis results by an independent dataset of HCC cohort samples (LIRI-JP) from the International Cancer Genome Consortium database. In addition, microRNAs targeting key TFs and genes were also involved in established cancer-related pathways. Taken together, the multi-scale regulatory-metabolic model provided a new approach to assess key mechanisms of HCC cell proliferation in the context of systems and suggested potential targets.

Neural Tube Patterning: From a Minimal Model for Rostrocaudal Patterning Towards an Integrated 3D Model

Rostrocaudal patterning of the neural tube is a key event in vertebrate brain development. Underlying this process are morphogen gradients which specify the fate of neural progenitor cells dose-dependently, leading to the partitioning of the tube. Although this is extensively studied experimentally, an integrated view of the genetic circuitry is currently lacking. Here, we present a minimal gene regulatory model for rostrocaudal patterning, whose configuration was determined in a data-driven way, resulting in a tristable network motif. Further analysis identified the repression of hindbrain fate as promising strategy for the improvement of current protocols for the generation of dopaminergic neurons. To test our model further, we integrated it with an established model for dorsoventral patterning to simulate the steady state pattern of a physiological 3D neural tube. Simulations of elevated morphogen secretion illustrate the sensitivity of neural tube patterning to morphogen levels, implying that these are tightly regulated during development.

Nonequilibrium models of optimal enhancer function

In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the "linking rate," by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics-an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.

Elucidation of molecular links between obesity and cancer through microRNA regulation

BackgroundObesity contributes to high cancer risk in humans and the mechanistic links between these two pathologies are not yet understood. Recent emerging evidence has associated obesity and cancer with metabolic abnormalities and inflammation where microRNA regulation has a strong implication.MethodsIn this study, we have developed an integrated framework to unravel obesity-cancer linkage from a microRNA regulation perspective. Different from traditional means of identifying static microRNA targets based on sequence and structure properties, our approach focused on the discovery of context-dependent microRNA-mRNA interactions that are potentially associated with disease progression via large-scale genomic analysis. Specifically, a meta-regression analysis and the integration of multi-omics information from obesity and cancers were presented to investigate the microRNA regulation in a dynamic and systematic manner.ResultsOur analysis has identified a total number of 2,143 unique microRNA-gene interactions in obesity and seven types of cancer. Common interactions in obesity and obesity-associated cancers are found to regulate genes in key metabolic processes such as fatty acid and arachidonic acid metabolism and various signaling pathways related to cell growth and inflammation. Additionally, modulated co-regulations among microRNAs targeting the same functional processes were reflected through the analysis.ConclusionWe demonstrated the statistical modeling of microRNA-mediated gene regulation can facilitate the association study between obesity and cancer. The entire framework provides a powerful tool to understand multifaceted gene regulation in complex human diseases that can be generalized in other biomedical applications.

TREAP: A New Topological Approach to Drug Target Inference

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug’s mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA’s accuracy. We find that network topology predominantly determines the accuracy of ProTINA’s predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

A mathematical model of flagellar gene regulation and construction in Salmonella enterica

Millions of people worldwide develop foodborne illnesses caused by Salmonella enterica (S. enterica) every year. The pathogenesis of S. enterica depends on flagella, which are appendages that the bacteria use to move through the environment. Interestingly, populations of genetically identical bacteria exhibit heterogeneity in the number of flagella. To understand this heterogeneity and the regulation of flagella quantity, we propose a mathematical model that connects the flagellar gene regulatory network to flagellar construction. A regulatory network involving more than 60 genes controls flagellar assembly. The most important member of the network is the master operon, flhDC, which encodes the FlhD4C2 protein. FlhD4C2 controls the construction of flagella by initiating the production of hook basal bodies (HBBs), protein structures that anchor the flagella to the bacterium. By connecting a model of FlhD4C2 regulation to a model of HBB construction, we investigate the roles of various feedback mechanisms. Analysis of our model suggests that a combination of regulatory mechanisms at the protein and transcriptional levels induce bistable FlhD4C2 levels and heterogeneous numbers of flagella. Also, the balance of regulatory mechanisms that become active following HBB construction is sufficient to provide a counting mechanism for controlling the total number of flagella produced.

