Realistic simulation of time-course measurements in systems biology

Abstract

MotivationIn systems biology, the analysis of complex nonlinear systems faces many methodological challenges. However, the performance evaluation of competing methods is limited by the small amount of publicly available data from biological experiments. Therefore, simulation studies with a realistic representation of the data are a promising alternative and bring the advantage of knowing the ground truth. ResultsWe present an approach for designing a realistic simulation study. Based on 19 published systems biology models with experimental data, we assess typical measurement characteristics such as observables, observation type, measurement error, and observation times. For the latter, we estimate typical time features by fitting a transient response function. We demonstrate the approach on a meal model of the glucose insulin system, a mitogen-activated protein-kinase cascade and a model for the epidermal growth factor signaling. The performance of the realistic design is validated on 9 systems biology models in terms of optimization, integration and identifiability. For any dynamic model downloaded from an online database, our algorithm analyzes the model dynamics and specifies a realistic experimental design. The approach is specifically suited for systematic benchmarking of methods for timecourse data in the context of systems biology. In particular, various application settings such as number of parameters, initial conditions, error model etc. can be tested. AvailabilityThe approach is implemented in the MATLAB-based modelling toolbox Data2Dynamics and available at https://github.com/Data2Dynamics/d2d.