Abstract
Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example.
Highlights
A central problem in systems and synthetic biology is the calibration of the unknown parameters of a biochemical reaction network model on the basis of real data [1]
For the reconstruction of interaction networks, we develop a two-step method where a first step devoted to the identification of the network moment dynamics is followed by a second step that determines the network of interactions by the algorithmic solution of a factorization problem
We have investigated the problems of parameter identification and reconstruction of interactions in biochemical reaction networks
Summary
A central problem in systems and synthetic biology is the calibration of the unknown parameters of a biochemical reaction network model on the basis of real data [1]. It has been realized that the combination of stochastic modelling and single-cell measurements allows one to discriminate parameters of reaction models that would not be identifiable based on deterministic modelling counterparts and population-averaged data [8,9,10]. We discuss identifiability and reconstruction of unknown parameters and interactions in biochemical reaction networks. For the parameter estimation problem, we develop general results and inexpensive methods to test structural identifiability, and discuss connections with practical identifiability. For mathematical formulas, we stick to common notation, adding explanations where needed
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have