The adaptive landscape is a black box

This picture symbolizes much of what we know about fitness landscapes: Fascinating, important, but mostly in the dark. We often don't know what axes to use, let alone how to navigate them. A key goal of EvoSysBio is to quantify various aspects of fitness landscapes to improve our understanding of this important metaphor.



Nothing in biology makes sense
except when properly quantified in the light of evolution.

Evolutionary systems biology aims to bring together the rich mechanistic details of current systems biology and the long-standing quantitative experience in evolutionary genetics in order to increase the quantitative rigor of biological analyses. For a definition see here or here.

Since current systems biology means many things to many people, it is perhaps inevitable that evolutionary systems biology might be even broader. We hope that EvoSysBio will bring together approaches from systems biology and evolutionary biology to help construct more realistic and illuminating models of life and its evolution. This goes beyond comparative analyses and aims to provide mechanistic models reliable enough to predict evolution. Weather forecasts are very difficult, yet computer models have helped tremendously. Will we be able to simulate key aspects of evolution in a similar way?

Such questions and related issues are regularly discussed at diverse Meetings on Evolutionary Systems Biology. The next meeting will be held on:

Thur + Fri,  Aug 4-5, 2016 at UW-Madison
Workshop on Evolutionary Systems Biology & Modeling, Madison, Wisconsin, USA, Website

Watch out for an upcoming meeting later this year.  


Soyer O.S. (editor, 2012). Evolutionary Systems Biology. Book series: Advances in Experimental Medicine and Biology. Springer. Contents

Loewe L (2009) A framework for evolutionary systems biology. BMC Systems Biology 3:27; Framework redefined: Loewe (2016) Systems in Evolutionary Systems Biology, pp 297–318, vol 4 in: Kliman (ed) Encyclopedia of Evolutionary Biology, Academic Press, Oxford, UK. Summarized in 10 slides on Figshare.

Ibarra, R. U., Edwards, J. S., & Palsson, B. O. (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186-9. PubMed

Wagner A (2008) "Neutralism and selectionism: a network-based reconciliation.", Nat Rev Genet 9:965-74. PubMed