Results 1  10
of
135
Modelling and analysis of gene regulatory networks,
 Nat Rev Mol Cell Biol
, 2008
"... The genome encodes thousands of genes whose pro ducts enable cell survival and numerous cellular func tions. The amounts and the temporal pattern in which these products appear in the cell are crucial to the pro cesses of life. Gene regulatory networks govern the levels of these gene products. A ge ..."
Abstract

Cited by 118 (2 self)
 Add to MetaCart
The genome encodes thousands of genes whose pro ducts enable cell survival and numerous cellular func tions. The amounts and the temporal pattern in which these products appear in the cell are crucial to the pro cesses of life. Gene regulatory networks govern the levels of these gene products. A gene regulatory net work is the collection of molecular species and their inter actions, which together control geneproduct abundance. Numerous cellular processes are affected by regulatory networks. Innovations in experimental methods have ena bled largescale studies of gene regulatory networks and can reveal the mechanisms that underlie them. Consequently, biologists must come to grips with extremely complex networks and must analyse and integrate great quantities of experimental data. Essential to this challenge are computational tools, which can answer various questions: what is the full range of behaviours that this system exhibits under different conditions? What changes are expected in the dynamics of the system if certain parts stop functioning? How robust is the system under extreme conditions? Various computational models have been developed for regulatory network analysis. These models can be roughly divided into three classes. The first class, logi cal models, describes regulatory networks qualitatively. They allow users to obtain a basic understanding of the different functionalities of a given network under dif ferent conditions. Their qualitative nature makes them flexible and easy to fit to biological phenomena, although they can only answer qualitative questions. To under stand and manipulate behaviours that depend on finer timing and exact molecular concentrations, a second class of models was developed continuous models. For example, to simulate the effects of dietary restriction on yeast cells under different nutrient concentrations 1 , users must resort to the finer resolution of continuous models. A third class of models was introduced follow ing the observation that the functionality of regulatory networks is often affected by noise. As the majority of these models account for interactions between individual molecules, they are referred to here as singlemolecule level models. Singlemolecule level models explain the relationship between stochasticity and gene regulation. Predictive computational models of regulatory net works are expected to benefit several fields. In medi cine, mechanisms of diseases that are characterized by dysfunction of regulatory processes can be elucidated. Biotechnological projects can benefit from predictive models that will replace some tedious and costly lab experiments. And, computational analysis may con tribute to basic biological research, for example, by explaining developmental mechanisms or new aspects of the evolutionary process. Here we review the available methodologies for mod elling and analysing regulatory networks. These meth odologies have already proved to be a valuable research tool, both for the development of network models and for the analysis of their functionality. We discuss their relative advantages and limitations, and outline some open questions regarding regulatory networks, includ ing how structure, dynamics and functionality relate to each other, how organisms use regulatory networks to adapt to their environments, and the interplay between regulatory networks and other cellular processes, such as metabolism. Stochasticity The property of a system whose behaviour depends on probabilities. In a model with stochasticity, a single initial state can evolve into several different trajectories, each with an associated probability. Modelling and analysis of gene regulatory networks Guy Karlebach and Ron Shamir Abstract  Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
What is flux balance analysis?
 Nat Biotech,
, 2010
"... matrix of stoichiometriesthat consumes precursor metabolites at stoichiometries that simulate biomass production. The biomass reaction is based on experimental measurements of biomass components. This reaction is scaled so that the flux through it is equal to the exponential growth rate (µ) of the ..."
Abstract

Cited by 93 (8 self)
 Add to MetaCart
matrix of stoichiometriesthat consumes precursor metabolites at stoichiometries that simulate biomass production. The biomass reaction is based on experimental measurements of biomass components. This reaction is scaled so that the flux through it is equal to the exponential growth rate (µ) of the organism. Now that biomass is represented in the model, predicting the maximum growth rate can be accomplished by calculating the conditions that result in the maximum flux through the biomass reaction. In other cases, more than one reaction may contribute to the phenotype of interest. Mathematically, an 'objective function' is used to quantitatively define how much each reaction contributes to the phenotype. Taken together, the mathematical representations of the metabolic reactions and of the objective define a system of linear equations. In flux balance analysis, these equations are solved using linear programming Suppose we want to calculate the maximum aerobic growth of E. coli under the assumption that uptake of glucose, and not oxygen, is the limiting constraint on growth. This calculation can be performed using a published model of E. coli metabolism 12 . In addition to metabolic reactions and the biomass reaction discussed above, this model also includes reactions that represent glucose and oxygen uptake into the cell. The assumptions are mathematically represented by setting the maximum rate of glucose uptake to a physiologically realistic level (18.5 mmol The core feature of this representation is a tabulation, in the form of a numerical matrix, of the stoichiometric coefficients of each reaction Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system. The matrix of stoichiometries imposes flux (that is, mass) balance constraints on the system, ensuring that the total amount of any compound being produced must be equal to the total amount being consumed at steady state From constraints to optimizing a phenotype The next step in FBA is to define a phenotype in the form of a biological objective that is relevant to the problem being studied In this primer, we illustrate the principles behind FBA by applying it to predict the maximum growth rate of Escherichia coli in the presence and absence of oxygen. The principles outlined can be applied in many other contexts to analyze the phenotypes and capabilities of organisms with different environmental and genetic perturbations (a Supplementary Tutorial provides ten additional worked examples with figures and computer code). Flux balance analysis is based on constraints The first step in FBA is to mathematically represent metabolic reactions What is flux balance analysis? Jeffrey D Orth, Ines Thiele & Bernhard Ø Palsson Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.
Petri nets for systems and synthetic biology.
 Formal Methods for Computational Systems Biology, Lecture Notes in Computer Science,
, 2008
"... Abstract. We give a description of a Petri netbased framework for modelling and analysing biochemical pathways, which unifies the qualitative, stochastic and continuous paradigms. Each perspective adds its contribution to the understanding of the system, thus the three approaches do not compete, b ..."
Abstract

