Identification of Transition Models of Biological Systems in the Presence of Transition Noise

Abstract

The identification of transition models of biological systems (Petri net models, for example) in noisy environments has not been examined to any significant extent, although they have been used to model the ideal behaviour of metabolic, signalling and genetic networks. Progress has been made in identifying such models from sequences of qualitative states of the system; and, more recently, with additional logical constraints as background knowledge. Both forms of model identification assume the data are correct, which is often unrealistic since biological systems are inherently stochastic. In this paper, we model the transition noise that can affect model identification as a Markov process where the corresponding transition functions are assumed to be known. We investigate, in the presence of this transition noise, the identification of transitions in a target model. The experiments are re-constructions of known networks from simulated data with varying amounts of transition-noise added. In each case, the target model traces a specific trajectory through the state-space. Model structures that explain the noisy state-sequences are obtained based on recent work which formulates the identification of transition models as logical consequence-finding. With noisy data, we need to extend this formulation by allowing the abduction of new transitions. The resulting structures may be both incorrect and incomplete with respect to the target model. We quantify the ability to identify the transitions in the target model, using probability estimates computed from transition-sequences using PRISM. Empirical results suggest that we are able to identify correctly the transitions in the target model with transition noise levels ranging from low to high values.