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Aaron D'Souza

Publications and source records attributed to Aaron D'Souza.

2 recordsLinked to original sources
arXiv2026

An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Single-Cell Protein Production in Saccharomyces cerevisiae

Saccharomyces cerevisiae is increasingly recognised as a key source for single-cell protein (SCP) production, a rising solution to global protein-supply challenges. This study presents a computational framework combining the Yeast9 genome-scale metabolic model (GEM) with machine learning and optimisation to predict and enhance biomass flux for SCP yield. The Yeast9 GEM, comprising 4,131 reactions, 2,806 metabolites, and 1,161 genes, was simulated using flux balance analysis (FBA) across 2,000 Latin Hypercube-sampled flux profiles. Random Forest and XGBoost regressors achieved R2 values of 0.9999760 and 0.9997702, respectively. A variational autoencoder (VAE) identified four metabolic clusters with mean biomass fluxes of 0.472, 0.493, 0.527, and 0.505 gDW/hr. SHAP-based feature attribution identified twenty key reactions in glycolysis, the TCA cycle, and amino-acid biosynthesis; 18/20 (90%) were confirmed essential by in silico knockout. Bayesian optimisation produced a 12.13-fold improvement in biomass flux (0.0858 to 1.041 gDW/hr) at glucose = -20.0, oxygen = -20.0, and ammonium = -8.9 mmol/gDW/hr. A generative adversarial network (GAN) generated novel flux configurations (variance = 0.124); stoichiometric feasibility verification returned 0/100 feasible profiles due to incomplete generator convergence, reported as a limitation. Pareto front analysis identified an optimal SCP operating point at 0.0858 gDW/hr biomass flux with amino-acid biosynthesis score of 1000.029 mmol/gDW/hr.

cs.LG↗
arXiv2026

Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems

Efficient materials discovery requires reducing costly first-principles calculations for training machine-learned interatomic potentials (MLIPs). We develop an active learning (AL) framework that iteratively selects informative structures from the Materials Project and Open Quantum Materials Database (OQMD) using compositional and property-based descriptors with a neural network ensemble model. Query-by-Committee enables real-time uncertainty quantification. We compare four strategies: random sampling (baseline), uncertainty-based sampling, diversity-based sampling (k-means clustering with farthest-point refinement), and a hybrid approach. Experiments across four material systems (C, Si, Fe, and TiO2) with 5 random seeds demonstrate that diversity sampling achieves competitive or superior performance, with 10.9% improvement on TiO2. Our approach achieves equivalent accuracy with 5-13% fewer labeled samples than random baselines. The complete pipeline executes on Google Colab in under 4 hours per system using less than 8 GB RAM, democratizing MLIP development for resource-limited researchers. Open-source code and configurations are available on GitHub. This multi-system evaluation provides practical guidelines for data-efficient MLIP training and highlights integration with symmetry-aware architectures as a promising future direction.

cs.LG↗