Kinetic modeling and analysis of nonlinear biochemical networks with no quantitative information

ObjectiveThe complexity of biochemical networks is enormous and difficult to unravel by intuitive reasoning alone. Kinetic modeling has traditionally been proposed as tool for the analysis of network dynamics. However, one of the major bottlenecks of computational modeling is the lack of quantitative information, which is a necessity for simulation and system identification. The objective of this study was therefore to develop a method that can simulate and analyze the dynamical behavior of nonlinear biochemical networks without requiring accurate time-series data, precise parameter values or other quantitative data. To validate the practical relevance of our approach, a nonlinear kinetic model of extracellular matrix (ECM) remodeling was chosen.ResultsWe developed a qualitative modeling procedure that is able to link various types of dynamical behavior (limit cycle, single steady state, multiple steady states) of the complex ECM model to unique sets of parameter inequalities. The outcome of this qualitative analysis was similar to results found by cumbersome numerical analysis of the original model. ConclusionsIn our test case, qualitative analysis deduces three sets of inequalities, constraining the parameters to guarantee bistability in the ECM model. When these parameter restrictions are not satisfied, only a single stable steady state is present. These observations correspond exactly to extensive numerical exploration of the phase space.The scarcity of quantitative information and large amounts of qualitative data in current biological research has motivated the development of a qualitative modeling method that accurately describes and predicts the dynamics of nonlinear biochemical networks. Our approach can be applied to a large variety of biochemical networks and might assist in smart experimental design, parameter estimation, metabolic engineering and identifying specific drug targets