scholarly journals Robustness and evolvability in transcriptional regulation

2021 ◽  
Author(s):  
José Aguilar-Rodríguez ◽  
Joshua L. Payne
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Central Position ◽  
Regulatory Circuits ◽  
Gene Regulatory ◽  
Living Organisms ◽  
Gene Regulatory Circuits ◽  
The Relationship

The relationship between genotype and phenotype is central to our understanding of development, evolution, and disease. This relationship is known as the genotype- phenotype map. Gene regulatory circuits occupy a central position in this map, because they control when, where, and to what extent genes are expressed, and thus drive fundamental physiological, developmental, and behavioral processes in living organisms as different as bacteria and humans. Mutations that affect these gene expression patterns are often implicated in disease, so it is important that gene regulatory circuits are robust to mutation. Such mutations can also bring forth beneficial phenotypic variation that embodies or leads to evolutionary adaptations or innovations. Here we review recent theoretical and experimental work that sheds light on the robustness and evolvability of gene regulatory circuits.

scholarly journals Variation, Variegation and Heritable Gene Repression in S. cerevisiae

2021 ◽  
Vol 12 ◽  
Author(s):  
Kholoud Shaban ◽  
Safia Mahabub Sauty ◽  
Krassimir Yankulov
Keyword(s):  
Gene Expression ◽  
Current Knowledge ◽  
Regulatory Circuits ◽  
Epigenetic Diversity ◽  
Short And Long Term ◽  
Unstructured Proteins ◽  
Gene Regulatory ◽  
Gene Regulatory Circuits ◽  
Insight Into

Phenotypic heterogeneity provides growth advantages for a population upon changes of the environment. In S. cerevisiae, such heterogeneity has been observed as “on/off” states in the expression of individual genes in individual cells. These variations can persist for a limited or extended number of mitotic divisions. Such traits are known to be mediated by heritable chromatin structures, by the mitotic transmission of transcription factors involved in gene regulatory circuits or by the cytoplasmic partition of prions or other unstructured proteins. The significance of such epigenetic diversity is obvious, however, we have limited insight into the mechanisms that generate it. In this review, we summarize the current knowledge of epigenetically maintained heterogeneity of gene expression and point out similarities and converging points between different mechanisms. We discuss how the sharing of limiting repression or activation factors can contribute to cell-to-cell variations in gene expression and to the coordination between short- and long- term epigenetic strategies. Finally, we discuss the implications of such variations and strategies in adaptation and aging.

scholarly journals Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Matthias Bauer ◽  
Johannes Knebel ◽  
Matthias Lechner ◽  
Peter Pickl ◽  
Erwin Frey
Keyword(s):  
Gene Expression ◽  
Quorum Sensing ◽  
Promoter Activity ◽  
Phenotypic Heterogeneity ◽  
Microbial Populations ◽  
Alternative Mechanism ◽  
Stochastic Gene Expression ◽  
Regulatory Circuits ◽  
Gene Regulatory ◽  
Gene Regulatory Circuits

Autoinducers are small signaling molecules that mediate intercellular communication in microbial populations and trigger coordinated gene expression via ‘quorum sensing’. Elucidating the mechanisms that control autoinducer production is, thus, pertinent to understanding collective microbial behavior, such as virulence and bioluminescence. Recent experiments have shown a heterogeneous promoter activity of autoinducer synthase genes, suggesting that some of the isogenic cells in a population might produce autoinducers, whereas others might not. However, the mechanism underlying this phenotypic heterogeneity in quorum-sensing microbial populations has remained elusive. In our theoretical model, cells synthesize and secrete autoinducers into the environment, up-regulate their production in this self-shaped environment, and non-producers replicate faster than producers. We show that the coupling between ecological and population dynamics through quorum sensing can induce phenotypic heterogeneity in microbial populations, suggesting an alternative mechanism to stochastic gene expression in bistable gene regulatory circuits.

scholarly journals Sequence-based model of gap gene regulatory network

2015 ◽  
Author(s):  
Konstantin N Kozlov ◽  
Vitaly V Gursky ◽  
Ivan V Kulakovskiy ◽  
Maria G Samsonova
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Gene Network ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Model Output ◽  
Wild Type ◽  
Gap Gene ◽  
Gene Regulatory