Cited by 80 (23 self)
 Add to MetaCart
(Show Context)
Abstract. We give a description of a Petri netbased framework for modelling and analysing biochemical pathways, which unifies the qualitative, stochastic and continuous paradigms. Each perspective adds its contribution to the understanding of the system, thus the three approaches do not compete, but complement each other. We illustrate our approach by applying it to an extended model of the three stage cascade, which forms the core of the ERK signal transduction pathway. Consequently our focus is on transient behaviour analysis. We demonstrate how qualitative descriptions are abstractions over stochastic or continuous descriptions, and show that the stochastic and continuous models approximate each other. Although our framework is based on Petri nets, it can be applied more widely to other formalisms which are used to model and analyse biochemical networks. Motivation Biochemical reaction systems have by their very nature three distinctive characteristics. (1) They are inherently bipartite, i.e. they consist of two types of game players, the species and their interactions. (2) They are inherently concurrent, i.e. several interactions can usually happen independently and in parallel. (3) They are inherently stochastic, i.e. the timing behaviour of the interactions is governed by stochastic laws. So it seems to be a natural choice to model and analyse them with a formal method, which shares exactly these distinctive characteristics: stochastic Petri nets. However, due to the computational efforts required to analyse stochastic models, two abstractions are more popular: qualitative models, abstracting away from any time dependencies, and continuous models, commonly used to approximate stochastic behaviour by a deterministic one. We describe an overall framework to unify these three paradigms, providing a family of related models with high analytical power. The advantages of using Petri nets as a kind of umbrella formalism are seen in the following: M. Bernardo, P. Degano, and G. Zavattaro (Eds.): SFM
Network inference from cooccurrences
, 2008
"... The discovery of networks is a fundamental problem ..."
Abstract

Cited by 23 (1 self)
 Add to MetaCart
(Show Context)
The discovery of networks is a fundamental problem
Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and BioInspired Systems.
, 2010
"... ..."
(Show Context)
A unique transformation from ordinary differential equations to reaction networks
 PlosONE
, 2010
"... ..."
(Show Context)
Petri Nets for Systems Biology
 In MS Iyengar (ed.): Symbolic Systems Biology: Theory and Methods, Chapter 3, Jones
, 2010
"... Abstract In this chapter we introduce qualitative, stochastic as well as continuous Petri nets and related analysis techniques in a rather informal way, and use as running example a model of the influence of the Raf Kinase Inhibitor Protein (RKIP) on the Extracellular signal Regulated Kinase (ERK) ..."
Abstract

Cited by 12 (7 self)
 Add to MetaCart
(Show Context)
Abstract In this chapter we introduce qualitative, stochastic as well as continuous Petri nets and related analysis techniques in a rather informal way, and use as running example a model of the influence of the Raf Kinase Inhibitor Protein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signalling pathway. We show how the qualitative and quantitative analyses complement each other, and how Petri nets can be used for stepwise modelling and analysis of biochemical networks as well as for structured design of systems of ordinary differential equations. 1
Reviewed by:
, 2012
"... From its initial discovery that ROSGC membrane guanylate cyclase is a monomodal Ca 2+transduction system linked exclusively with the phototransduction machinery to the successive finding that it embodies a remarkable bimodal Ca 2+ signaling device, its widened transduction role in the general si ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
From its initial discovery that ROSGC membrane guanylate cyclase is a monomodal Ca 2+transduction system linked exclusively with the phototransduction machinery to the successive finding that it embodies a remarkable bimodal Ca 2+ signaling device, its widened transduction role in the general signaling mechanisms of the sensory neuron cells was envisioned. A theoretical concept was proposed where Ca 2+modulates ROSGC through its generated cyclic GMP via a nearby cyclic nucleotide gated channel and creates a hyper or depolarized sate in the neuron membrane (Ca 2+ Binding Proteins 1:1, 7–11, 2006). The generated electric potential then becomes a mode of transmission of the parent [Ca 2+]i signal. Ca 2+ and ROSGC are interlocked messengers in multiple sensory transduction mechanisms. This comprehensive review discusses the developmental stages to the present status of this concept and demonstrates how neuronal Ca 2+sensor (NCS) proteins are the interconnected elements of this elegant ROSGC transduction system. The focus is on the dynamism of the structural composition of this system, and how it accommodates selectivity and elasticity for the Ca 2+ signals to perform multiple tasks linked with the SENSES of vision, smell, and possibly of taste and the pineal
Comparative genomescale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets
, 2009
"... Supplemental material This article cites 28 articles, 10 of which can be accessed free at: ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
Supplemental material This article cites 28 articles, 10 of which can be accessed free at:
Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. PLoS One 2010;5:e12383
"... Background: Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
Background: Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anticancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. Methodology/Principal Findings: This work presents a constraintbase modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. Conclusions/Significance: Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most