Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

ABSTRACT: Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose d

SUBMITTER: Lee D 

PROVIDER: S-EPMC7769624 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature