Background: Ordinary differential equation (ODE) models are common tools for modeling biochemical systems because of their ability to capture dynamics and encode mechanism. However, ODE models often require detailed mechanistic information which limit their utility. Alternatively, logic formalisms such as boolean or fuzzy logic have emerged to address the requirement for deep mechanistic knowledge. However, modeling complex dynamics with logical models is challenging. Thus, there is an unmet need for a third approach which combines ODEs and logical models. In this study, we developed a hybrid approach which combined ODE modeling with logical rules to model a well studied biochemical network, the human coagulation system. Coagulation is mediated by serine proteases in plasma, called coagulation factors, and blood cells called platelets. The central process of coagulation, following an injury, involves the initiation, amplification and termination of a master protease called thrombin. Each of these phases is affected by multiple activators and inhibitors. Our hybrid model consisted of five differential equations augmented with several logical rules describing regulatory connections between model components, and unmodeled interactions in the network. This formulation was more than an order of magnitude smaller than current coagulation models. Results and Discussion: We estimated an ensemble of likely model parameters (N = 20) from in vitro extrinsic coagulation data sets using particle swarm optimization (PSO). We then validated the model using thrombin data sets that were not used for training. The ensemble predicted thrombin trajectories for a variety of conditions, including thrombin generation for normal and hemophilic coagulation. We then used flux analysis to understand how the network operated in a variety of conditions, and global sensitivity analysis to identify which parameters controlled the performance of the network. Flux analysis showed that the logical rules formulation encoded the transitions between initiation, amplification and termination of thrombin generation. Sensitivity analysis suggested that the amplification rate constant was more important to the time to peak thrombin, while the ATIII inhibition constant controlled thrombin exposure. Taken together, our approach produced a surprisingly predictive model given its size, suggesting the framework could also be used to model other networks important to human health and biotechnology.

Dynamic modeling of human coagulation cascade using reduced order effective kinetic models

251st National Meeting (2016)

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