Ví­ctor M. Zavala

Scalable Extraction of Information on Protein-Protein Interactions using Topological Data Analysis

This paper introduces a scalable topological data analysis framework for predicting protein-protein interaction interfaces, achieving comparable performance to a geometric deep learning method with reduced computational cost.

Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics

The paper introduces a physics-constrained neural ordinary differential equation model to predict microbial community dynamics and interactions, outperforming existing mechanistic and machine learning approaches.

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The paper develops a high-throughput optogenetic platform combined with machine learning to enable multiplexed control of light-sensitive transcription factors in yeast.

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

The paper develops a physically constrained recurrent neural network and Bayesian experimental design framework to optimize microbial community functions by efficiently navigating complex design spaces.

Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models

The paper introduces a scalable nonlinear programming framework for parameter estimation in dynamic biological models, demonstrating its efficiency on large-scale synthetic microbiome data.