Carlos F. Lopez

The paper develops a multiscale model to study how conformational dynamics regulate the PP1-GADD34-eIF2 complex, showing folding-dependent assembly enhances biochemical function.

The paper introduces a deep learning framework to resolve mechanistic ambiguity in complex signaling networks by learning dynamic signatures from competing models.

The paper develops a theoretical model of the ISR using time-resolved proteomics data to show molecular timers and a translational cliff limit ISR activation.

The paper introduces PyDyNo, a framework to identify dominant reaction paths in biochemical networks using Bayesian parameter optimization, revealing distinct signal execution modes in apoptosis and JAK2/STAT5 networks.

The paper analyzes the structure of SCLC transcription factor networks to identify subtype regulators and transition pathways using graph theory and RNA-seq data.

The paper introduces MC-Boomer, a method using Monte Carlo Tree Search to generate Boolean models consistent with experimental data, revealing novel biological mechanisms.

The paper uses Bayesian multimodel inference to analyze tumor growth mechanisms in SCLC, finding that high lineage plasticity, not rare stem-like populations, drives tumor evolution.

This paper introduces a dynamical low-rank method to solve the chemical master equation for biological reaction networks, enabling efficient simulation of large systems.

Microbench automates metadata capture for reproducibility in computational systems biology by recording execution details and environment information.

This paper introduces an unsupervised logic-based method to infer mechanistic hypotheses of cellular processes from state transitions, recovering dynamics and biochemical species concentration changes.

The paper introduces a programmatic modeling approach for biological systems using Python, enhancing reproducibility and collaboration through software engineering practices.

The paper introduces a Bayesian and machine learning approach to assess predictive uncertainty in mechanistic models of apoptosis using experimental data, showing that non-quantitative data can improve model accuracy when combined.

ACDC automates cell detection and counting in time-lapse microscopy images, achieving high accuracy without large annotated datasets.

The paper demonstrates that probabilistic methods can explore biochemical network execution mechanisms with limited data by analyzing apoptosis signal processing modes.

The paper introduces MAGINE, a framework for analyzing multi-omics time-course data to build mechanistic hypotheses of cellular responses to perturbations.

The paper presents GPU-SSA, a GPU-accelerated implementation of the Stochastic Simulation Algorithm, showing significant speedup over CPU implementations for chemical reaction simulations.

The paper introduces PyViPR, a visualization tool for biochemical network processes, demonstrating its ability to simplify complex models and reveal dynamic behavior in apoptosis.

The paper identifies four SCLC subtypes using a systems approach, with NEv2 being a new subtype, and finds master regulators and destabilizers using BooleaBayes.

The paper investigates how COX-2 processes multiple input signals in a complex network, finding that input variability and correlation affect information transmission and that COX-2 acts as a signal integrator.