Working group: "Computational Biology"
2010, the 16th of November
Ferdinanda Camporesi
Title: On model reduction
Abstract:
Molecular biological models usually suffer from a large combinatorial
explosion. Indeed, proteins form complexes and modify each others, which
leads to the formation of a huge number of distinct chemical species. Thus
we cannot generate explicitly the quantitative semantics of these models,
and even less compute their properties. Model reduction aims at reducing this complexity by providing another grain of observation.
Rules-based languages offers a convenient and compact way for describing
this kind of system and combinatorial complexity is partially avoided
thanks to context-free rules, in which the set of all potential contexts
of application for an interaction does not need to be written explicitly.
Anyway that is not enough when considering quantitative aspects.
Starting from the fact that rules cannot observe observe the correlation
between specific parts of some chemical species, we are able to cut them
in autonomous fragments whose behavior is an exact projection of the
original system's behavior. This reduction can be further improved by
taking into account some symmetries between sites.
We propose a generic framework to formalize and combine model reductions and we apply it both to the differential semantics and to the stochastic one.