Seminar: Prof. Martin Lercher

Predicting metabolic network evolution

4 November 2013 00:00 hrs.
Figdor Lecture Theatre, 8th floor RIMLS Building, Geert Grooteplein 26-28, route 289
Predicting metabolic network evolution

Prof. Martin Lercher, Heinrich-Heine-Universität Düsseldorf


Prof. Martijn Huynen, department of CMBI, Radboudumc

04-11-2013 00:00:00Europe/AmsterdamPredicting metabolic network evolution Figdor Lecture Theatre, 8th floor RIMLS Building, Geert Grooteplein 26-28, route

Remarks / more information:

Martin _LercherEvolutionary biologists seem condemned to studying the past. Their ultimate goal, however, would be to predict the course of evolution over millions of years from what we know about how a species interacts with its environment. This endeavor has become realistic at least for one complex cellular subsystem: metabolism, for which detailed, experimentally tested models are available.  

Bacterial metabolic networks predominantly adapt through the integration or loss of genes whose products act at the interface to the environment. Metabolic evolution is relatively easy to predict in a special case, the massive gene loss following a transition to a purely endosymbiotic lifestyle. Conversely, computer simulations show that environmental adaptation of generalist metabolic networks such as that ofE. coli(which already possess a well-filled metabolic 'toolbox') require surprisingly few additional enzymes.  

The prediction of metabolic adaptation is generally hampered by incomplete information on environmental changes. However, the adaptation of plants to rising oxygen levels can be modeled by focusing on a well-defined metabolic subsystem, the fixation of CO2 in photosynthesis. This system evolves on a very simple, 'Mount-Fuji' like fitness landscape, resulting in evolutionary predictions that can be compared to experimental data from so-called C3-C4 intermediate plants. Surprisingly, individual evolutionary steps of plants towards C4 metabolism provide fitness advantages of roughly uniform size, and the rate of adaptation does not slow down during the progression towards the peak of highest fitness.

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