Josep Sardanyés A

The paper develops a mathematical model linking viral fitness components to persistence dynamics, showing how replicative and dispersal fitness influence infection outcomes.

This study develops a computational framework to model genomic instability in tumors, showing it can both drive evolution and lead to population collapse, with implications for therapy.

This paper models oncolytic virotherapy with and without a prodrug-activating transgene, showing that asymmetric drug effects can drive oscillations in cancer cell populations.

The paper analyzes a mathematical model of phage-bacteria interactions to show coexistence and clearance dynamics governed by quasineutral equilibria and bifurcations.

This paper develops a mathematical framework to predict epigenetic tipping points under chromatin modifier competition by analyzing metabolic cofactor sensitivity and early warning signals.

The paper studies transient dynamics near multiple bifurcations in gene regulatory networks, showing synergistic effects and noise-driven control of transition timing.

This paper develops a computational model to predict effective combinations of epigenetic drugs for cancer treatment by analyzing bivalent chromatin states and their impact on cell heterogeneity.

The paper analyzes viral replication modes using a dynamical systems approach to determine critical mutation rates for lethal mutagenesis and error catastrophe in different replication strategies.

The paper develops mathematical models for four synthetic Terraformation motifs to understand their stability and effectiveness in preventing ecosystem collapse.

The paper studies noise-induced bistability in viral RNA replication modes using stochastic simulations and diffusion approximation, showing noise significantly impacts amplification and leads to bistability.

The paper analyzes an HIV-1 model with cancer cells, showing chaotic dynamics and using optimal homotopy analysis to improve solution efficiency.