Computational studies of gene regulatory networks: in numero molecular biology
TL;DR: The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.
Abstract: Remarkable progress in genomic research is leading to a complete map of the building blocks of biology Knowledge of this map is, in turn, setting the stage for a fundamental description of cellular function at the DNA level Such a description will entail an understanding of gene regulation, in which proteins often regulate their own production or that of other proteins in a complex web of interactions The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques
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TL;DR: It is shown that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law.
Abstract: Spatially or chemically isolated functional modules composed of several cellular components and carrying discrete functions are considered fundamental building blocks of cellular organization, but their presence in highly integrated biochemical networks lacks quantitative support Here, we show that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law Within Escherichia coli, the uncovered hierarchical modularity closely overlaps with known metabolic functions The identified network architecture may be generic to system-level cellular organization
4,080 citations
TL;DR: This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equation, stochastic equations, and so on.
Abstract: The spatiotemporal expression of genes in an organism is determined by regulatory systems that involve a large number of genes connected through a complex network of interactions. As an intuitive understanding of the behavior of these systems is hard to obtain, computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This report reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, ordinary and partial differential equations, stochastic equations, Boolean networks and their generalizations, qualitative differential equations, and rule-based formalisms. In addition, the report discusses how these formalisms have been used in the modeling and simulation of regulatory systems.
2,739 citations
Cites methods from "Computational studies of gene regul..."
...While it was being prepared, several other reviews on the modeling and simulation of genetic regulatory systems appeared (Endy and Brent, 2001; Hasty et al., 2001; McAdams and Arkin, 1998; Smolen et al., 2000) [see Glass (1977), Rosen (1968), Thomas (1979) for earlier reviews]....
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TL;DR: The results provide the first direct experimental evidence of the biochemical origin of phenotypesic noise, demonstrating that the level of phenotypic variation in an isogenic population can be regulated by genetic parameters.
Abstract: Stochastic mechanisms are ubiquitous in biological systems. Biochemical reactions that involve small numbers of molecules are intrinsically noisy, being dominated by large concentration fluctuations 1‐3 . This intrinsic noise has been implicated in the random lysis/lysogeny decision of bacteriophage-λ 4 , in the loss of synchrony of circadian clocks 5,6 and in the decrease of precision of cell signals7. We sought to quantitatively investigate the extent to which the occurrence of molecular fluctuations within single cells (biochemical noise) could explain the variation of gene expression levels between cells in a genetically identical population (phenotypic noise). We have isolated the biochemical contribution to phenotypic noise from that of other noise sources by carrying out a series of differential measurements. We varied independently the rates of transcription and translation of a single fluorescent reporter gene in the chromosome of Bacillus subtilis, and we quantitatively measured the resulting changes in the phenotypic noise characteristics. We report that of these two parameters, increased translational efficiency is the predominant source of increased phenotypic noise. This effect is consistent with a stochastic model of gene expression in which proteins are produced in random and sharp bursts. Our results thus provide the first direct experimental evidence of the biochemical origin of phenotypic noise, demonstrating that the level of phenotypic variation in an isogenic population can be regulated by genetic parameters. We selected as our reporter system a single-copy chromosomal gene with an inducible promoter. As an estimated 50‐80% of bacterial genes are transcriptionally regulated 8 , this system typifies the majority of naturally occurring genes, allowing our results to be extended to natural systems. We incorporated a single copy of our reporter, the green fluorescent protein gene (gfp), into the chromosome of B. subtilis. We chose to integrate gfpinto the chromosome itself, rather than in the form of plasmids, as variation in plasmid copy number 9,10 can act as an additional and unwanted source of noise. Transcriptional efficiency was regulated by using an isopropyl-β-D-thiogalactopyranoside (IPTG)‐inducible promoter, Pspac, upstream of gfp, and varying the concentration of IPTG in the growth medium. Translational
1,682 citations
Cites methods from "Computational studies of gene regul..."
...The technique of translational noise control can be applied in the fast-growing field of artificial genetic network...
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TL;DR: This work has demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms and shed light on the design principles of biological control systems and make predictions that have been verified experimentally.
1,581 citations
Cites background from "Computational studies of gene regul..."
...Excellent reviews from other perspectives can be found elsewhere [17,18 ,19–22,23 ,24 ,25], and also book-length treatments [26–29]....
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TL;DR: Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.
Abstract: Motivation: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. Results: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks—a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.
1,571 citations
Cites background from "Computational studies of gene regul..."
...…Hartemink et al., 2001; Moler et al., 2000), neural networks (Weaver et al., 1999), differential equations (Mestl et al., 1995), and models including stochastic components on the molecular level (Arkin et al., 1998; see Smolen et al., 2000 and Hasty et al., 2001 for reviews of general models)....
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References
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TL;DR: In this article, a simulation algorithm for the stochastic formulation of chemical kinetics is proposed, which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of a given chemical system.
Abstract: There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the “reaction-rate equations”); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the “master equation”). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this “stochastic simulation algorithm” correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments df by finite time steps At. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.
10,275 citations
TL;DR: "It is certain that all bodies whatsoever, though they have no sense, yet they have perception, and whether the body be alterant or alterec, evermore a perception precedeth operation; for else all bodies would be like one to another."
8,157 citations
"Computational studies of gene regul..." refers background in this paper
...The concept of OPERON regulation was introduced over 40 years ag...
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TL;DR: The p53 tumour-suppressor gene integrates numerous signals that control cell life and death, and the disruption of p53 has severe consequences when a highly connected node in the Internet breaks down.
Abstract: The p53 tumour-suppressor gene integrates numerous signals that control cell life and death. As when a highly connected node in the Internet breaks down, the disruption of p53 has severe consequences.
6,605 citations
TL;DR: The synthesis of enzymes in bacteria follows a double genetic control, which appears to operate directly at the level of the synthesis by the gene of a shortlived intermediate, or messenger, which becomes associated with the ribosomes where protein synthesis takes place.
5,588 citations
"Computational studies of gene regul..." refers background in this paper
...The concept of OPERON regulation was introduced over 40 years ag...
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TL;DR: This work used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli, which periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells.
Abstract: Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks
4,488 citations
"Computational studies of gene regul..." refers background in this paper
...In tandem with the toggle work, a synthetic network that can generate oscillations in the concentrations of cellular proteins was presente...
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