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