Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.08.19.608570

A modular model of immune response as a computational platform to investigate a pathogenesis of infection disease

Abstract

The COVID-19 pandemic significantly transformed the field of mathematical modeling in immunology. International collaboration among numerous research groups yielded a substantial amount of experimental data, which greatly facilitated model validation and led to the development of new mathematical models. The aim of the study is an improvement of system understanding of the immune response to SARS-CoV-2 infection based on the development of a modular mathematical model which provides a foundation for further research on host-pathogen interactions. We utilized the open-source BioUML platform to develop a model using ordinary, delay and stochastic differential equations. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load, antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were conducted to refine the models accuracy. The model effectively reproduces moderate, severe, and critical COVID-19 progressions, consistent with experimental observations. We investigated the efficiency and contributions of innate and adaptive immunity in response to SARS-CoV-2 infection and assessed immune system behavior during co-infection with HIV and organ transplantation. Additionally, we studied therapy methods, such as interferon administration. The developed model can be employed as a framework for simulating other infectious diseases taking into account follow-up immune response. Author summaryDespite the significant progress reached in understanding of COVID-19, traditional methods still struggle to analyze and interpret the extensive and sometimes controversial experimental data on SARS-CoV-2 infection. Mathematical and systems biology approaches attempt to address this challenge by developing mathematical models of the immune response. We aimed not only to investigate the disease at a systemic level but also to provide a framework for further research on host-pathogen interactions, both existing and forthcoming. To achieve this, we constructed a model incorporating both innate and adaptive immunity, as well as cellular and humoral components. This together allowed us to conduct a series of in silico experiments, exploring the immune response across various levels and compartments. The results of these investigations offer valuable insights into the complex dynamics of the immune system and can guide future research and therapeutic strategies.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Miroshnichenko, M., Kolpakov, F. A., Akberdin, I. R.. 2024-08-20. A modular model of immune response as a computational platform to investigate a pathogenesis of infection disease. https://doi.org/10.1101/2024.08.19.608570

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues

H2O2 released to the extracellular space (ECS) regulates diverse physiological processes, yet its concentrations and spatial distribution in tissues remain poorly defined. This uncertainty hampers mechanistic understanding of redox signaling. Here, we used reaction-diffusion modeling to estimate extracellular H2O2 concentrations and transport ranges in various scenarios. Idealized analytical models were combined with numerical models incorporating localized NADPH oxidase (NOX) clusters, ECS microstructure, membrane permeability, and the thioredoxin- and GSH-dependent clearance systems. Using maximal neutrophil and NOX superoxide/H2O2 release rates, we obtained upper bounds for extracellular H2O2. Adjacent to isolated average-sized, fully active NOX2 clusters H2O2 peaked at ~540 nM at adhesion cell-cell separations, and decreased radially over ~50-100 nm. At the receptor cell surface, peak concentration decreased inversely with intercellular separation, to <5 nM at 1 m separation. Radial decrease here, for this wide separation, was over ~2.5 m. Even the former maximal extracellular concentrations induce just a minimal, highly localized oxidation of the intracellular Prdx, Trx and GSH pools. In turn, maximally activated neutrophils carry ~2000 such NOX2 clusters, inducing 10s of M peak ECS H2O2 concentrations. These cause extensive Prdx and Trx oxidation near the exposed membranes. However, the GSH-dependent system still sustains a strong transmembrane gradient if the permeation barrier remains intact, and ECS H2O2 concentrations decay to sub-M within a few m of the source cell. Extracellular H2O2 concentrations scaled linearly with source flux in all the examined conditions. These results establish stringent constraints on autocrine, juxtacrine and next-cell paracrine H2O2 signaling.

systems biology↗

LSD-pipeline: Causal Inference of miRNA Network Effects in Alzheimer's Disease

MicroRNAs (miRNAs) are implicated in Alzheimer's disease (AD), but research has focused on individual miRNAs and direct targets. Existing approaches to miRNA regulation in AD identify associations rather than causal effects, and few methods estimate multi-stage chains from miRNAs through target genes to target transcription factor (TF) cascades. We developed the LSD pipeline (LASSO-SEM-DoWhy), integrating LASSO feature selection, multi-stage structural equation modeling, and DoWhy causal inference to identify and validate miRNA causal pathways in AD. Applying LSD to six blood miRNA and brain mRNA datasets, we identified four LSD-validated miRNAs (miR-30d-5p, miR-92a-3p, miR-296-5p, miR-193a-5p) as AD biomarkers, achieving >86% ROC accuracy in an independent validation cohort. Several miRNAs with no significant direct association with AD showed significant effects when estimated through their target networks, while others significant in direct analysis were not supported at the network level, underscoring the value of network-level analysis. Extending to the TF layer revealed complete miRNA [->] targets [->] TF cascades [->] AD causal chains, with HMGA1, NKX2-3, and PRRX2 as key intermediaries. Confirmed classic pathways converge primarily on tau pathology and synaptic dysfunction. miRNA effects were largely age-independent, suggesting miRNAs act as early initiators of AD pathogenesis. Beyond AD, the LSD pipeline provides a generalizable framework for uncovering causal regulatory mechanisms in other diseases.

systems biology↗

PyKappa: Rule-based modeling in Python

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

systems biology↗