title: Approximate probabilistic analysis of biopathway dynamics.
abstract: BACKGROUND
  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 graphical processing unit (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 bio-pathways: the epidermal growth factor-nerve growth factor pathway, the segmentation clock network and the MLC-phosphorylation pathway models. The GPU implementation shows significant gains in performance and scalability whereas the model checking framework turns out to be convenient and efficient for specifying and verifying interesting pathways properties.
  
  
  BACKGROUND
  The source code is freely available at http://www.comp.nus.edu.sg/~rpsysbio/pada-gpu/
authors.affiliation: Department of Computer Science, National University of Singapore, Singapore.
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.forename: Andrei
authors.initials: A
authors.lastname: Hagiescu
authors.forename: Sucheendra K
authors.initials: SK
authors.lastname: Palaniappan
authors.forename: Bipasa
authors.initials: B
authors.lastname: Chattopadhyay
authors.forename: Zheng
authors.initials: Z
authors.lastname: Cui
authors.forename: Weng-Fai
authors.initials: WF
authors.lastname: Wong
authors.forename: P S
authors.initials: PS
authors.lastname: Thiagarajan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001683:Biological Clocks; D003196:Computer Graphics; D004815:Epidermal Growth Factor / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D018994:Myosin Light Chains / Q000378:metabolism; D020932:Nerve Growth Factor / Q000378:metabolism; D011381:Programming Languages; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*; D013917:Thrombin / Q000378:metabolism
chemical_list: D018994:Myosin Light Chains; D004815:Epidermal Growth Factor; D020932:Nerve Growth Factor; D013917:Thrombin