Optimizing Biochemical Pathways in Zoological Systems through Network Analysis and Computational Algorithms

Abstract

Enhanced effectiveness of zoological systems hinges on optimizing biochemical pathways with a focus on respiration, disease mitigation, and adjusting to changes in the environment. This study focuses on data from network analysis combined with algorithmic computations to focus on particular metabolism models that merge multi Omic data, genes, and the environment. Metabolic systems biology interactions were modeled with four different techniques – Flux Balance Analysis (FBA), Bayesian Network Modeling (BNM), Artificial Neural Networks (ANN), Evolutionary Optimization (EO) – and their effectiveness evaluated. From the results obtained experimentally, it was proven that the ANN-based technique predicted metabolic changes with an accuracy of 92.4%, which is higher than FBA (85.7%), BNM (88.1%), EO (90.2%). Furthermore, when multi Omic data was incorporated into the network models, the efficiency of metabolic adaptation improved by 28.3%. Compared to already available research, a 15 % higher predictive accuracy was found in non-standard models. Other results showed that external diets with the aid of DNA methylation greatly affect stability of metabolism. Optimized pathways in the system were shown to result in energy savings of 18.6%. Overall, it demonstrates that AI powered computational models could greatly improve metabolic efficiency with the greater purpose of enhancing conservation measures, disease control, and farming into productivity. Future research should be focused on enlarging datasets and improving the AI-driven model for real-time predictions on metabolism towards advancement in computational zoology.