scholarly journals Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Masayo Inoue ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Environmental Changes ◽  
Adaptive Responses ◽  
Gene Regulatory ◽  
Robust Adaptive

scholarly journals Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes

2020 ◽  
Author(s):  
Masayo Inoue ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Environmental Changes ◽  
Hill Coefficient ◽  
Adaptive Responses ◽  
Input Output ◽  
Network Analyses ◽  
Rapid Responses ◽  
Gene Regulatory

Living organisms must respond to environmental changes. Generally, accurate and rapid responses are provided by simple, unidirectional networks that connect inputs with outputs. Besides accuracy and speed, however, biological responses should also be robust to environmental or intracellular noise and mutations. Furthermore, cells must also respond to unforeseen environmental changes that have not previously been experienced, to avoid extinction prior to the evolutionary rewiring of their networks, which takes numerous generations. To address the question how cells can make robust adaptation even to unforeseen challenges, we have investigated gene regulatory networks that mutually activate or inhibit, and have demonstrated that complex entangled networks can make appropriate input-output relationships that satisfy such adaptive responses. Such entangled networks function when the expression of each gene shows sloppy and unreliable responses with low Hill coefficient reactions. To compensate for such sloppiness, several detours in the regulatory network exist. By taking advantage of the averaging over such detours, the network shows a higher robustness to environmental and intracellular noise as well as to mutations in the network, when compared to simple unidirectional circuits. Furthermore, it is demonstrated that the appropriate response to unforeseen environmental changes, allowing for functional outputs, is achieved as many genes exhibit similar dynamic expression responses, irrespective of inputs including unforeseen inputs. The similarity of the responses is statistically confirmed by applying dynamic time warping and dynamic mode decomposition methods. As complex entangled networks are commonly observed in the data in gene regulatory networks whereas global gene expression responses are measured in transcriptome analysis in microbial experiments, the present results give an answer how cells make adaptive responses and also provide a novel design principle for cellular networks.Author summaryRecent experimental advances have demonstrated that cells often have appropriate, robust responses to environmental changes, including those that have not previously been experienced. It is known that accurate and rapid responses can be achieved by simple unidirectional networks that connect straightforwardly input and outputs. However, such responses were not robust to perturbations. Here we have made numerical evolution of gene regulatory networks with mutual activation and inhibitions, and uncovered that complex entangled networks including many feedforward and feedback paths can make robust input-output responses, when each gene expression is not accurate. Remarkably, they make appropriate responses even to unforeseen environmental changes, as are supported by global, correlated responses across genes that are similar for all input signals. The results explain why cells adopt complex gene regulatory networks and exhibit global expression changes, even though they may not be advantageous in terms of their energy cost or response speed. The present results are consistent with the recent experiments on microbial gene expression changes and network analyses. This investigation provides insights into how cells survive fluctuating and unforeseen unpredictable environmental changes, and gives a universal conceptual framework to go beyond the standard picture based on a combination of network motifs.

scholarly journals MarR Family Transcription Factors fromBurkholderiaSpecies: Hidden Clues to Control of Virulence-Associated Genes

2018 ◽  
Vol 83 (1) ◽  
Author(s):  
Ashish Gupta ◽  
Anuja Pande ◽  
Afsana Sabrin ◽  
Sudarshan S. Thapa ◽  
Brennan W. Gioe ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Environmental Changes ◽  
Phenotypic Diversity ◽  
Regulation Of Gene Expression ◽  
Virulence Determinants ◽  
Human Pathogens ◽  
Plant Host ◽  
Gene Regulatory

SUMMARYSpecies within the genusBurkholderiaexhibit remarkable phenotypic diversity. Genomic plasticity, including genome reduction and horizontal gene transfer, has been correlated with virulence traits in several species. However, the conservation of virulence genes in species otherwise considered to have limited potential for infection suggests that phenotypic diversity may not be explained solely on the basis of genetic diversity. Instead, differential organization and control of gene regulatory networks may underlie many phenotypic differences. In this review, we evaluate how regulation of gene expression by members of the multiple antibiotic resistance regulator (MarR) family of transcription factors may contribute to shaping the physiological diversity ofBurkholderiaspecies, with a focus on the clinically relevant human pathogens. AllBurkholderiaspecies encode a relatively large number of MarR proteins, a feature common to bacteria that must respond to environmental changes such as those associated with host invasion. However, evolution of gene regulatory networks has likely resulted in orthologous transcription factors controlling disparate sets of genes. Adaptation to, and survival in, diverse habitats, including a human or plant host, is key to the success ofBurkholderiaspecies as (opportunistic) pathogens, and recent reports suggest that control of virulence-associated genes by MarR proteins features prominently among the survival strategies employed by these species. We suggest that identification of MarR regulons will contribute significantly to clarification of virulence determinants and phenotypic diversity.

scholarly journals A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models

2020 ◽  
Vol 2 (3) ◽  
pp. 207-226 ◽  
Author(s):  
Roberto Barbuti ◽  
Roberta Gori ◽  
Paolo Milazzo ◽  
Lucia Nasti
Keyword(s):  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Quantitative Methods ◽  
Environmental Changes ◽  
Boolean Networks ◽  
Specific Cell ◽  
Qualitative Approaches ◽  
Gene Regulatory ◽  
Gillespie’S Algorithm

Abstract Gene Regulatory Networks (GRNs) represent the interactions among genes regulating the activation of specific cell functionalities, such as reception of (chemical) signals or reaction to environmental changes. Studying and understanding these processes is crucial: they are the fundamental mechanism at the basis of cell functioning, and many diseases are based on perturbations or malfunctioning of some gene regulation activities. In this paper, we provide an overview on computational approaches to GRN modelling and analysis. We start from the biological and quantitative modelling background notions, recalling differential equations and the Gillespie’s algorithm. Then, we describe more in depth qualitative approaches such as Boolean networks and some computer science formalisms, including Petri nets, P systems and reaction systems. Our aim is to introduce the reader to the problem of GRN modelling and to guide her/him along the path that goes from classical quantitative methods, through qualitative methods based on Boolean network, up to some of the most relevant qualitative computational methods to understand the advantages and limitations of the different approaches.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

2021 ◽  
Vol 72 (1) ◽  
Author(s):  
Jose M. Alvarez ◽  
Matthew D. Brooks ◽  
Joseph Swift ◽  
Gloria M. Coruzzi
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Environmental Changes ◽  
Annual Review ◽  
Publication Date ◽  
Learning Approaches ◽  
Dynamic Events ◽  
Genome Wide ◽  
Gene Regulatory

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets—at both the local and genome-wide levels—and using them to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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