APPLICATION OF CONVEX AND NONCONVEX OPTIMIZATION METHODS IN SYSTEMS BIOLOGY AND METABOLIC NETWORKS

The pressing need for accurate and efficient biological modeling in Ghana’s emerging bioinformatics sector provides a compelling backdrop for exploring advanced optimization techniques. As global bioinformatics evolves towards complex, data-intensive systems, Ghana’s reliance on outdated models presents a critical challenge to scientific progress. This study aimed to assess the application of convex, nonconvex, and hybrid optimization methods in systems biology and metabolic networks across Ghana from 2020 to 2024. Utilizing a secondary data approach covering 133 national projects, exhaustive sampling was employed, and statistical methods including correlation coefficient matrix and multiple regression analysis were applied. The findings revealed that hybrid optimization adoption had the strongest impact on model accuracy (β = 0.455, p = 0.001), followed by convex optimization (β = 0.368, p = 0.004) and nonconvex optimization (β = 0.294, p = 0.018), with an overall model R² of 0.902 and an overall correlation coefficient between optimization adoption and model accuracy of r = 0.913. Notably, pathway prediction accuracy improved from 64.3% to 79.2%, flux balance analysis precision rose from 60.1 to 73.5, and validated biomarker identification rate increased from 18.2% to 28.1% over the study period. The results underscore that adopting advanced optimization techniques and improving computational infrastructures are key to enhancing biological modeling in Ghana. Practical recommendations include capacity building in optimization techniques, greater investment in high-performance computing, and fostering international collaborations. This study provides a pioneering roadmap for adapting optimization-driven systems biology solutions within resource-constrained environments like Ghana.