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Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists

01 Sep 2000-
TL;DR: This work presents a graphical representation of biochemical systems, a sequence of models describing purine metabolism, and a model of the tricarboxylic acid cycle in Dictyostelium discoideum, which shows the importance of knowing the initial steps of the Glycolytic-Glycogenolytic pathway.
Abstract: Preface Introduction 1 Graphical representation of biochemical systems 2 Models of biochemical systems 3 From maps to equations 4 Computer simulation 5 Parameter estimation 6 Analytical steady-state evaluation 7 Sensitivity analysis 8 Case study 1 - Anaerobic fermentation pathway in Saccharomyces cerevisiae 9 Case study 2 - diagnosis and refinement of a model of the tricarboxylic acid cycle in Dictyostelium discoideum 10 Case study 3 - A sequence of models describing purine metabolism 11 Case study 4 - Algebraic analysis of the initial steps of the Glycolytic-Glycogenolytic pathway in perfused rat liver 12 Epilogue-Canonical modeling beyond biochemistry Appendix Hints and solutions References Author index Subject index
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Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods and approaches are discussed that enable the modelling of the dynamics of Gene regulatory systems.
Abstract: Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.

742 citations


Cites background from "Computational Analysis of Biochemic..."

  • ...However, many GRN inference approaches based on differential equations consider linear models or are limited to very specific types of non-linear functions (Voit, 2000; De Jong, 2002; see Section 3.3.2)....

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Journal ArticleDOI
TL;DR: The article presented here reviews the field of inverse modeling within BST and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms.
Abstract: The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

413 citations


Cites background or methods from "Computational Analysis of Biochemic..."

  • ...branched pathway with several feedback inhibition signals (similar pathway as model (F) but without independent variables) [35]; (L) Ten-variable system ([159])....

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  • ...on the models and the investigated biological system [1,35]....

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  • ...Voit [35] 2000 Review of various bottom-up and top-down methods S-system...

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  • ...BST models have a number of important advantages which have been discussed in detail elsewhere [27,32,33,35,36]....

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  • ...variable) [35]; (G) Three-variable cascaded pathway [35]; (H) Two-variable system [83]; (I) Seven-variable system [83]; (J) Twenty-variable system [189]; (K) Three-variable...

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Journal ArticleDOI
TL;DR: The substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially.
Abstract: Rationale: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. Results: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). Conclusions: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. Availability: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS© (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.

321 citations


Cites background from "Computational Analysis of Biochemic..."

  • ...Due to these and other unique features, BST has been the subject of numerous articles and several books (Savageau, 1976; Voit, 1991, 2000; Torres and Voit, 2002) and therefore needs no detailed review here....

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  • ...For instance, it is not intuitive what would happen to X3 if the input to the system were increased for some while....

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  • ...…Reed and Palsson, 2003), thereby avoiding the need for kinetic details like regulatory feedback signals, or fully kinetic models have been constructed with in vivo or in vitro information from different sources and, quite often, different organisms (e.g. Mulquiney and Kuchel, 2003; Voit, 2000)....

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  • ...∗To whom correspondence should be addressed....

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  • ...The only ‘help’ provided to the regression was a restriction of the ranges of possible kinetic orders, which was based on general knowledge in the field (cf. Voit, 2000: Chapter 5)....

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Journal ArticleDOI
TL;DR: A comprehensive assessment of the intermediary enzymes involved in butanol formation from carbohydrates by the saccharolytic bacterium, Clostridium acetobutylicum and other closely allied clostridia was performed to provide guidelines for potentially enhancing butanol productivity.

242 citations


Cites background from "Computational Analysis of Biochemic..."

  • ...The authors are aiming to translate the kinetic information in the present report into more comprehensive and integrated mathematical models such as Generalized Mass Action Systems or S-Systems (Voit, 2000)....

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