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Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology.

Miles MacLeod1, Nancy J Nersessian2

  • 1Centre of Excellence in the Philosophy of Social Sciences, Department of Political and Economic Studies, University of Helsinki, P.O. Box 24, 00014, Finland.

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Systems biology modeling often prioritizes prediction over mechanistic explanation. This pragmatic approach, termed top-down abstraction, yields practical understanding for controlling biological systems.

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Area of Science:

  • Systems Biology
  • Philosophy of Science

Background:

  • Large-scale systems biology modeling aims for mechanistic explanations.
  • Existing models often face constraints limiting detailed mechanistic accounts.

Purpose of the Study:

  • To explore the roles of explanation and understanding in large-scale systems biology modeling.
  • To characterize pragmatic modeling approaches and their resulting forms of understanding.

Main Methods:

  • Ethnographic data from two systems biology laboratories.
  • Analysis of modeling practices, focusing on "top-down abstraction".

Main Results:

  • Practices depart from dynamic mechanistic explanation for limited modeling goals.
  • Top-down abstraction trades mechanistic accuracy for specific system information.
  • This generates pragmatic, non-mechanistic understanding focused on prediction and control.

Conclusions:

  • The study characterizes a pragmatic approach to understanding in systems biology.
  • This approach is a response to practical constraints in large-scale systems modeling.
  • It offers an interpretation of the "systems-level understanding" sought by many researchers.