( robustness |
evolutionary simulations |
mathematical models |
protein pathways |
signal transduction )
Theoretical Biology Group, Institute for Integrative Biology, Swiss Federal Institute of Technology (ETH), Universitätsstrasse 16, ETH Zentrum, CHN K12.2, CH-8092 Zürich, Switzerland
Edited by Steven Strogatz, Cornell University, Ithaca, NY, and accepted by the Editorial Board September 14, 2006 (received for review May 30, 2006) It is not clear how biological pathways evolve to mediate a certain physiological response and why they show a level of complexity that is generally above the minimum required to achieve such a response. One possibility is that pathway complexity increases due to the nature of evolutionary mechanisms. Here, we analyze this possibility by using mathematical models of biological pathways and evolutionary simulations. Starting with a population of small pathways of three proteins, we let the population evolve with mutations that affect pathway structure through duplication or deletion of existing proteins, deletion or creation of interactions among them, or addition of new proteins. Our simulations show that such mutational events, coupled with a selective pressure, leads to growth of pathways. These results indicate that pathways could be driven toward complexity via simple evolutionary mechanisms and that complexity can arise without any specific selective pressure for it. Furthermore, we find that the level of complexity that pathways evolve toward depends on the selection criteria. In general, we find that final pathway size tends to be lower when pathways evolve under stringent selection criteria. This leads to the counterintuitive conclusion that simple response requirements on a pathway would facilitate its evolution toward higher complexity.
Evolution
Evolution of complexity in signaling pathways

and Sebastian Bonhoeffer
Author contributions: O.S.S. and S.B. designed research; O.S.S. performed research; O.S.S. analyzed data; and O.S.S. and S.B. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
To whom correspondence should be addressed.
Present address: The Microsoft Research-University of Trento, Centre for Computational and Systems Biology, Piazza Manci 17, 38050 Povo (TN), Italy.
www.pnas.org/cgi/doi/10.1073/pnas.0604449103
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