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Causal Graphs and Biological Mechanisms

  • Alexander GebharterEmail author
  • Marie I. Kaiser
Chapter
Part of the Synthese Library book series (SYLI, volume 367)

Abstract

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice shows the need for quantitative, probabilistic models of mechanisms, too. In this chapter, we argue that the formal framework of causal graph theory is well suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the basis of an example from contemporary biological practice, namely, feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multilevel character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes while being insufficient for others.

Keywords

Causal graph theory Modeling Mechanism Probabilistic model Quantitative model 

Notes

Acknowledgments

We would like to thank the members of the research group “Causation, Laws, Dispositions, and Explanation at the Intersection of Science and Metaphysics” (FOR 1063), the participants of the colloquia at the University of Cologne and at the University of Düsseldorf, and the members of the Lake Geneva Biological Interest Group at the University of Geneva for their helpful comments on earlier drafts. This project was made possible by the funding provided by the Deutsche Forschungsgemeinschaft (DFG).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Düsseldorf Center for Logic and Philosophy of ScienceHeinrich-Heine-Universität DüsseldorfDüsseldorfGermany
  2. 2.Philosophisches SeminarUniversität zu KölnKölnGermany

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