Quantitative dissection of transcription in development yields evidence for transcription-factor-driven chromatin accessibility

Thermodynamic models of gene regulation can predict transcriptional regulation in bacteria, but in eukaryotes, chromatin accessibility and energy expenditure may call for a different framework. Here, we systematically tested the predictive power of models of DNA accessibility based on the Monod-Wyman-Changeux (MWC) model of allostery, which posits that chromatin fluctuates between accessible and inaccessible states. We dissected the regulatory dynamics of hunchback by the activator Bicoid and the pioneer-like transcription factor Zelda in living Drosophila embryos and showed that no thermodynamic or non-equilibrium MWC model can recapitulate hunchback transcription. Therefore, we explored a model where DNA accessibility is not the result of thermal fluctuations but is catalyzed by Bicoid and Zelda, possibly through histone acetylation, and found that this model can predict hunchback dynamics. Thus, our theory-experiment dialogue uncovered potential molecular mechanisms of transcriptional regulatory dynamics, a key step toward reaching a predictive understanding of developmental decision-making.

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.

Percolation on the gene regulatory network

We consider a simplified model for gene regulation, where gene expression is regulated by transcription factors (TFs), which are single proteins or protein complexes. Proteins are in turn synthesised from expressed genes, creating a feedback loop of regulation. This leads to a directed bipartite network in which a link from a gene to a TF exists if the gene codes for a protein contributing to the TF, and a link from a TF to a gene exists if the TF regulates the expression of the gene. Both genes and TFs are modelled as binary variables, which indicate, respectively, whether a gene is expressed or not, and a TF is synthesised or not. We consider the scenario where for a TF to be synthesised, all of its contributing genes must be expressed. This results in an ‘AND’ gate logic for the dynamics of TFs. By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the necessary conditions, in the network parameter space, under which bipartite networks can support a multiplicity of stable gene expression patterns, under noisy conditions, as required in stable cell types. In particular, the analysis reveals the possibility of a bi-stability region, where the extensive percolating cluster is or is not resilient to perturbations. This is remarkably different from the transition observed in standard percolation theory. Finally, we consider perturbations involving single node removal that mimic gene knockout experiments. Results reveal the strong dependence of the gene knockout cascade on the logic implemented in the underlying network dynamics, highlighting in particular that avalanche sizes cannot be easily related to gene-gene interaction networks.

Structural Properties of an S-system Model of Mycobacterium tuberculosis Gene Regulation

Magombedze and Mulder (2013) studied the gene regulatory system of Mycobacterium tuberculosis (Mtb) by partitioning this into three subsystems based on putative gene function and role in dormancy/latency development. Each subsystem, in the form of S-system, is represented by an embedded chemical reaction network (CRN), defined by a species subset and a reaction subset induced by the set of digraph vertices of the subsystem. For the embedded networks of S-system, we showed interesting structural properties and proved that all S-system CRNs (with at least two species) are discordant. Analyzing the subsystems as subnetworks, where arcs between vertices belonging to different subsystems are retained, we formed a digraph homomorphism from the corresponding subnetworks to the embedded networks. Lastly, we explored the modularity concept of CRN in the context of the digraph.

The HRI-regulated transcription factor ATF4 activates BCL11A transcription to silence fetal hemoglobin expression

AbstractReactivation of fetal hemoglobin remains a critical goal in the treatment of patients with sickle cell disease and β-thalassemia. Previously, we discovered that silencing of the fetal γ-globin gene requires the erythroid-specific eIF2α kinase heme-regulated inhibitor (HRI), suggesting that HRI might present a pharmacologic target for raising fetal hemoglobin levels. Here, via a CRISPR-Cas9–guided loss-of-function screen in human erythroblasts, we identify transcription factor ATF4, a known HRI-regulated protein, as a novel γ-globin regulator. ATF4 directly stimulates transcription of BCL11A, a repressor of γ-globin transcription, by binding to its enhancer and fostering enhancer-promoter contacts. Notably, HRI-deficient mice display normal Bcl11a levels, suggesting species-selective regulation, which we explain here by demonstrating that the analogous ATF4 motif at the murine Bcl11a enhancer is largely dispensable. Our studies uncover a linear signaling pathway from HRI to ATF4 to BCL11A to γ-globin and illustrate potential limits of murine models of globin gene regulation.

Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events.In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting.With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.