Dizzy: stochastic simulation of large-scale genetic regulatory networks
01 Apr 2005-Journal of Bioinformatics and Computational Biology (Imperial College Press)-Vol. 3, Iss: 2, pp 415-436
TL;DR: Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks, is described.
Abstract: We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.
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TL;DR: This DSRS substantially improved the stability of biodiesel-producing strains and increased the titer and yield threefold and can be extended to many other biosynthetic pathways to balance metabolism, thereby increasing product titers and conversion yields and stabilizing production hosts.
Abstract: Microbial production of chemicals is now an attractive alternative to chemical synthesis. Current efforts focus mainly on constructing pathways to produce different types of molecules. However, there are few strategies for engineering regulatory components to improve product titers and conversion yields of heterologous pathways. Here we developed a dynamic sensor-regulator system (DSRS) to produce fatty acid-based products in Escherichia coli, and demonstrated its use for biodiesel production. The DSRS uses a transcription factor that senses a key intermediate and dynamically regulates the expression of genes involved in biodiesel production. This DSRS substantially improved the stability of biodiesel-producing strains and increased the titer to 1.5 g/l and the yield threefold to 28% of the theoretical maximum. Given the large number of natural sensors available, this DSRS strategy can be extended to many other biosynthetic pathways to balance metabolism, thereby increasing product titers and conversion yields and stabilizing production hosts.
757 citations
TL;DR: It is shown that increased variability in gene expression, affected by the sequence of the TATA box, can be beneficial after an acute change in environmental conditions.
636 citations
Cites methods from "Dizzy: stochastic simulation of lar..."
...The model was implemented using the chemical kinetics simulation software Dizzy (Ramsey et al., 2005)....
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...Dizzy: stochastic simulation of large-scale genetic regulatory networks....
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...The model was implemented using the chemical kinetics simulation software Dizzy ( Ramsey et al., 2005 )....
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TL;DR: Bio-PEPA is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use of general kinetic laws.
377 citations
Cites background from "Dizzy: stochastic simulation of lar..."
...The derivation of Gillespie’s rates for reactions with more than two reactants is presented in [56] and these reactions are supported by various stochastic simulators (for instance [4])....
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TL;DR: A “linearizer” circuit was constructed by adding TetR autoregulation to the original cascade and observed a massive (7-fold) reduction of noise at intermediate induction and linearization of dose–response before saturation, indicating that linearization is highly robust to parameter variations.
Abstract: Although several recent studies have focused on gene autoregulation, the effects of negative feedback (NF) on gene expression are not fully understood. Our purpose here was to determine how the strength of NF regulation affects the characteristics of gene expression in yeast cells harboring chromosomally integrated transcriptional cascades that consist of the yEGFP reporter controlled by (i) the constitutively expressed tetracycline repressor TetR or (ii) TetR repressing its own expression. Reporter gene expression in the cascade without feedback showed a steep (sigmoidal) dose–response and a wide, nearly bimodal yEGFP distribution, giving rise to a noise peak at intermediate levels of induction. We developed computational models that reproduced the steep dose–response and the noise peak and predicted that negative autoregulation changes reporter expression from bimodal to unimodal and transforms the dose–response from sigmoidal to linear. Prompted by these predictions, we constructed a “linearizer” circuit by adding TetR autoregulation to our original cascade and observed a massive (7-fold) reduction of noise at intermediate induction and linearization of dose–response before saturation. A simple mathematical argument explained these findings and indicated that linearization is highly robust to parameter variations. These findings have important implications for gene expression control in eukaryotic cells, including the design of synthetic expression systems.
327 citations
Cites methods from "Dizzy: stochastic simulation of lar..."
...Ramsey S, Orrell D, Bolouri H (2005) Dizzy: Stochastic simulation of large-scale genetic regulatory networks....
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...Stochastic simulations based on the Gillespie algorithm were performed in Dizzy (45) and analyzed with Octave (University of Wisconsin, Madison)....
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...Gillespie algorithm were performed in Dizzy (45) and analyzed with Octave...
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TL;DR: The Network-Free Stochastic Simulator (NFsim) is introduced, a general- Purpose modeling platform that overcomes the combinatorial nature of molecular interactions and enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software.
Abstract: The Network-Free Stochastic Simulator (NFsim) allows the representation of complex biological systems as rule-based models and facilitates coarse-graining of the reaction mechanisms.
322 citations
Cites background from "Dizzy: stochastic simulation of lar..."
...A limited number of simulators currently support basic approximations in the form of mathematical expressions, such as the Michaelis-Menten function for enzymatic activit...
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References
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TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
32,980 citations
01 Jan 1994
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Abstract: Note: Includes bibliographical references, 3 appendixes and 2 indexes.- Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08
19,881 citations
Book•
16 Feb 2013
TL;DR: This well written book is enlarged by the following topics: B-splines and their computation, elimination methods for large sparse systems of linear equations, Lanczos algorithm for eigenvalue problems, implicit shift techniques for theLR and QR algorithm, implicit differential equations, differential algebraic systems, new methods for stiff differential equations and preconditioning techniques.
Abstract: This well written book is enlarged by the following topics:
$B$-splines and their computation, elimination methods for
large sparse systems of linear equations, Lanczos algorithm for
eigenvalue problems, implicit shift techniques for the $LR$ and
$QR$ algorithm, implicit differential equations, differential
algebraic systems, new methods for stiff differential
equations, preconditioning techniques and convergence rate of
the conjugate gradient algorithm and multigrid methods for
boundary value problems. Cf. also the reviews of the German
original editions.
6,270 citations
TL;DR: In this paper, an exact method is presented for numerically calculating, within the framework of the stochastic formulation of chemical kinetics, the time evolution of any spatially homogeneous mixture of molecular species which interreact through a specified set of coupled chemical reaction channels.
5,875 citations
TL;DR: This work constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated and reveals how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
Abstract: Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
5,209 citations