Bing Liu

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We present a statistical model checking (SMC) based framework for studying ordinary differential equation (ODE) models of bio-pathways. We address cell-to-cell variability explicitly by using probability distributions to model initial concentrations and kinetic rate values. The core component of our framework is an SMC procedure for verifying the dynamical(More)
MOTIVATION Biopathways are often modeled as systems of ordinary differential equations (ODEs). Such systems will usually have many unknown parameters and hence will be difficult to calibrate. Since the data available for calibration will have limited precision, an approximate representation of the ODEs dynamics should suffice. One must, however, be able to(More)
Biochemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we(More)
Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here(More)
Hybrid systems whose mode dynamics are governed by non-linear ordinary differential equations (ODEs) are often a natural model for biological processes. However such models are difficult to analyze. To address this, we develop a probabilistic analysis method by approximating the mode transitions as stochastic events. We assume that the probability of making(More)
We present a novel code generation scheme for GPUs. Its key feature is the platform-aware generation of a heterogeneous pool of threads. This exposes more data-sharing opportunities among the concurrent threads and reduces the memory requirements that would otherwise exceed the capacity of the on-chip memory. Instead of the conventional strategy of focusing(More)
Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks [CHECK END OF SENTENCE]. To analyze these models, one must compute the probability distribution over system states at a given time point. Doing this exactly is infeasible for large models; hence one must use approximate algorithms. The Factored(More)
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