Biological networks 101: computational modeling for molecular biologists

Jetse Scholma, Stefano Schivo, Ricardo A Urquidi Camacho, Jaco van de Pol, Marcel Karperien, Janine N Post*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)379-84
Number of pages6
JournalGene
Volume533
Issue number1
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Fingerprint

Molecular Models
Mathematics
Chondrocytes
Signal Transduction
Software
Joints
Phenotype
Gene Expression

Keywords

  • Chondrocytes/cytology
  • Computer Simulation
  • Humans
  • Molecular Biology
  • Signal Transduction

Cite this

Scholma, J., Schivo, S., Urquidi Camacho, R. A., van de Pol, J., Karperien, M., & Post, J. N. (2014). Biological networks 101: computational modeling for molecular biologists. Gene, 533(1), 379-84. https://doi.org/10.1016/j.gene.2013.10.010
Scholma, Jetse ; Schivo, Stefano ; Urquidi Camacho, Ricardo A ; van de Pol, Jaco ; Karperien, Marcel ; Post, Janine N. / Biological networks 101 : computational modeling for molecular biologists. In: Gene. 2014 ; Vol. 533, No. 1. pp. 379-84.
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Scholma, J, Schivo, S, Urquidi Camacho, RA, van de Pol, J, Karperien, M & Post, JN 2014, 'Biological networks 101: computational modeling for molecular biologists', Gene, vol. 533, no. 1, pp. 379-84. https://doi.org/10.1016/j.gene.2013.10.010

Biological networks 101 : computational modeling for molecular biologists. / Scholma, Jetse; Schivo, Stefano; Urquidi Camacho, Ricardo A; van de Pol, Jaco; Karperien, Marcel; Post, Janine N.

In: Gene, Vol. 533, No. 1, 01.01.2014, p. 379-84.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Biological networks 101

T2 - computational modeling for molecular biologists

AU - Scholma, Jetse

AU - Schivo, Stefano

AU - Urquidi Camacho, Ricardo A

AU - van de Pol, Jaco

AU - Karperien, Marcel

AU - Post, Janine N

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N2 - 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.

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.

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KW - Computer Simulation

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Scholma J, Schivo S, Urquidi Camacho RA, van de Pol J, Karperien M, Post JN. Biological networks 101: computational modeling for molecular biologists. Gene. 2014 Jan 1;533(1):379-84. https://doi.org/10.1016/j.gene.2013.10.010