Two weeks earlier this month I attended the second ‘Evolutionary Systems Biology Workshop‘ in Edinburgh, organized by Laurence Loewe. First thing to say is that Edinburgh is a very nice city. It was exactly as I would have imagined a Scottish city: mountains, a castle, people with kilts, soups, pipes, pubs, wonderful beer, and even a greek temple on a hill. Too bad I didn’t have the time to visit it as turist.
The workshop has been very interesting and there has been a lot of nice talks; however, the biggest point of discussion was: “What is the best definition for Evolutionary Systems Biology?“.
In short, Systems Biology is the science that creates formulas and models to predict how a single individual will react in a certain situation. An example would be: I put an Yeast cell in a certain environment, and I want to find the equations and formulas that makes me predict best how this Yeast cell would react to the environment.
Evolutionary Systems Biology is similar to Systems Biology, but it tries to predict the reaction of an organism to an environment given its genome or changes to its genome. A nice example, explained by John Yin in the first talk of the workshop, is: I have a single strand mRNA virus, whose genome codifies only 4 types of proteins. What happens if I change the order of these genes? Is the virus more efficient in infecting a cell, if I put the gene that encodes for the polymerase after the one that encodes for the capside, or vice-versa?
So, Evolutionary Systems Biology can be resumed as this, the science that defines models to predict what happens to an organism when I modify its genome. EvolSysBio also studies what is the importance of the order and position of a gene within the genome, to the fitness. I admit that this can not be immediately associated with what we do in my lab (I will explain it in other posts), but it is interesting nevertheless. I wrote down some notes on the talks that I have attended, let’s see if I am able to transcribe them better and put them in this website soon.
1: Lim KI, Yin J. Computational fitness landscape for all gene-order permutations of an RNA virus. PLoS Comput Biol. 2009 Feb;5(2):e1000283. Epub 2009 Feb 6. PubMed PMID: 19197345; PubMed Central PMCID: PMC2627932. Open Access.