On projection-based model reduction of biochemical networks part II: The stochastic case

Aivar Sootla, James Anderson
2014 53rd IEEE Conference on Decision and Control  
In this paper, we consider the problem of model order reduction of stochastic biochemical networks. In particular, we reduce the order of (the number of equations in) the Linear Noise Approximation of the Chemical Master Equation, which is often used to describe biochemical networks. In contrast to other biochemical network reduction methods, the presented one is projection-based. Projection-based methods are powerful tools, but the cost of their use is the loss of physical interpretation of
more » ... nodes in the network. In order alleviate this drawback, we employ structured projectors, which means that some nodes in the network will keep their physical interpretation. For many models in engineering, finding structured projectors is not always feasible; however, in the context of biochemical networks it is much more likely as the networks are often (almost) monotonic. To summarise, the method can serve as a trade-off between approximation quality and physical interpretation, which is illustrated on numerical examples. Index Terms-model order reduction; structured model order reduction; stochastic averaging principle; linear noise approximation; chemical master equation 1 Whilst both papers are self contained the first paper is available on line at http://arxiv.org/abs/1403.3579
doi:10.1109/cdc.2014.7039952 dblp:conf/cdc/SootlaA14a fatcat:zkynjh4apnd7zp4wicgb2m7r2y