Title

Parameter Discovery For Stochastic Biological Models Against Temporal Behavioral Specifications Using An Sprt Based Metric For Simulated Annealing

Abstract

Stochastic Differential Equation (SDE) models are often used to model the dynamics of complex biological systems. The stochastic nature of these models means that some behaviors are more likely than others. It is often the case that a model's primary purpose is to study rare but interesting or important behaviors, such as the formation of a tumor, or the failure of a cyber-physical system. Unfortunately, due to the limited availability of analytic methods for SDEs, stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviors. Naturally, the cost of stochastic simulations increases with the rarity of the behavior under consideration. To address this problem, we introduce a new algorithm, RESERCHE, that is specifically designed to quantify the likelihood of rare but interesting behaviors in SDE models. Our approach relies on the use of temporal logics for specifying rare behaviors of possible interest, and on the ability of bit-vector decision procedures to reason exhaustively about fixed precision arithmetic. We also compute the probability of an observed behavior under the assumption of Gaussian noise. © 2012 IEEE.

Publication Date

5-8-2012

Publication Title

2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCABS.2012.6182640

Socpus ID

84860537236 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84860537236

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