Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs.

ABSTRACT:

Motivation

Dynamical properties of biochemical pathways (BPs) help in understanding the functioning of living cells. Their in silico assessment requires simulating a dynamical system with a large number of parameters such as kinetic constants and species concentrations. Such simulations are based on numerical methods that can be time-expensive for large BPs. Moreover, parameters are often unknown and need to be estimated.

Results

We developed a framework for the prediction of dynamical properties of BPs directly from the structure of their graph representation. We represent BPs as Petri nets, which can be automatically generated, for instance, from standard SBML representations. The core of the framework is a neural network for graphs that extracts relevant information direc

SUBMITTER: Fontanesi M 

PROVIDER: S-EPMC10651430 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature