Mathematical tools for evolutionary systems biology (13w5080)
Ryan Gutenkunst (University of Arizona)
Laurence Loewe (University of Wisconsin-Madison)
Peter Swain (University of Edinburgh - Centre for Synthetic and Systems Biology)
Evolutionary systems biology is currently emerging as a hot, new topic in both systems biology and evolutionary biology. Technological advances in genome sequencing and in the quantitative study of single cells have fueled an enormous development of new theory in biology. The data these technologies create has allowed researchers to quantitatively assess why a biochemical system or a whole organism has its observed phenotype. In both disciplines, it is only now that these innovations have been assimilated within the discipline and that the overlaps and common goals between disciplines are being recognized.
The quantization of molecular biology is beginning to impact the more mathematically mature evolutionary biology, and techniques from evolutionary biology are being adopted by systems biologists, but such changes are happening piecemeal. The overall goal of our BIRS workshop is to catalyze this transition.
The time is ripe now to bring together experts for discussing mathematical techniques that are important to propel the field forwards. In particular, we will focus on:
- Rigorous simulation mechanisms: forward simulations in systems biology and evolutionary biology and ecology frequently employ the same underlying techniques. Both disciplines will benefit from learning the `tricks' of the other.
- Navigating complex model and parameter spaces: the high dimensional adaptive landscape of evolutionary biology and the high dimensional parameter space of models in systems biology will benefit from techniques that summarize important features of these spaces.
- Hierarchical models: biochemical models describe what happens within cells, yet cells comprise basic building blocks for evolutionary models despite their inherent complexity. Multi-scale mathematics provides a means to combine these models and again would benefit both fields.
- Inference techniques: estimating parameters and distinguishing between models are fundamental to both fields and methods developed in one are often straightforwardly applied to the other.
Information also see website: http://evolutionarysystemsbiology.org/meeting/2013-BIRS/