Conceptual Confusion: The case of Epigenetics

ABSTRACTThe observations of phenotypic plasticity have stimulated the revival of ‘epigenetics’. Over the past 70 years the term has come in many colors and flavors, depending on the biological discipline and time period. The meanings span from Waddington’s “epigenotype” and “epigenetic landscape” to the molecular biologists’ “epigenetic marks” embodied by DNA methylation and histone modifications. Here we seek to quell the ambiguity of the name. First we place “epigenetics” in the various historical contexts. Then, by presenting the formal concepts of dynamical systems theory we show that the “epigenetic landscape” is more than a metaphor: it has specific mathematical foundations. The latter explains how gene regulatory networks produce multiple attractor states, the self-stabilizing patterns of gene activation across the genome that account for “epigenetic memory”. This network dynamics approach replaces the reductionist correspondence of molecular epigenetic modifications with concept of the epigenetic landscape, by providing a concrete and crisp correspondence.

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A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

Mathematics ◽

10.3390/math9091022 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1022

Author(s):

Gianluca D’Addese ◽

Martina Casari ◽

Roberto Serra ◽

Marco Villani

Keyword(s):

Complex Systems ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Computational Cost ◽

Graph Analysis ◽

The Past ◽

Medium Level ◽

Micro Level ◽

Gene Regulatory ◽

High Level

In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case.

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CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer

Cancer Informatics ◽

10.4137/cin.s19965 ◽

2015 ◽

Vol 14s4 ◽

pp. CIN.S19965 ◽

Cited By ~ 2

Author(s):

Simone Rubinacci ◽

Alex Graudenzi ◽

Giulio Caravagna ◽

Giancarlo Mauri ◽

James Osborne ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Dynamical Behavior ◽

Gene Activation ◽

Spatial Dynamics ◽

Multiscale Simulation ◽

Activation Patterns ◽

Gene Regulatory ◽

Cellular Phenotypes

We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation, and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modeling paradigms, two example applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second example is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue.

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Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2002092117 ◽

2020 ◽

Vol 117 (21) ◽

pp. 11589-11596 ◽

Cited By ~ 2

Author(s):

Héloïse D. Dufour ◽

Shigeyuki Koshikawa ◽

Cédric Finet

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Window ◽

Single Gene ◽

Wing Pattern ◽

The Past ◽

Novel Traits ◽

Temporal Flexibility ◽

Gene Regulatory ◽

Anterior Posterior

Organisms have evolved endless morphological, physiological, and behavioral novel traits during the course of evolution. Novel traits were proposed to evolve mainly by orchestration of preexisting genes. Over the past two decades, biologists have shown that cooption of gene regulatory networks (GRNs) indeed underlies numerous evolutionary novelties. However, very little is known about the actual GRN properties that allow such redeployment. Here we have investigated the generation and evolution of the complex wing pattern of the flySamoaia leonensis. We show that the transcription factor Engrailed is recruited independently from the other players of the anterior–posterior specification network to generate a new wing pattern. We argue that partial cooption is made possible because 1) the anterior–posterior specification GRN is flexible over time in the developing wing and 2) this flexibility results from the fact that every single gene of the GRN possesses its own functional time window. We propose that the temporal flexibility of a GRN is a general prerequisite for its possible cooption during the course of evolution.

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Artificial Gene Regulatory Networks—A Review

Artificial Life ◽

10.1162/artl_a_00267 ◽

2019 ◽

Vol 24 (4) ◽

pp. 296-328 ◽

Cited By ~ 8

Author(s):

Sylvain Cussat-Blanc ◽

Kyle Harrington ◽

Wolfgang Banzhaf

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Environmental Signals ◽

The Past ◽

Current State ◽

Behavioral Expression ◽

Gene Regulatory ◽

Living Organisms ◽

Artificial Gene ◽

Engineering Modeling

In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.

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Evolving Always-Critical Networks

Life ◽

10.3390/life10030022 ◽

2020 ◽

Vol 10 (3) ◽

pp. 22 ◽

Cited By ~ 1

Author(s):

Marco Villani ◽

Salvatore Magrì ◽

Andrea Roli ◽

Roberto Serra

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Large Scale ◽

Gene Activation ◽

Biological Significance ◽

Biological Evolution ◽

Interesting Candidate ◽

Gene Regulatory ◽

Average Gene ◽

Almost All

Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed.

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