Abstract
The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.
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Notes
One limitation of this kind of network analysis is that it represents regulatory interactions as pairwise relations between discrete and static objects (nodes in the network) and assumes that these are uniformly distributed throughout the cell. However, in many regulatory systems (e.g. in development) the spatial distribution of molecules is crucial and needs to be accounted for. Mechanistic research providing such details can therefore provide a useful corrective to network analysis. (We would like to thank an anonymous reviewer for stressing this point.)
In this context, ‘path length’ does not measure physical distance (only number of edges) and ‘organization’ does not track spatial organization of the target system (but rather its functional organization).
Mcm2p and Mcm3p, along with Cdc46p, Cdc47p, and Cdc54p are all synthesized during the M/G1 cell phase transition and form a complex with constitutively expressed Mcm6p. Next Cdc6p (also expressed at the M/G1 transition) recruits this complex to the Orc complex during the G1 phase. Finally, Cdc45p is expressed early in the S phase and is proposed to then recruit the whole complex to the site where replication originates.
The expression patterns change in response to initial conditions, in this context maternally expressed transcription factors that can be measured experimentally and manipulated, and the state space analysis can be based on systems parameters fitted to gene expression data.
We would like to thank an anonymous reviewer for making this point.
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Acknowledgements
This paper was initiated while the authors were fellows at the Center for Philosophy of Science, University of Pittsburgh. We would like to thank the Center for providing us such a rich environment that was conducive to establishing this collaboration. We also thank three anonymous referees for their critical suggestions. Ingo Brigandt’s work is also supported by the Social Sciences and Humanities Research Council of Canada (Insight Grant 435-2016-0500).
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Sara Green, Maria Şerban, Raphael Scholl, Nicholaos Jones, Ingo Brigandt and William Bechtel have contributed equally to this work.
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Green, S., Şerban, M., Scholl, R. et al. Network analyses in systems biology: new strategies for dealing with biological complexity. Synthese 195, 1751–1777 (2018). https://doi.org/10.1007/s11229-016-1307-6
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DOI: https://doi.org/10.1007/s11229-016-1307-6