Control Of Gene Regulatory Networks Research Articles

Optimal control of gene regulatory networks for morphogen-driven tissue patterning

The generation of distinct cell types in developing tissues depends on establishing spatial patterns of gene expression. Often, this is directed by spatially graded chemical signals-known as morphogens. In the "French Flag model," morphogen concentration instructs cells to acquire specific fates. How this mechanism produces timely and organized cell-fate decisions, despite the presence of changing morphogen levels, molecular noise, and individual variability, is unclear. Moreover, feedback is present at various levels in developing tissues, breaking the link between morphogen concentration, signaling activity, and position. Here, we develop an alternative framework using optimal control theory to tackle the problem of morphogen-driven patterning: intracellular signaling is derived as the control strategy that guides cells to the correct fate while minimizing a combination of signaling levels and time. This approach recovers experimentally observed properties of patterning strategies and offers insight into design principles that produce timely, precise, and reproducible morphogen patterning.

Bifurcation Analysis and Fractional PD Control of Gene Regulatory Networks with sRNA

This paper investigates the problem of bifurcation analysis and bifurcation control of a fractional-order gene regulatory network with sRNA. Firstly, the process of stability change of system equilibrium under the influence of the sum of time delay is discussed, the critical condition of Hopf bifurcation is explored, and the effect of fractional order on the system stability domain. Secondly, aiming at the system’s instability caused by a large time delay, we design a controller to improve the system’s stability and derive the parameter conditions that satisfy the system’s stability. It is found that changing the parameter values of the controller within a certain range can control the system’s nonlinear behaviours and effectively expand the stability range. Then, a numerical example is given to illustrate the results of this paper.

MANGO: Inferring gene regulatory networks from single cell multiomics

Abstract Transcription factors (TFs) and their target gene regulatory networks (GRNs) control immune cell fate decisions during differentiation and responses to environmental cues and signals. To better understand how GRN control immune outcomes and are dysregulated in disease settings, we developed MANGO (Multiomics Aided Neural Graph Ontology), a computational method that integrates single cell gene expression and chromatin accessibility data to infer genome-wide, cell-type specific GRN. MANGO harnesses graph neural networks to generate representations of gene and regulatory link behavior and uses existing interpretable methods, like UMAPs, to visualize the GRN and gather insights about functional neighborhoods of regulators or programs of regulation. By scoring each regulatory interaction and aggregating those with common sources, TF activity can be quantified and compared between networks. We applied MANGO to a human PBMC dataset to define GRN and identify key regulatory, cell-type specific TFs, despite sometimes having low changes in expression. Additionally, we show that MANGO can distinguish cells by regulation network at single cell resolution by feeding each cell's expression and accessibility through its respective cell type's regulation network and projecting the resulting embedding in lower dimensions with a UMAP. Finally, MANGO was used to uncover memory B and T cell GRNs that prime rapid secondary immune responses. Given the increase in single cell multiome datasets, we anticipate MANGO will provide unique insights into GRN in normal and diseased immune responses, and ultimately give rise to patient specific understanding of the immune system and precision medicine. Supported by grants from NIH/NIAID (R01 AI148471) to CDS

Satellite DNA arrays barcode chromosomes to regulate genes

Satellite DNA arrays barcode chromosomes to regulate genes In this piece, Dr Helen Rowe summarizes how arrays or strings of multi-copy satellite DNAs can barcode chromosomes to regulate cell fate, by acting as molecular switches. Rowe explores Dark matter in relation to DNA and its unknown function and that makes up a huge 98% of our genome. Intense research in this area has revealed that dark matter contributes to gene-regulatory networks that serve to control where and when sets of genes are switched ON or OFF. Specialised cell types work by each expressing a unique set of genes: for example, a cell that functions in the adaptive immune system, such as an activated T cell, will switch on a different subset of genes than a neuron in the brain. Likewise, development is a highly regulated process, whereby different sets of genes are progressively switched on in different tissues. Thus, the dark matter within our genome contributes to the control of gene regulatory networks and biological systems.

A modified orthogonal matching pursuit for construction of sparse probabilistic boolean networks

Probabilistic Boolean Networks play a remarkable role in the modelling and control of gene regulatory networks. In this paper, we consider the inverse problem of constructing a sparse probabilistic Boolean network from the prescribed transition probability matrix. We propose a modified orthogonal matching pursuit for solving the inverse problem. We provide some conditions under which the proposed algorithm can recover a sparse probabilistic Boolean network. We also report some numerical results to illustrate the effectiveness of the proposed algorithm.

