Biology subjects

Hernandez Vargas, E. A.

Publications and source records attributed to Hernandez Vargas, E. A..

8 recordsLinked to original sources

bioRxiv2026

Linking Infection, Immunity, and Symptoms for Age-Dependent Influenza Severity

Influenza infection results from tightly coupled interactions between viral replication, host immune responses, and the emergence of clinical symptoms. While mathematical models have extensively characterized viral and immune dynamics, the mechanistic link between immune activity and disease severity remains poorly understood. Here, we develop an integrative, within-host modeling framework that explicitly connects infection dynamics, immune responses, and symptom manifestation through a unified dynamical system. Using murine influenza data, we incorporate key immune components alongside a mechanistic representation of symptom progression, quantified via host weight loss.Our analysis identifies inflammatory signaling, particularly TNF- mediated pathways, as a central driver linking immune activity to symptom severity. Importantly, we demonstrate that age-dependent alterations in immune regulation reshape this coupling: aged hosts exhibit prolonged inflammatory responses that amplify and sustain symptom burden despite comparable viral kinetics. These results highlight that disease severity cannot be inferred from viral load alone, but instead emerges from the dynamical interplay between immune regulation and host physiology. This framework provides a quantitative basis for understanding age-specific morbidity and offers a foundation for designing interventions that target immune-mediated pathology rather than viral replication alone.

systems biology↗

bioRxiv2026

Triangular Invariant Sets for Containment of Drug Resistance Under Evolutionary Therapy

Evolutionary therapies regulate heterogeneous populations by altering selective pressures through treatment sequences in cancer and infections. This letter develops an invariant-set framework for treatment-induced containment based on positive triangular invariant sets. For periodically switched systems, sufficient conditions are derived for the existence of such invariant regions. Robustness with respect to mutation is established by showing that the invariant simplex persists under small perturbations of the subsystem matrices. In the two-phenotype case, the analysis yields an explicit mutation threshold that separates regimes in which therapy cycling maintains containment from regimes in which mutation can enable evolutionary escape. Simulations illustrate the geometry of the invariant sets and the role of mutation and dwell time in containment robustness.

evolutionary biology↗

bioRxiv2025

Computational Platform for streamlining the success of sequential antibiotic therapy

The scarcity of antibiotics and the need for swift decision-making present significant challenges for healthcare practitioners. When confronted with such circumstances, practitioners must prioritize their approach based on several key factors. By leveraging the recent biological discovery of collateral sensitivity, we have devised an open-source computational platform. This platform utilizes the drug resistance profile of an evolved strain and initial population conditions to potentially predict the failure of cycling therapy in eradicating or restraining a multi-drug resistant bacterial population. We demonstrate how this framework can anticipate potential failures for a range of antibiotics in chronic pseudomonas aeruginosa infections. This innovative methodology lays the foundation for evolutionary therapies that can facilitate the selection of appropriate treatments, thereby reducing antibiotic resistance.

evolutionary biology↗

bioRxiv2022

Tracking influenza a virus infection in the lung from hematological data with machine learning

The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-{gamma} and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.

microbiology↗

bioRxiv2021

Untangling the cell immune response dynamic for severe and critical cases of SARS-CoV-2 infection

COVID-19 is a global pandemic leading high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe and critical cases. In particular, studies have highlighted the relationship between the lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical case. To this end, several mathematical models are proposed to represent the dynamic of the immune response in patients with SARS-CoV-2 infection. The best model had a good fit to reported experimental data, and in accordance with values found in the literature. Our results suggest that a rapid proliferation of CD8+ T cells is decisive in the severity of the disease.

biophysics↗

bioRxiv2020

Uncovering antibody cross-reaction dynamics in influenza A infections

Uncovering the hidden pathways of how antibodies induced by one influenza strain is effective against another, cross-reaction, is the central dogma for the design of universal flu vaccines. Here, we conceive a stochastic model that successfully represents the antibody cross-reactive data from mice infected with H3N2 influenza strains and further validation with cross-reaction data of H1N1 strains. After modifying several aspects and parameters in the model, our computational simulations highlight that changes in time of infection and the B-cells population are relevant, however, the affinity threshold of B-cells between consecutive infections is a necessary condition for the successful Abs cross-reaction. Our results suggest a reformulation in 3-D of the current antibody influenza landscape.

systems biology↗

bioRxiv2017

Parameter Estimation In Mathematical Models Of Viral Infections Using R

In recent years, mathematical modeling approaches have played a central role to understand and to quantify mechanisms in different viral infectious diseases. In this approach, biological-based hypotheses are expressed via mathematical relations and then tested based on empirical data. The simulation results can be used to either identify underlying mechanisms, provide predictions on infection outcomes, or evaluate the efficacy of a treatment.\n\nConducting parameter estimation for mathematical models is not an easy task. Here we detail an approach to conduct parameter estimation and to evaluate the results using the free software R. The method is applicable to influenza virus dynamics at different complexity levels, widening experimentalists capabilities in understanding their data. The parameter estimation approach presented here can be also applied to other viral infections or biological applications.

bioinformatics↗

bioRxiv2017

Windows of opportunity for Ebola virus infection treatment and vaccination

Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV infected patients within 7 days. Comprehensive data on EBOV infection are very fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (VSV-EBOV), we found that the time window can be subject-specific, but vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for developing therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.

systems biology↗