Metabolic Pathway
Design with AI
Optimize metabolic pathways for chemical production. Model enzyme kinetics, predict flux distributions, and design efficient biosynthetic routes with AI foundation models.
The Challenge of Pathway Optimization
Metabolic pathway design is the foundation of microbial biomanufacturing. Whether producing pharmaceuticals, biofuels, or specialty chemicals, the efficiency of the biosynthetic route determines commercial viability. Yet designing optimal pathways remains a formidable challenge because cellular metabolism operates as a tightly coupled network where changes to one reaction ripple through hundreds of others.
Traditional flux balance analysis provides steady-state predictions but cannot capture the dynamic behavior of engineered pathways, including enzyme saturation, regulatory feedback, and metabolite toxicity that often derail promising designs in practice. Kinetic models offer more realism but require extensive parameterization that is rarely available for heterologous enzymes or non-model organisms.
The combinatorial challenge is immense: selecting which enzymes to use from which organisms, determining optimal expression levels for each pathway step, balancing cofactor regeneration, and avoiding toxic intermediate accumulation. This design space grows exponentially with pathway length, making systematic exploration impossible without computational guidance.
How Avitai Optimizes Pathway Design
Our models integrate kinetic modeling, thermodynamic constraints, and literature knowledge to design pathways that work in practice.
Route Enumeration
Systematically identify all feasible biosynthetic routes to a target compound, ranking them by thermodynamic favorability, pathway length, and enzyme availability.
Dynamic Flux Prediction
Go beyond steady-state models with physics-informed neural networks that predict time-dependent metabolic flux distributions under realistic growth conditions.
Bottleneck Identification
Pinpoint rate-limiting steps, cofactor imbalances, and toxic intermediate accumulation points before building the pathway, saving months of troubleshooting.
Expression Balancing
Optimize the expression level of each pathway enzyme to maximize flux to product while minimizing metabolic burden on the host cell.
Foundation Models for Pathway Engineering
Three foundation models combine to deliver end-to-end pathway design and optimization capabilities.
Research Model
Searches metabolic databases and literature to identify characterized enzymes, known pathway variants, and validated engineering strategies for target compounds. Provides knowledge-guided enzyme selection and pathway architecture recommendations.
Learn moreDynamics Model
Simulates metabolic dynamics with physics-informed constraints, predicting how pathway flux changes over time under varying growth conditions. Captures enzyme kinetics, allosteric regulation, and thermodynamic feasibility that static models miss.
Learn morePerturbation Model
Optimizes pathway configurations by exploring combinations of enzyme variants, expression levels, and regulatory elements. Identifies minimal interventions that redirect metabolic flux toward the target product while maintaining cell viability.
Learn moreOpen-Source Tools for Pathway Design
Build custom metabolic engineering workflows with our open-source JAX ecosystem.
Physics-informed neural networks for modeling enzyme kinetics and metabolic dynamics
Differentiable bioinformatics for gradient-based pathway optimization
Data pipelines for integrating metabolomics, fluxomics, and transcriptomics data
Benchmarking framework for evaluating pathway prediction accuracy
Design Optimal Metabolic Pathways
Let AI guide your pathway engineering decisions from route selection through expression optimization.