Temporal dynamics of a CSF1R signaling gene regulatory network involved in epilepsy.

Colony Stimulating Factor 1 Receptor (CSF1R) is a potential target for anti-epileptic drugs. However, inhibition of CSF1R is not well tolerated by patients, thereby prompting the need for alternative targets. To develop a framework for identification of such alternatives, we here develop a mathematical model of a pro-inflammatory gene regulatory network (GRN) involved in epilepsy and centered around CSF1R. This GRN comprises validated transcriptional and post-transcriptional regulations involving STAT1, STAT3, NFκB, IL6R, CSF3R, IRF8, PU1, C/EBPα, TNFR1, CSF1 and CSF1R. The model was calibrated on mRNA levels of all GRN components in lipopolysaccharide (LPS)-treated mouse microglial BV-2 cells, and allowed to predict that STAT1 and STAT3 have the strongest impact on the expression of the other GRN components. Microglial BV-2 cells were selected because, the modules from which the GRN was deduced are enriched for microglial marker genes. The function of STAT1 and STAT3 in the GRN was experimentally validated in BV-2 cells. Further, in silico analysis of the GRN dynamics predicted that a pro-inflammatory stimulus can induce irreversible bistability whereby the expression level of GRN components occurs as two distinct states. The irreversibility of the switch may enforce the need for chronic inhibition of the CSF1R GRN in order to achieve therapeutic benefit. The cell-to-cell heterogeneity driven by the bistability may cause variable therapeutic response. In conclusion, our modeling approach uncovered a GRN controlling CSF1R that is predominantly regulated by STAT1 and STAT3. Irreversible inflammation-induced bistability and cell-to-cell heterogeneity of the GRN provide a theoretical foundation to the need for chronic GRN control and the limited potential for disease modification via inhibition of CSF1R.

Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

The primary challenge with biological sciences is to control gene regulatory networks (GRNs), thereby creating therapeutic intervention methods that alter network dynamics in the desired manner. The optimal control of GRNs with probabilistic Boolean control networks (PBCNs) as the underlying structure is a solution to this challenge. Owing to the exponential growth in network size with the increase in the number of genes, we need an optimal control approach that scales to large systems without imposing any limitations on network dynamics. Furthermore, we are encouraged to use the graphics processing unit (GPU) to reduce time complexity utilizing the easily available and enhanced computational resources. The optimal control of PBCNs in the Markovian framework is developed in this paper employing an information-theoretic approach which includes Kullback-Leibler (KL) divergence. We convert the nonlinear optimal control problem of PBCN to a linear problem by using the exponential transformation of the cost function, also known as the desirability function. The linear formulation enables us to compute an optimal control using the path integral (PI) method. Furthermore, we offer sampling-based methodologies for approximating PI and therefore optimizing PBCN control. The sampling-based method can be implemented in parallel, which solves the optimal control problem for large PBCNs.

How haematopoiesis research became a fertile ground for regulatory network biology as pioneered by Eric Davidson.

This historical perspective reviews how work of Eric H. Davidson was a catalyst and exemplar for explaining haematopoietic cell fate determination through gene regulation. Researchers studying blood and immune cells pioneered many of the early mechanistic investigations of mammalian gene regulatory processes. These efforts included the characterization of complex gene regulatory sequences exemplified by the globin and T-cell/B-cell receptor gene loci, as well as the identification of many key regulatory transcription factors through the fine mapping of chromosome translocation breakpoints in leukaemia patients. As the repertoire of known regulators expanded, assembly into gene regulatory network models became increasingly important, not only to account for the truism that regulatory genes do not function in isolation but also to devise new ways of extracting biologically meaningful insights from even more complex information. Here we explore how Eric H. Davidson's pioneering studies of gene regulatory network control in nonvertebrate model organisms have had an important and lasting impact on research into blood and immune cell development. The intellectual framework developed by Davidson continues to contribute to haematopoietic research, and his insistence on demonstrating logic and causality still challenges the frontier of research today.

Closing the gap: a roadmap to single-cell regulatory genomics.

