Call for Abstracts
Sometime during the ESEB Congress 20-25 Aug 2011
Update 24 Jan 2011
(Latest updates on this page)
The emerging field of evolutionary systems biology draws on systems biology, laboratory evolution, population genetics and comparative genomics to answer system level biological questions within an evolutionary framework. Such an evolutionary approach might allow characterising and understanding the significance of observed diversity in molecular systems, uncovering evolutionary principles and extending predictions made in model organisms to other species. In addition, by extending molecular systems biology models to predict fitness correlates evolutionary systems biology can enable new insights into the adaptive landscape and genotype-phenotype maps and may thus be used to address problems in population genetics. Such work can inspire mechanistic simulations of evolution, testing increasingly detailed evolutionary hypotheses.
The biannual congress of the European Society for Evolutionary Biology usually attracts well over 1000 participants. It is divided into several parallel symposia tracks (see ESEB conference website), one of which will feature the symposium on evolutionary systems biology. This symposium provides an exciting opportunity for interacting with some of the leading researchers in EvoSysBio. More details below.
- 28 Feb 2011: Abstract submission deadline (submit)
- 28 Feb 2011: Reduced registration rates end (register)
- 15 May 2011: Cancellation refund deadline (link)
- 20-25 Aug 2011: Symposium. Precise date not yet clear.
- Prof. Jennifer Reed
University of Wisconsin-Madison, WI, USA (webpage)
Using computational models to explore and leverage biochemical networks
Integrated genome-scale models of metabolism and transcriptional regulation can be used to predict cellular phenotypes for adaptively evolved and un-evolved strains. The construction of such models is currently a challenge since computational methods that automate the refinement of such models are not available. We recently developed an algorithm, GeneForce, which pinpoints what aspects of the model cause incorrect phenotype predictions. We used the approach to refine integrated models of Escherichia coli metabolism and regulation, and experimentally confirmed some of the algorithm’s suggested refinements. After making the model refinements the model’s accuracy improved to (~80.0%) when comparing predictions to over 50,000 measured phenotypes. We believe that this general computational approach will enable the rapid development and improvement of integrated genome-scale metabolic and regulatory network models. Since such models can be constructed from genome annotations and cis-regulatory network predictions, they can be developed for less characterized microorganisms. Once accurate integrated metabolic and regulatory models are constructed, our recently developed computational methods can be used to propose metabolic engineering strategies to improve chemical production using these models. The genetic strategies identified can include deletion of transcription factors and metabolic genes, as well as over expression of metabolic genes. Using this approach we have identified strategies for improving chemical production by E. coli. Together approaches can be applied to improve our understanding of metabolism and regulation in microbial organisms and to improve production of a wide variety of compounds through modification of metabolic and regulatory networks.
- Prof. Andreas Wagner
University of Zürich, Switzerland (webpage)
The origins of evolutionary innovations
Life can be viewed as a four billion year long history of innovations. These range from dramatic macroscopic innovations like the evolution of wings or eyes, to a myriad molecular changes that form the basis of macroscopic innovations. We know many examples of innovations -- qualitatively new phenotypes that can provide a critical advantage in the right environment -- but have no systematic understanding of the principles that allow organisms to innovate. Most phenotypic innovations result from changes in three classes of systems: metabolic networks, regulatory circuits, and protein or RNA molecules. I will discuss evidence that these classes of systems share two important features that are essential for their ability to innovate.
Detailed description of symposium
Evolutionary genetics has a long history of successful quantitative modelling, especially in areas where functional molecular details can be abstracted by selection coefficients. Molecular biology has a long history of uncovering functional molecular details and has recently started to engage in quantitative modelling, giving rise to the field of molecular systems biology. We propose to bring these two fields together to help elucidate fundamental problems in evolutionary biology that include explaining the evolution of complex traits, predicting the distribution of mutational effects and epistasis. A central question in addressing such evolutionary topics is how molecular systems biology models can be extended to define biologically relevant fitness correlates that can be computed in silico and how to establish their calibration to experimentally observable fitness measures.
This symposium builds on the successful 2009 ESEB symposium on evolutionary systems biology. We aim to bring together researchers that have contributed towards a synthesis of evolutionary genetics and functional molecular properties. We believe that the current excitement in systems biology is an excellent opportunity for progress in many fundamental evolutionary questions, if the new models that are being constructed can be extended to include fitness correlates that turn them into powerful tools for investigating the adaptive landscape. Although such realistic systems models have become increasingly available in the past few years, their application to evolutionary issues has remained limited so far. Thus, this interdisciplinary symposium on evolutionary systems biology is timely. It aims to facilitate the generation of novel evolutionary research programmes.
Dr. Laurence Loewe
Evolutionary Systems Biology Group
Laboratory of Genetics and
Wisconsin Institute for Discovery
University of Wisconsin-Madison
330 North Orchard Street, Madison, WI, 53715
Tel: +1 (608) 316 4324
Dr. Orkun Soyer
School of Engineering, Computing and Mathematics
University of Exeter
Harrison Building, Streatham Campus, North Park Road
Exeter EX4 4QF, England, UK
Tel: +44 (1392) 72 3615