A linear framework for modeling biochemical networks and predicting long-term states

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Abstract

Biochemical mechanisms are complex and consist of many interacting proteins, genes, and metabolites. Predicting the future states of components in biochemical processes is widely applicable to biomedical research. Here we introduce a minimal model of biochemical networks using a system of coupled linear differential equations and a corresponding numerical model. To capture biological reality, the model includes parameters for stochastic noise, constant time delay, and basal interactions from an external environment. The model is sufficient to produce key biochemical patterns including accumulation, oscillation, negative feedback, and homeostasis. Applying the model to the well-studied {\it lac} operon regulatory network reproduces key experimental observations under different metabolic conditions. By component subtraction, the model predicts the effect of genetic or chemical inhibition in the same {\it lac} regulatory network. Thus, the minimal model may lead to methods for motivating therapeutic targets and predicting the effects of experimental perturbations in biochemical networks.

Versions

➤  Version 1 (2020-05-01)

Citation

Eric Sun (2020). A linear framework for modeling biochemical networks and predicting long-term states. Researchers.One, https://researchers.one/articles/a-linear-framework-for-modeling-biochemical-networks-and-predicting-long-term-states/5f52699d36a3e45f17ae7e7e/v1.

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