Computational Biology · Bioinformatics · Machine Learning · Deep Learning
I tell Stories from Omics data Insights and genome-scale metabolic network models. I build computational tools and models at the intersection of microbial biology, multi-omics, and machine learning. PhD in Biotechnology. Research Associate Scientist at the Great Lakes Bioenergy Research Center.
Computational biologist and data scientist with 8+ years of experience applying machine learning, metabolic modeling, and multi-omics analysis to problems in microbial biotechnology and bioproduct development.
My work spans the full stack — from designing NGS experiments and building bioinformatics pipelines, to developing scientific software tools and training predictive models for biological systems.
I am currently completing Andrew Ng's Deep Learning Specialization and developing a lightweight transformer model for structure-function prediction in microbial proteins.
New pieces about computational biology, machine learning/AI. Putting down some rants.
MicroKinetics Analyzer
A full-stack scientific software tool for automated fitting and extraction of microbial growth kinetic parameters from experimental data. Provides an interactive web-based interface for wet-lab scientists, enabling parameter estimation analyses not previously feasible without specialized programming expertise. Developed at the Great Lakes Bioenergy Research Center.
→ View on GitHubMicroStruct Transformer
A lightweight transformer-based model for protein structure-function prediction in extremophilic microbial proteins, drawing on AlphaFold2 and ESM-2 architectures. Built on public datasets from UniProt and NCBI, focused on organisms studied in prior research including Halomonas elongata and Novosphingobium. Designed as a domain-specific, computationally accessible alternative to large-scale models.
→ View on GitHubMetagenomes & MAGs from a Nutrient Removal Plant at Los Angeles and Hamptons Roads Sanitation Districts
Operating biological nutrient removal (BNR) wastewater treatment plants under low dissolved oxygen (DO) conditions can significantly reduce energy costs. This project reports on metagenomes and metagenome-assembled genomes (MAGs) obtained from samples collected at the Pomona and Hamptons Roads Water Reclamation Plant before and after a DO reduction providing a genomic window into how microbial communities adapt to shifting operational conditions.
→ GitHub Repository and Publications2026
Metagenomes and metagenome-assembled genomes from a nutrient removal plant at Los Angeles County Sanitation Districts (LACSD) that transitioned from high to low dissolved oxygen
Microbiology Resource Announcements · Enuh BM, et al.
2025
Pathogenic microbiota disrupts the intact structure of cerebral organoids by altering energy metabolism
Nature · Isik, et al.
2022
Genome Analysis of Halomonas elongata Strain 153B and Insights Into Polyhydroxyalkanoate Synthesis and Adaptive Mechanisms to High Saline Environments
Current Microbiology · Enuh BM, et al.
2024
Genome‐Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments
Biotechnology Journal · Enuh BM, et al.