Causal graphs and biological mechanisms

In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Dordrecht: Springer. pp. 55-86 (2014)

Authors
Marie I. Kaiser
Bielefeld University
Alexander Gebharter
University of Groningen
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 paper 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 ba-sis 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 multi-level 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
Categories (categorize this paper)
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

Our Archive
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.
Thinking About Mechanisms.Peter K. Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
Rethinking Mechanistic Explanation.Stuart Glennan - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S342-353.

View all 32 references / Add more references

Citations of this work BETA

How to Model Mechanistic Hierarchies.Lorenzo Casini - 2016 - Philosophy of Science 83 (5):946-958.

View all 8 citations / Add more citations

Similar books and articles

Graphical Models, Causal Inference, and Econometric Models.Peter Spirtes - 2005 - Journal of Economic Methodology 12 (1):3-34.
The Concept of Mechanism in Biology.Daniel J. Nicholson - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):152-163.
Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Jon Williamson - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (1):5-33.
On Levels of Cognitive Modeling.Ron Sun, Andrew Coward & Michael J. Zenzen - 2005 - Philosophical Psychology 18 (5):613-637.
Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts.Alex Broadbent - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. pp. 45--69.
Mechanisms, Malfunctions and Explanation in Medicine.Mauro Nervi - 2010 - Biology and Philosophy 25 (2):215-228.

Analytics

Added to PP index
2013-02-21

Total views
268 ( #25,831 of 2,290,759 )

Recent downloads (6 months)
72 ( #9,985 of 2,290,759 )

How can I increase my downloads?

Downloads

My notes

Sign in to use this feature