Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

Alves, Rui; Vilaprinyó, Ester; Sorribas, Albert

doi:10.2174/157489308784340694

Cited by 10 publications

(8 citation statements)

References 382 publications

(129 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The disruption of specific alternative splicing events has been implicated in several human genetic diseases including cancer [129–131]. Along with new technologies providing more and more “-omics” data, improved bioinformatics tools, statistical methods and systems biology approaches need to be developed so that researchers can comprehensively incorporate the complex networks of various relationships [132] (e.g. gene-environment, gene-drug, gene-gender, gene-age, cis - or trans - regulators) that affect complex traits such as drug response and toxicity.…”

Section: Current Developments and Outlookmentioning

confidence: 99%

Use of Cell Lines in the Investigation of Pharmacogenetic Loci

Zhang

Dolan

2009

CPD

View full text Add to dashboard Cite

Drug response and toxicity, complex traits that are often highly varied among individuals, likely involve multiple genetic and non-genetic factors. Pharmacogenomic research aims to individualize therapy in an effort to maximize efficacy and minimize toxicity for each patient. Cell lines can be used as a model system for cellular pharmacologic effects, which include, but are not limited to, drug-induced cytotoxicity or apoptosis, biochemical effects and enzymatic reactions. Because severe toxicities may be associated with drugs such as chemotherapeutics, cell lines derived from healthy individuals or patients provide a convenient model to study how human genetic variation alters response to these drugs that would be unsafe or unethical to administer to human volunteers. In addition to the traditional candidate gene approaches that focus on well-understood candidate genes and pathways, the availability of extensive genotypic and phenotypic data on some cell line models has begun to allow genome-wide association (GWA) studies to simultaneously test the entire human genome for associations with drug response and toxicity. Though with some important limitations, the use of these cell lines in pharmacogenomic discovery demonstrates the promise of constructing a more comprehensive model that may ultimately integrate both genetic and non-genetic factors to predict individual response and toxicity to anticancer drugs.

show abstract

Section: Current Developments and Outlookmentioning

confidence: 99%

Use of Cell Lines in the Investigation of Pharmacogenetic Loci

Zhang

Dolan

2009

CPD

View full text Add to dashboard Cite

show abstract

“…Each velocity can be represented by different functional forms that may include various parameters. The choice of a particular mathematical form is not trivial and can either facilitate or hinder the task of analyzing the optimal design and the adaptive responses to different environmental changes [ 31 , 32 ]. From all the available formalisms, the so-called power-law formalism is one of the most convenient.…”

Section: Methodsmentioning

confidence: 99%

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Guillén‐Gosálbez

Sorribas

2009

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

BackgroundOptimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements.ResultsWe first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer-approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shockConclusionOur results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

show abstract

“…The vast majority of these have targeted gene regulatory or protein–protein interaction networks (e.g., Refs ), but the results of these genome‐ and proteome‐based inferences can be translated, under certain assumptions, into inferences regarding specific metabolic networks . It is also sometimes feasible, and of course desirable, to combine the metabolomics top‐down approaches with more traditional bottom‐up approaches . While most reverse‐engineering methods and applications have targeted gene networks, some have been applied to metabolic networks as well.…”

Section: Steps 1 and 2: Identification Of Constituents Topology And mentioning

confidence: 99%

The best models of metabolism

Voit

2017

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models.

show abstract

Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

Cited by 10 publications

References 382 publications

Use of Cell Lines in the Investigation of Pharmacogenetic Loci

Use of Cell Lines in the Investigation of Pharmacogenetic Loci

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

The best models of metabolism

Contact Info

Product

Resources

About