Approximate probabilistic analysis of biopathway dynamics

Bing Liu, Andrei Hagiescu, Sucheendra K. Palaniappan, Bipasa Chattopadhyay, Zheng Cui, Weng-Fai Wong, P. S. Thiagarajan
2012 Computer applications in the biosciences : CABIOS  
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 efficiently construct such approximations for large models and perform model calibration and subsequent analysis. Results: We present a
more » ... We present a GPU-based scheme by which a system of ODEs is approximated as a dynamic Bayesian network (DBN). We then construct a model checking procedure for DBNs based on a simple probabilistic linear time temporal logic. The GPU implementation considerably extends the reach of our previous PC-cluster based implementation (Liu et al., 2011b) . Further, the key components of our algorithm can serve as the GPU kernel for other Monte Carlo simulations based analysis of biopathway dynamics. Similarly, our model checking framework is a generic one and can be applied in other systems biology settings. We have tested our methods on three ODE models of biopathways: the EGF-NGF pathway, the segmentation clock network and the MLCphosphorylation pathway models. The GPU implementation shows significant gains in performance and scalability while the model checking framework turns out to be convenient and efficient for specifying and verifying interesting pathways properties. Availability: The source code is freely available at
doi:10.1093/bioinformatics/bts166 pmid:22492313 fatcat:2rmckbrr3bb7nf4bkcxxtafkfq