This picture symbolizes much of what we know about the adaptive landscape: Fascinating, important looking, but mostly in the dark and hard to quantify. At this stage we often don't even know what axes to use, let alone how to navigate this maze. A key goal of EvoSysBio is to quantify various aspects of the adaptive landscape to improve our understanding of is important metaphor for evolutionary biology.
 

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.

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 at ultimately arriving at a mechanistic description of life that is reliable enough to have a stab at predicting evolution. Predicting the weather is hard, yet progress is being made. Why not work towards predicting aspects of evolution that matter?

If you are interested in related work, you may want to attend upcoming Meetings on Evolutionary Systems Biology:

BIRS workshop "Mathematical Methods in Evolutionary Systems Biology", May 26-31, 2013, Banff, Canada. Website

References
Loewe L (2009) A framework for evolutionary systems biology. BMC Systems Biology 3:27. Journal Link

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