Developing BayFlux-v2.0: Advanced Genome-Scale Metabolic Modeling Software

• Designed and developed BayFlux-v2.0, a high-performance software platform that uses Bayesian inference to model genome-scale metabolic networks, with applications in biofuel optimization and industrial biotechnology.

• Enhanced BayFlux to accurately analyze metabolic flux distributions, improving predictions and pathway optimization across diverse biological systems.

• Extended BayFlux to support high-throughput simulations for species such as Pseudomonas putida, enabling cross-species flux analysis for industrial applications.

Algorithm Optimization: Multiple-Proposal Markov Chain Monte Carlo (MP-MCMC)

• Implemented two novel Multiple-Proposal Markov Chain Monte Carlo (MP-MCMC) algorithms to parallelize the sampling process in BayFlux, significantly improving computational efficiency.

• Achieved:

  • 50× speedup via sparse matrix optimization

  • 100× speedup using PyTorch-based CPU acceleration

• Currently optimizing BayFlux for GPU-based execution using CUDA and PyTorch to further scale performance on heterogeneous compute platforms.

• Integrated advanced sampling techniques to improve convergence speed, accuracy, and robustness of the Bayesian inference process.

Docker Containerization & HPC Deployment

• Created a Docker-based deployment pipeline from scratch to streamline setup and ensure reproducibility across environments.

• Optimized the container for deployment on high-performance computing (HPC) clusters like NERSC, using Shifter to enable large-scale metabolic simulations with minimal overhead.

• Simplified usability by packaging a pre-configured environment, reducing user setup time and compatibility issues.

Scalable Architecture: Parallel Computing on CPU & GPU Clusters

• Optimized BayFlux for distributed computing architectures, supporting both multi-core AMD CPUs and NVIDIA GPUs, paving the way for scalable, high-throughput metabolic modeling across HPC systems.

Expanding BayFlux to New Species: Toward Automation

• Extended BayFlux’s capabilities to support non-model organisms, with successful implementation in Pseudomonas putida.

• Ongoing work includes automating the genome-scale atom transition mapping process, currently a manual bottleneck, to enable high-throughput simulation pipelines across new biological species.

Our Github Repo: BayFlux