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Why model?

Olaf Wolkenhauer1

  • 1Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock Rostock, Germany ; Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University Stellenbosch, South Africa.

Frontiers in Physiology
|January 31, 2014
PubMed
Summary
This summary is machine-generated.

Mining sequence data offers insights but may not fully explain therapy efficacy. Quantitative mechanistic modeling of cellular networks provides an alternative for understanding disease mechanisms, despite its limited scope.

Keywords:
cell biologymathematical modelingsystems biologysystems medicine

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Area of Science:

  • Systems Biology
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) enables extensive data mining for patient stratification.
  • Identifying patient groups for targeted therapies is a key application of genomic data.
  • Current limitations exist in interpreting complex genetic data for predicting therapy response.

Purpose of the Study:

  • To explore the limitations of data mining in understanding therapy mechanisms.
  • To present quantitative mechanistic modeling as an alternative approach for disease mechanism elucidation.
  • To guide researchers on the rationale and application of modeling in biological research.

Main Methods:

  • Comparative analysis of data mining and dynamical systems theory.
  • Discussion of interdisciplinary collaboration experiences.
  • Framework for understanding the "Why model?" question in biological research.

Main Results:

  • Data mining approaches and dynamical systems theory represent ends of a methodological spectrum.
  • Modeling small networks offers mechanistic insights into cellular functions.
  • Critique highlights the trade-off between model scope and biological relevance.

Conclusions:

  • A balanced perspective is needed when choosing between data mining and mechanistic modeling.
  • Understanding disease mechanisms requires considering both large-scale data and detailed models.
  • Guidance is provided for selecting appropriate modeling strategies based on research questions.