Background: The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. Results: We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and the set, from which the site of interest was left out. Conclusions: The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays. Keywords: transcription; thermodynamics; reaction-diffusion; drosophila

scholarly journals Robustness, Evolvability, and the Logic of Genetic Regulation

2014 ◽  
Vol 20 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Joshua L. Payne ◽  
Jason H. Moore ◽  
Andreas Wagner
Keyword(s):  
Expression Patterns ◽  
Genetic Regulation ◽  
Regulatory Region ◽  
Signal Integration ◽  
Random Mutation ◽  
Regulatory Circuits ◽  
Connected Set ◽  
Gene Regulatory ◽  
Time Required ◽  
Gene Regulatory Circuits

In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene's cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.

scholarly journals Gene Circuit Explorer (GeneEx): an interactive web-app for visualizing, simulating and analyzing gene regulatory circuits

2020 ◽  
Author(s):  
Vivek Kohar ◽  
Danya Gordin ◽  
Ataur Katebi ◽  
Herbert Levine ◽  
José N Onuchic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Simulated Data ◽  
Random Parameter ◽  
Supplementary Information ◽  
Gene Expression Noise ◽  
Regulatory Circuits ◽  
Gene Regulatory ◽  
Web App ◽  
Parameter Ranges ◽  
Gene Regulatory Circuits

Abstract Summary GeneEx is an interactive web-app that uses an ODE-based mathematical modeling approach to simulate, visualize and analyze gene regulatory circuits (GRCs) for an explicit kinetic parameter set or for a large ensemble of random parameter sets. GeneEx offers users the freedom to modify many aspects of the simulation such as the parameter ranges, the levels of gene expression noise and the GRC network topology itself. This degree of flexibility allows users to explore a variety of hypotheses by providing insight into the number and stability of attractors for a given GRC. Moreover, users have the option to upload, and subsequently compare, experimental gene expression data to simulated data generated from the analysis of a built or uploaded custom circuit. Finally, GeneEx offers a curated database that contains circuit motifs and known biological GRCs to facilitate further inquiry into these. Overall, GeneEx enables users to investigate the effects of parameter variation, stochasticity and/or topological changes on gene expression for GRCs using a systems-biology approach. Availability and implementation GeneEx is available at https://geneex.jax.org. This web-app is released under the MIT license and is free and open to all users and there is no mandatory login requirement. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Role of noise and parametric variation in the dynamics of gene regulatory circuits

2018 ◽  
Author(s):  
Vivek Kohar ◽  
Mingyang Lu
Keyword(s):  
Gene Expression ◽  
Stochastic Analysis ◽  
Computational Cost ◽  
Basins Of Attraction ◽  
Gillespie Algorithm ◽  
Extrinsic Factors ◽  
Regulatory Circuits ◽  
Gene Regulatory ◽  
Gene Regulatory Circuits

AbstractStochasticity in gene expression impacts the dynamics and functions of gene regulatory circuits. Intrinsic noises, including those that are caused by low copy number of molecules and transcriptional bursting, are usually studied by stochastic analysis methods, such as Gillespie algorithm and Langevin simulation. However, the role of extrinsic factors, such as cell-to-cell variability and heterogeneity in microenvironment, is still elusive. To evaluate the effects of both intrinsic and extrinsic noises, we develop a new method, named sRACIPE, by integrating stochastic analysis with random circuit perturbation (RACIPE) method. Unlike traditional methods, RACIPE generates and analyzes an ensemble of mathematical models with random kinetic parameters. Previously, we have shown that the gene expression from random models form robust and functionally related clusters. Under the framework of this randomization-based approach, here we develop two stochastic simulation schemes, aiming to reduce the computational cost without sacrificing the convergence of statistics. One scheme uses constant noise to capture the basins of attraction, and the other one uses simulated annealing to detect the stability of states. By testing the methods on several gene regulatory circuits, we found that high noise, but not large parameter variation, merges clusters together. Our approach quantifies the robustness of a gene circuit in the presence of noise and sheds light on a new mechanism of noise induced hybrid states. We have implemented sRACIPE into a freely available R package.

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