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CMU-CS-13-136
Computer Science Department
School of Computer Science, Carnegie Mellon University
CMU-CS-13-136
Parameter Identification Using δ-Decisions
for Biological Hybrid Systems
Bing Liu, Soonho Kong, Sicun Gao, Edmund M. Clarke
December 2013
CMU-CS-13-136.pdf
Keywords:
Systems biology, hybrid systems, decision procedures, parameter identifcation
Biological systems can possess multiple operational modes with specific nonlinear dynamics
in each mode. Hybrid automata and its variants are often used to model and analyze the
dynamics of such systems. Highly nonlinear hybrid automata are difficult to analysis and usually
have many parameters. An important problem is to identify parameter values using which
the system can reach certain states of interests. We present a parameter identification framework
for biological hybrid systems using δ-complete decision procedures, which can solve
satisfiability modulo theories (SMT) problems over the reals with a wide range of nonlinear
functions, including ordinary differential equations (ODEs). We demonstrate our method using
two hybrid systems: the prostate cancer progression model and the cardiac cellular action potential
model. The results show that the parameter identification framework is convenient and
efficient for performing tasks such as model falsification, personalized therapy optimization as
well as disease-related parameter range identification.
18 pages
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