POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Theoretical Computer Science - Special issue: Computational systems biology
Bisimulation relations for weighted automata
Theoretical Computer Science
Efficient, correct simulation of biological processes in the stochastic pi-calculus
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Scalable simulation of cellular signaling networks
APLAS'07 Proceedings of the 5th Asian conference on Programming languages and systems
Abstracting the Differential Semantics of Rule-Based Models: Exact and Automated Model Reduction
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Rule-based modelling of cellular signalling
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
Lumpability abstractions of rule-based systems
Theoretical Computer Science
Hi-index | 0.00 |
Molecular biological models usually suffer from a dramatic combinatorial blow up. Indeed, proteins form complexes and can modify each others, which leads to the formation of a huge number of distinct chemical species (ie non-isomorphic connected components of proteins). Combinatorial complexity forbids an explicit description of the quantitative semantics (stochastic or differential), since the set of states is usually a vector space the dimension of which is the number of distinct chemical species. Model reduction aims at reducing this complexity by providing another grain of observation. Fragments-based reduction consists in computing a set (hopefully smaller than the set of chemical species) of pieces of chemical species, such that the evolution of the number (or concentration) of these pieces can be soundly described in self-consistent abstract quantitative semantics. In this paper, we provide several intuitive examples so as to give some intuition about why this approach may work; and why stochastic semantics are more difficult to abstract than differential semantics.