Identification of neutral biochemical network models from time series data

Abstract

Background: The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines.

Perspective: Sloppiness and emergent theories in physics, biology, and beyond

“…12 However, advances in the theory of experimental design have suggested that such estimates might be feasible after all, [61][62][63][64] although requiring considerable experimental effort. 65 The perspective provided by sloppy model analysis provides at least two alternatives to this method of operation.…”

Parameter identifiability of power-law biochemical system models

“…The lack of parameter identifiability can lead to grossly inaccurate parameter estimates, rendering the model useless for downstream applications, such as process or strain optimization for the production of certain metabolites. This identifiability problem seems to plague the parameter estimation of power-law models (Vilela et al, 2009), but parameter identifiability has not been addressed in this context. This paper aims to fill this gap.…”

Modeling multiple experiments using regularized optimization: A case study on bacterial glucose utilization dynamics

“…The data was made publicly accessible in BGFit, a biological data management and curve fitting system [9] (http://kdbio.inesc-id.pt/bgfit). Some models using similar datasets were proposed previously [4,[10][11][12][13][14], integrated in wider dynamic models for the glycolytic pathway.…”