Computational Design of Synthetic Pathways

Abstract

Synthetic biology aims to engineer living cells as programmable factories for producing valuable chemicals - pharmaceuticals, biofuels, food ingredients, and materials -- by designing and installing synthetic metabolic pathways that convert simple substrates into target products. However, the combinatorial space of possible pathways (>10^6 theoretical routes for most target molecules) makes manual pathway design impractical, and the majority of computationally proposed pathways fail experimentally due to thermodynamic infeasibility, enzyme incompatibility, or metabolic burden on the host cell. This study developed and benchmarked six pathway design approaches -- manual expert design, retrosynthesis rule-based search (RetroPath2.0), thermodynamic pathway analysis (eQuilibrator), constraint-based flux modelling (cobrapy), graph neural network pathway prediction (GNN-Path), and a proposed AI-Integrated Pathway Designer (AIPD) combining retrosynthetic search with thermodynamic filtering, enzyme-substrate compatibility prediction (ESM-2-based), metabolic flux optimisation, and reinforcement learning for host-aware pathway ranking -- for designing production pathways to five high-value target molecules in Escherichia coli: 1,3-propanediol, artemisinic acid, muconic acid, violacein, and naringenin. AIPD achieved the highest experimental success rate: 4 of 5 pathways (80%) produced detectable target compound upon construction in E. coli, with titres ranging from 0.8 to 4.2 g/L after initial optimisation -- compared to 2/5 (40%) for RetroPath2.0 and 1/5 (20%) for manual design at equivalent optimisation effort. The best AIPD pathway (1,3-propanediol) achieved 4.2 g/L in shake flask culture, representing 68% of the theoretical maximum yield. AIPD's thermodynamic pre-filtering eliminated 72% of retrosynthetically valid but thermodynamically infeasible routes, while the enzyme compatibility module predicted functional enzyme-substrate pairs with 84.6% accuracy. These results establish AI-integrated pathway design as a transformative approach for metabolic engineering