For the complete program please visit the relative page.
The origin of life is one of the most fundamental, but also one of the most difficult problems in science. Despite differences between various proposed scenarios, one common element seems to be the emergence of an autocatalytic set or cycle at some stage. However, there has been much disagreement as to how likely it is that such self-sustaining sets could arise and evolve ``spontaneously''. This disagreement is largely caused by a lack of mathematical models that can be formally analyzed.
In this talk, after a brief introduction to the origin of life problem itself, I will introduce a formal framework of chemical reaction systems and autocatalytic sets. I will then present both theoretical and computational results which indicate that the emergence of autocatalytic sets is highly likely, even for very moderate (and chemically plausible) levels of catalysis. Furthermore, I will present a mathematical method to identify and classify autocatalytic subsets, which elucidates possible mechanisms for evolution and emergence to happen in such sets. Finally, I will show how the formal framework can be applied successfully to real (experimental) chemical systems.
The role of extrinsic bounded noises in Systems Biology, and their interplay with intrinsic stochasticity
After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded implying the necessity of defining and implementing Extrinsic Bounded Noises (EBN). In this talk, after briefly introducing bounded noises, we shall illustrate some examples of biomolecular networks affected by this kind of noise. In particular we shall illustrate: i) The effects of EBNs in a spatiotemporal continuous model of cell polarization ii) A simulation algorithm to analyze Gillespie-like stochastic models of nonlinear networks where the model jump rates are affected by EBNs synthesized by a suitable biochemical state-dependent Langevin system. Applications will be illustrated for: the Michaelis-Menten approximation of noisy enzymatic reactions (which we show to be applicable also in co-presence of both intrinsic stochasticity and EBN), a model of enzymatic futile cycle and a genetic toggle switch.
Invited talk 3 (Tuesday 2 July, 16:00 - 17:00)
Asymptotic Dynamics of (Some) Asynchronous Cellular Automata