A Look-Ahead Simulation Algorithm for DBN Models of Biochemical Pathways
Dynamic Bayesian Networks (DBNs) have been proposed  as an efficient abstraction formalism of biochemical models. They have been shown to approximate well the dynamics of biochemical models, while offering improved efficiency for their analysis [17, 18]. In this paper, we compare different representations and simulation schemes on these DBNs, testing their efficiency and accuracy as abstractions of biological pathways. When generating these DBNs, many configurations are never explored by the underlying dynamics of the biological systems. This can be used to obtain sparse representations to store and analyze DBNs in a compact way. On the other hand, when simulating these DBNs, singular configurations may be encountered, that is configurations from where no transition probability is defined. This makes simulation more complex. We initially evaluate two simple strategies for dealing with singularities: First, re-sampling simulations visiting singular configurations; second filling up uniformly these singular transition probabilities. We show that both these approaches are error prone. Next, we propose a new algorithm which samples only those configurations that avoid singularities by using a look-ahead strategy. Experiments show that this approach is the most accurate while having a reasonable run time.
KeywordsSparse Representation Simulation Algorithm Ordinary Differential Equation Dynamic Bayesian Network Continuous Time Markov Chain
This work was partially supported by ANR projects STOCH-MC (ANR-13-BS02-0011-01) and Iceberg (ANR-IABI-3096).
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