Alan R. Pacheco

Dimalis automates the analysis of bacterial cell growth from time-lapse imaging, providing standardized outputs for downstream processing.

This paper introduces an evolutionary algorithm combining machine learning and dynamic flux balance analysis to design microbial communities by optimizing environmental conditions for target phenotypes.

This paper develops a Python tool for studying string chemistries and introduces a network-pruning algorithm to generate minimal metabolic networks.

The paper uses genome-scale metabolic models to show that costless metabolite exchange can drive stable microbial interactions and synthetic consortia.