Studying the spatiotemporal control of gene regulatory networks at the single-cell level is still a challenge, yet it is key to understanding the mechanisms driving cellular identity. In their recent study, Aerts and colleagues (González-Blas etal, 2020) develop a new strategy to spatially map and integrate single-cell transcriptome and epigenome profiles in the Drosophila eye-antennal disc and to deduce in each cell precise enhancer-to-gene activity relationships. This opens a new era in the transcriptional regulation field, as it allows extracting from each of the thousands of cells forming a tissue the critical features driving their identity, from enhancer sequences to transcription factors to gene regulatory networks.

Systematic Evaluation of CRISPRa and CRISPRi Modalities Enables Development of a Multiplexed, Orthogonal Gene Activation and Repression System.

The ability to manipulate the expression of mammalian genes using synthetic transcription factors is highly desirable in both fields of basic research and industry for diverse applications, including stem cell reprogramming and differentiation, tissue engineering, and drug discovery. Orthogonal CRISPR systems can be used for simultaneous transcriptional upregulation of a subset of target genes while downregulating another subset, thus gaining control of gene regulatory networks, signaling pathways, and cellular processes whose activity depends on the expression of multiple genes. We have used a rapid and efficient modular cloning system to build and test in parallel diverse CRISPRa and CRISPRi systems and develop an efficient orthogonal gene regulation system for multiplexed and simultaneous up- and downregulation of endogenous target genes.

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology.

Investigating the use of Boolean networks for the control of gene regulatory networks

The behaviour of biological cells emerges from complex patterns of interactions between genes and their products, known as gene regulatory networks (GRN). An important aim of biology is to control the dynamics of GRNs, in order to push a cell towards or away from certain behaviours. This could potentially be done by coupling a synthetic GRN to an existing biological GRN. In this work, we use Boolean networks, a methodology for modelling and simulating GRNs, to investigate the potential for doing this. Our results demonstrate that Boolean networks can be optimised to control other Boolean networks, and that the approach scales well as the target network size increases.

Modelling and Control of Gene Regulatory Networks for Perturbation Mitigation.

Synthetic Biologists are increasingly interested in the idea of using synthetic feedback control circuits for the mitigation of perturbations to gene regulatory networks that may arise due to disease and/or environmental disturbances. Models employing Michaelis-Menten kinetics with Hill-type nonlinearities are typically used to represent the dynamics of gene regulatory networks. Here, we identify some fundamental problems with such models from the point of view of control system design, and argue that an alternative formalism, based on so-called S-System models, is more suitable. Using tools from system identification, we show how to build S-System models that capture the key dynamics of an example gene regulatory network, and design a genetic feedback controller with the objective of rejecting an external perturbation. Using a sine sweeping method, we show how the S-System model can be approximated by a linear transfer function and, based on this transfer function, we design our controller. Simulation results using the full nonlinear S-System model of the network show that the synthetic control circuit is able to mitigate the effect of external perturbations. Our study is the first to highlight the usefulness of the S-System modelling formalism for the design of synthetic control circuits for gene regulatory networks.

Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs). Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between genes. Actually, it has been realized that biological functions at the cellular level are controlled by genes; thus, by controlling the behavior of genes, it is possible to regulate these biological functions. The GRN control problem has been studied mostly with the aid of probabilistic Boolean networks, and corresponding control policies have been devised. Though turns into a more challenging problem, we argue that partial observability would be a more natural and realistic method for handling the control of GRNs. Partial observability is a fundamental aspect of the problem; it is mostly ignored and substituted by assumption that states of GRN are known precisely, prescribed as full observability. We propose a method for the construction of POMDP model of GRN from only raw gene expression data which is original and novel. Then, we introduce a novel approach to decompose/factor the POMDP model into sub-POMDP's in order to solve it efficiently with the help of divide-and-conquer strategy. In order to demonstrate the effectiveness of the proposed solution we experimented with two synthetic network and one real network data from the literature. We also conducted two sets of separate experiments used to explore the impact of network connectivity and data order to our approach CONCLUSIONS: The reported test results using both synthetic and real GRNs are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. This is due to the fact that partial observability fits well to the problem of noisy acquisition of gene expression data as there are technological limitations to measure precisely exact expression levels of genes.

