Computational Molecular Biology 2024, Vol.14, No.2, 45-53 http://bioscipublisher.com/index.php/cmb 50 6.3 Integration with omics data The integration of omics data, such as transcriptomics, proteomics, and metabolomics, into metabolic network models has significantly enhanced their predictive capabilities. High-throughput technologies have generated vast amounts of omics data, which can be used to refine and constrain metabolic models, leading to more accurate predictions of cellular phenotypes (Blazier and Papin, 2012; Wang et al., 2021). Several methods have been developed to incorporate omics data into FBA, such as the Relative Expression and Metabolomic Integrations (REMI) method, which integrates gene expression and metabolomic data with thermodynamic constraints to provide more robust and biologically relevant results (Figure 2) (Pandey et al., 2018). These integrated models are valuable for understanding the dynamic adaptation of biochemical reaction fluxes and for exploring the interplay between metabolism and regulation in various physiological states (Wang et al., 2021). Figure 2 A genome-scale flux balance analysis (FBA) model and sets of gene-expression and/or metabolomic data In the pre-processing step, the FBA model is converted into a thermodynamic-based flux analysis (TFA) formulation, and the relative flux ratios are further assessed based on the omics data. Also based on the omics data provided, REMI translates to the REMI-TGex, REMI-TM, and REMI-TGexM methods (third block). Examples of gene-expression and metabolomic data (second block) together with a toy mode (third block) are used to illustrate the applicability of the REMI methods. The theoretical maximum consistency score (TMCS) is the number of available omics data (for metabolites, genes (reactions), or both) and the maximum consistency score (MCS) is the number of those constraints that are consistent with fluxes and could be integrated into REMI models. The MCS is always equal to or smaller than the TMCS. 7 Challenges and Future Directions 7.1 Scalability and complexity One of the primary challenges in modeling biological networks is managing the scalability and complexity of these systems. Biological networks often involve numerous components and interactions, making it difficult to create models that are both comprehensive and computationally feasible. For instance, the integration of various omics data (proteomics, genomics, lipidomics, and metabolomics) has led to large inventories of biological entities, but understanding how these entities interact remains a significant challenge (Kholodenko et al., 2012). Additionally, traditional methods such as Boolean networks and differential equations face limitations when applied to complex signal transduction networks due to their inability to handle the spatial and temporal dynamics
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