Abstract
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.
Highlights
In the life sciences, the abundance of experimental data is rapidly increasing due to the advent of novel measurement devices
We introduce a scalable method for parameter estimation for genomescale biochemical reaction networks
Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters
Summary
The abundance of experimental data is rapidly increasing due to the advent of novel measurement devices. Proteomics and metabolomics provide large datasets [1] at a steadily decreasing cost. While these genome-scale datasets allow for a variety of novel insights [2, 3], a mechanistic understanding on the genome scale is limited by the scalability of currently available computational methods. For small- and medium-scale biochemical reaction networks mechanistic modeling contributed greatly to the comprehension of biological systems [4]. Despite great advances during the last decade, mechanistic modeling of biological systems using ODEs is still limited to processes with a few dozens biochemical species and a few hundred parameters. New algorithms are required for massive and complex genomic datasets and the corresponding genome-scale models
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