Control of synthetic gene networks and its applications

BackgroundOne of the underlying assumptions of synthetic biology is that biological processes can be engineered in a controllable way.ResultsHere we discuss this assumption as it relates to synthetic gene regulatory networks (GRNs). We first cover the theoretical basis of GRN control, then address three major areas in which control has been leveraged: engineering and analysis of network stability, temporal dynamics, and spatial aspects.ConclusionThese areas lay a strong foundation for further expansion of control in synthetic GRNs and pave the way for future work synthesizing these disparate concepts.

From epigenetic landscape to phenotypic fitness landscape: Evolutionary effect of pathogens on host traits

The epigenetic landscape illustrates how cells differentiate through the control of gene regulatory networks. Numerous studies have investigated epigenetic gene regulation but there are limited studies on how the epigenetic landscape and the presence of pathogens influence the evolution of host traits. Here, we formulate a multistable decision-switch model involving several phenotypes with the antagonistic influence of parasitism. As expected, pathogens can drive dominant (common) phenotypes to become inferior through negative frequency-dependent selection. Furthermore, novel predictions of our model show that parasitism can steer the dynamics of phenotype specification from multistable equilibrium convergence to oscillations. This oscillatory behavior could explain pathogen-mediated epimutations and excessive phenotypic plasticity. The Red Queen dynamics also occur in certain parameter space of the model, which demonstrates winnerless cyclic phenotype-switching in hosts and in pathogens. The results of our simulations elucidate the association between the epigenetic and phenotypic fitness landscapes and how parasitism facilitates non-genetic phenotypic diversity.

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

TRIM28 Controls a Gene Regulatory Network Based on Endogenous Retroviruses in Human Neural Progenitor Cells

Endogenous retroviruses (ERVs), which make up 8%of the human genome, have been proposed to participate in the control of gene regulatory networks. In this study, we find a region- and developmental stage-specific expression pattern of ERVs inthe developing human brain, which is linked to a transcriptional network based on ERVs. We demonstrate that almost 10,000, primarily primate-specific, ERVs act as docking platforms for the co-repressor protein TRIM28 in human neural progenitor cells, which results in the establishment of local heterochromatin. Thereby, TRIM28 represses ERVs and consequently regulates the expression of neighboring genes. These results uncover a gene regulatory network based on ERVs that participates in control of gene expression of protein-coding transcripts important for brain development.

Diet-induced weight loss leads to a switch in gene regulatory network control in the rectal mucosa

BackgroundWeight loss may decrease risk of colorectal cancer in obese individuals, yet its effect in the colorectum is not well understood. We used integrative network modeling, Passing Attributes between Networks for Data Assimilation, to estimate transcriptional regulatory network models from mRNA expression levels from rectal mucosa biopsies measured pre- and post-weight loss in 10 obese, pre-menopausal women. ResultsWe identified significantly greater regulatory targeting of glucose transport pathways in the post-weight loss regulatory network, including “regulation of glucose transport” (FDR=0.02), “hexose transport” (FDR=0.06), “glucose transport” (FDR=0.06) and “monosaccharide transport” (FDR=0.08). These findings were not evident by gene expression analysis alone. Network analysis also suggested a regulatory switch from NFΚB1 to MAX control of MYC post-weight loss. ConclusionsThese network-based results expand upon standard gene expression analysis by providing evidence for a potential mechanistic alteration caused by weight loss.

Batch Mode TD($\lambda$ ) for Controlling Partially Observable Gene Regulatory Networks.

External control of gene regulatory networks (GRNs) has received much attention in recent years. The aim is to find a series of actions to apply to a gene regulation system making it avoid its diseased states. In this work, we propose a novel method for controlling partially observable GRNs combining batch mode reinforcement learning (Batch RL) and TD() algorithms. Unlike the existing studies inferring a computational model from gene expression data, and obtaining a control policy over the constructed model, our idea is to interpret the time series gene expression data as a sequence of observations that the system produced, and obtain an approximate stochastic policy directly from the gene expression data without estimation of the internal states of the partially observable environment. Thereby, we get rid of the most time consuming phases of the existing studies, inferring a model and running the model for the control. Results show that our method is able to provide control solutions for regulation systems of several thousands of genes only in seconds, whereas existing studies cannot solve control problems of even a few dozens of genes. Results also show that our approximate stochastic policies are almost as good as the policies generated by the existing studies.