Computational Molecular Biology 2024, Vol.14, No.2, 45-53 http://bioscipublisher.com/index.php/cmb 49 simulations have been widely used to investigate pathogenic mechanisms, virtual screening, and drug resistance mechanisms, providing essential information that guides the drug discovery and design process (Liu et al., 2018). Deep learning methods, such as graph neural networks (GNNs), have also emerged as powerful tools for predicting protein functions and interactions (Figure 1), facilitating in silico drug discovery and development (Muzio et al., 2020). These computational methods enable the identification of candidate disease genes or drug targets, which can be further validated experimentally, thus accelerating the drug discovery pipeline (Liang and Kelemen, 2018). Figure 1 On the GCN layer of the k-layer GCN (Aodpted from Muzio et al., 2020) Image caption: Each layer of the GCN is aggregated on each node's neighborhood using the node representation of the previous layer in the network. The aggregates in each layer then pass through an activation function (in this case, ReLU) before moving on to the next layer. The network can be used to generate a variety of different outputs: to predict new edges in the input network (link prediction), to classify individual nodes in the input graph (node classification), or to classify the entire input graph (graph classification)(Aodpted from Muzio et al., 2020) 6 Metabolic Network Modeling 6.1 Flux balance analysis Flux Balance Analysis (FBA) is a widely used computational method for predicting the flow of metabolites through a metabolic network. It relies on the principle of mass conservation and uses a stoichiometric matrix along with a biologically relevant objective function, such as biomass production or ATP generation, to identify optimal reaction flux distributions (Vidal-Limon et al., 2022). FBA has been instrumental in analyzing genome-scale reconstructions of various organisms and has applications in metabolic engineering and drug target identification (Sen, 2022). However, FBA has limitations, such as its inability to predict intracellular fluxes under all environmental conditions, necessitating the development of alternative strategies (Megchelenbrink et al., 2015). 6.2 Constraint-based optimization Constraint-based optimization methods extend the capabilities of FBA by incorporating additional constraints, such as kinetic, thermodynamic, and regulatory constraints, to improve the accuracy of metabolic flux predictions (Pandey et al., 2018; Sen et al., 2022). These methods allow for a more detailed and realistic representation of metabolic networks, enabling the analysis of complex cellular behaviors and the identification of key metabolic bottlenecks. For instance, the Maximum Metabolic Flexibility (MMF) method utilizes the observation that microorganisms often favor a suboptimal growth rate to maintain metabolic flexibility, thereby improving the quantitative predictions made by FBA.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==