Biological networks 101: computational modeling for molecular biologists

Research output: Contribution to journalArticle

  • 11 Citations

Abstract

Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.
LanguageEnglish
Pages379-384
Number of pages6
JournalGene
Volume533
Issue number42
DOIs
StatePublished - 1 Jan 2014

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Molecular Models
Mathematics
Chondrocytes
Signal Transduction
Software
Joints
Phenotype
Gene Expression

Keywords

  • EWI-23920
  • signal transduction
  • biological networks
  • METIS-300991
  • Experimental data
  • IR-88654
  • Computational modeling

Cite this

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title = "Biological networks 101: computational modeling for molecular biologists",
abstract = "Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.",
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author = "Jetse Scholma and Stefano Schivo and {Urquidi Camacho}, {Ricardo A.} and {van de Pol}, {Jan Cornelis} and Karperien, {Hermanus Bernardus Johannes} and Post, {Janine Nicole}",
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Biological networks 101: computational modeling for molecular biologists. / Scholma, Jetse; Schivo, Stefano; Urquidi Camacho, Ricardo A.; van de Pol, Jan Cornelis; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole.

In: Gene, Vol. 533, No. 42, 01.01.2014, p. 379-384.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Biological networks 101: computational modeling for molecular biologists

AU - Scholma,Jetse

AU - Schivo,Stefano

AU - Urquidi Camacho,Ricardo A.

AU - van de Pol,Jan Cornelis

AU - Karperien,Hermanus Bernardus Johannes

AU - Post,Janine Nicole

PY - 2014/1/1

Y1 - 2014/1/1

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AB - Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.

KW - EWI-23920

KW - signal transduction

KW - biological networks

KW - METIS-300991

KW - Experimental data

KW - IR-88654

KW - Computational modeling

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DO - 10.1016/j.gene.2013.10.010

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