Orkun Soyer has just finished his excellent presentation at CoSBi on the use of toy models for understanding the principles that govern biological pathways, in particular signaling pathways. One can obviously imagine several scenarios for how pathways came about:
The key point, however, is that we might be able to understand something about pathways through computational studies of simple toy models. The toy model discussed throughout the talk was bacterial chemotaxis:
The idea is that evolution can to some extend be approximated as an optimization process, in which the objective function corresponds to fitness. In case of the “tumble or swim” problem, computational simulations allowed simple regulatory network to evolve that mimic the food-finding behavior of bacteria.
He also presented an interesting view on how biological complexity has evolved. The idea is to show how complex systems can evolve even if assuming a (weak) selection against complexity:
I think that his results provide a lot of insight into how real signaling may have evolved, although all the simulations are based on simplistic toy models. I recommend that you download Orkun Soyer’s slides if you want to know more.
This talk ends the Computational and Systems Biology course at CoSBi.
Gabor Balazsi has just finished a very interesting presentation on the interplay between molecular networks, gene expression noise, and evolutionary selection – here is the opening slide:
In the first part of his talk he gave a nice introduction to global network topology and network motifs – this should be nothing new to people familiar with the work of the Barabasi and Alon labs. He also explained the “Commander, Intermediate, Executor” model for hierarchical regulatory networks, which I had personally not heard about before, and the concept of “origons”, which seems quite use for understanding the response of large signaling networks to environmental cues.
The second part of his talk was about stochastic noise in gene expression. Genetically identical cells in a culture may express the same protein at different levels; this is a result of random noise influencing transcription, mRNA degradation, translation, and protein degradation. This is simply a consequence of low copy numbers giving rise to stochastic, as opposed to deterministic, behavior.
Finally, he talked about how noise at the level of gene expression can influence the survival of species in a changing environment. This part of his talk was kicked off with the funniest slide of his presentation:
I guess it should be seen as a lesson on how not to do. He made some very good points about how noise plays hardly any role in multicellular organisms that reproduce sexually. By contrast, stochastic variation within clonal bacterial cultures provides much higher chance of survival when faced with sudden stress such treatment with anti-bacterial drugs. I would have liked to hear more about this, but unfortunately there was not much time left for this part of the presentation due to technical problems with the projectors. It looks like Guy Shinar picked the safe strategy for his presentation.
All in all, I found it to be a really inspiring talk. I have uploaded his slides in case if you want to take a look at it.
Fifteen minutes ago, Attila Csikasz-Nagy opened the Computational and Systems Biology Course at CoSBi in Trento, Italy:
Over the coming week, I will be covering the most interesting presentations and posters here on the blog and in the Picasa web album.