Genetic and Epigenetic Regulation Schemes: Need for an Alternative Paradigm (original) (raw)

Biological regulation: controlling the system from within

Biology & Philosophy, 31(2), 237-265, 2015

Biological regulation is what allows an organism to handle the effects of a perturbation, modulating its own constitutive dynamics in response to particular changes in internal and external conditions. With the central focus of analysis on the case of minimal living systems, we argue that regulation consists in a specific form of second-order control, exerted over the core (constitutive) regime of production and maintenance of the components that actually put together the organism. The main argument is that regulation requires a distinctive architecture of functional relationships, and specifically the action of a dedicated subsystem whose activity is dynamically decoupled from that of the constitutive regime. We distinguish between two major ways in which control mechanisms contribute to the maintenance of a biological organisation in response to internal and external perturbations: dynamic stability and regulation. Based on this distinction an explicit definition and a set of organisational requirements for regulation are provided, and thoroughly illustrated through the examples of bacterial chemotaxis and the lac-operon. The analysis enables us to mark out the differences between regulation and closely related concepts such as feedback, robustness and homeostasis.

A Review of Systems Biology Perspective on Genetic Regulatory Networks with Examples

Current Bioinformatics, 2008

We present an extensive review of genetic regulatory networks (GRNs) from a system biology perspective, and discuss pertinent research issues related to GRNs such as roles of feedback loops, and internal and external noise. A succinct review of mathematical modelling is also given as mathematical modelling is central to understand the complex molecular living systems. We discuss tryptophan production system in Escherichia coli bacteria, as an example, to illustrate how feedback loops originate, and finally we present a development of a mathematical model of circadian rhythms of Drosophila (fruit fly) as a comprehensive illustration of building a system model from components. We attempt to integrate experimentally acquired molecular biology knowledge in the model developments and discussions to emphasise the importance of being true to the data and insights in the experimental molecular biology. The experimental knowledge and insights should drive the efforts in development of mathematical and computational models of living systems, not the other way around, and this is a challenging task.

Dynamic patterns of gene regulation I: Simple two-gene systems

Journal of Theoretical Biology, 2007

Regulation of gene activities is studied by means of computer assisted mathematical analysis of ordinary differential equations (ODEs) derived from binding equilibria and chemical reaction kinetics. Here, we present results on cross-regulation of two genes through activator and/or repressor binding. Arbitrary (differentiable) binding function can be used but systematic investigations are presented for gene-regulator complexes with integer valued Hill coefficients up to n ¼ 4. The dynamics of gene regulation is derived from bifurcation patterns of the underlying systems of kinetic ODEs. In particular, we present analytical expressions for the parameter values at which one-dimensional (transcritical, saddle-node or pitchfork) and/or two-dimensional (Hopf) bifurcations occur. A classification of regulatory states is introduced, which makes use of the sign of a 'regulatory determinant' D (being the determinant of the block in the Jacobian matrix that contains the derivatives of the regulator binding functions): (i) systems with Do0, observed, for example, if both proteins are activators or repressors, to give rise to one-dimensional bifurcations only and lead to bistability for nX2 and (ii) systems with D40, found for combinations of activation and repression, sustain a Hopf bifurcation and undamped oscillations for n42. The influence of basal transcription activity on the bifurcation patterns is described. Binding of multiple subunits can lead to richer dynamics than pure activation or repression states if intermediates between the unbound state and the fully saturated DNA initiate transcription. Then, the regulatory determinant D can adopt both signs, plus and minus.

Modelling Gene Regulation: (De)compositional and Template-based Strategies

Studies in History and Philosophy of Science, 2019

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine – using the mechanistic philosophy of science as an analytic springboard – the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally.

General architecture of a genetic regulatory network. Applications to embryologic control

2011

The general architecture of a genetic regulatory network consists of strong connected components of its interaction graph, to which are attached three kinds of sub-structures:-a set of up-trees, rooted in the sources of the interaction graph, represented either by small RNAs like microRNAs: nuclear miRs or mitochondrial mitomiRs, i.e., translational inhibitors respectively of the messenger mRNAs and of the transfer tRNAs, or by gene repressors and/or inductors,-a set of circuits in the core (in graph sense) of the strong connected components of the interaction graph,-a set of down-trees going to the sinks of the interaction graph, i.e., to genes controlled, but not controlling any other gene. The various state configurations it is possible to observe in the above sub-structures correspond to different dynamical asymptotic behaviors. The network dynamics have in general a small number of attractors, corresponding in the Delbrück's paradigm to the functions of the tissue they represent. Examples of such dynamics will be given in embryology: cell proliferation control network in mammals and gastrulation control network in Drosophila melanogaster.

Molecular, metabolic, and genetic control: An introduction

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2001

The living cell is a miniature, self-reproducing, biochemical machine. Like all machines, it has a power supply, a set of working components that carry out its necessary tasks, and control systems that ensure the proper coordination of these tasks. In this Special Issue, we focus on the molecular regulatory systems that control cell metabolism, gene expression, environmental responses, development, and reproduction. As for the control systems in human-engineered machines, these regulatory networks can be described by nonlinear dynamical equations, for example, ordinary differential equations, reaction-diffusion equations, stochastic differential equations, or cellular automata. The articles collected here illustrate ͑i͒ a range of theoretical problems presented by modern concepts of cellular regulation, ͑ii͒ some strategies for converting molecular mechanisms into dynamical systems, ͑iii͒ some useful mathematical tools for analyzing and simulating these systems, and ͑iv͒ the sort of results that derive from serious interplay between theory and experiment.

A simple framework to describe the regulation of gene expression in prokaryotes

Comptes Rendus Biologies, 2005

Based on the bimolecular mass action law and the derived mass conservation laws, we propose a mathematical framework in order to describe the regulation of gene expression in prokaryotes. It is shown that the derived models have all the qualitative properties of the activation and inhibition regulatory mechanisms observed in experiments. The basic construction considers genes as templates for protein production, where regulation processes result from activators or repressors connecting to DNA binding sites. All the parameters in the models have a straightforward biological meaning. After describing the general properties of the basic mechanisms of positive and negative gene regulation, we apply this framework to the self-regulation of the trp operon and to the genetic switch involved in the regulation of the lac operon. One of the consequences of this approach is the existence of conserved quantities depending on the initial conditions that tune bifurcations of fixed points. This leads naturally to a simple explanation of threshold effects as observed in some experiments. To cite this article: F. Alves, R. Dilão, C. R. Biologies 328 (2005).  2005 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Special Issue: Genomic Regulation: Experiments, Computational Modeling, and Philosophy

Journal of Computational Biology

Dedicated to the memory of Eric Davidson (1937-2015) who inspired so many of us* T his special issue of the Journal of Computational Biology comprises many of the articles presented at the international workshop titled ''Genomic Regulation: Experiments, Computational Modeling, and Philosophy.'' This workshop took place on December 4-5, 2017 at Ben-Gurion University of the Negev in Beer Sheva, Israel, and was organized by the Jacques Loeb Centre for the History and Philosophy of the Life Sciences. This workshop was inspired by discussions with Eric Davidson, a participant in almost all the previous Jacques Loeb Centre international workshops, and who passed away in September of 2015. Davidson's modeling of developmental gene regulatory networks (GRNs) in sea urchins, in which the combination of mathematical modeling and experimentation was crucial, was a source of inspiration for many of the contributions to this special issue. Davidson's models were based on experimental data; they were not purely computational simulations. He used modeling to test causal-mechanistic theories and to guide experimentation. Davidson's work is also paradigmatic for modeling in biology, because as an experimental developmental biologist, he developed mathematical modeling only at a later stage of his research, when he switched to a systems approach. Although most of the articles in this special issue deal, at least to some extent, with models of developmental GRNs, the contributions expand these themes in various ways by exploring the roles and transformations of models in biology under different philosophical, historical, and scientific perspectives, including chemistry and evolutionary biology. As an introduction to this special issue, Michel Morange provides a brief philosophical introduction to the nature and roles of models in science before focusing on models in molecular and cellular biology and their relationships with experimental data. He focuses on molecular biology in general, and on gene regulation in particular. His detailed analysis of pertinent models in molecular biology shows the diversity of models and functions and their change in history. According to Morange, the construction of a mathematical or computational model is not always the final step in a research project that started from rough data, but can also be the starting point. He also makes it clear that models are not always a step toward abstraction, but can also be the opposite, a step toward a material representation of an abstract phenomenon. He concludes that there is no universal path of progress in modeling. The diversity of models is also a topic of interest for Ute Deichmann, who focuses on quantitative modeling in research that aims at understanding fundamental features of development and heredity, starting with Mendel's modeling of the generation of plant hybrids and ending with Eric Davidson's modeling of GRNs. Among other models discussed in the article are D'Arcy Thompson's models of biological form,