ArticleBMC systems biology2013
The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Recipient pretransplant inosine monophosphate dehydrogenase activity in nonmyeloablative hematopoietic cell transplantation.Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation · 2014Trial
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- Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis.Frontiers in digital health · 2026Article
- B Cell Differentiation Model for Identifying Predictors of Responses to Rituximab-Mediated B Cell Depletion in Rheumatic Diseases.CPT: pharmacometrics & systems pharmacology · 2026Article
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- A Modular Mathematical Model of the Immune Response for Investigating the Pathogenesis of Infectious Diseases.Viruses · 2025Article
- Optimizing monocyte-derived immune cell cultures: comparing xeno-free and xenogeneic conditions.Frontiers in immunology · 2025Article
- Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development.NPJ systems biology and applications · 2024Article
- In Silico Clinical Trials: Is It Possible?Methods in molecular biology (Clifton, N.J.) · 2024Article
- Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.Microorganisms · 2023Review
- A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions.NPJ systems biology and applications · 2023Article
- Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence.PLoS computational biology · 2023Article
- Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring.Frontiers in pharmacology · 2023Review
- Article
- Building digital twins of the human immune system: toward a roadmap.NPJ digital medicine · 2022Review
- Data-driven learning how oncogenic gene expression locally alters heterocellular networks.Nature communications · 2022Article
- Mathematical Modeling of the Gut-Bone Axis and Implications of Butyrate Treatment on Osteoimmunology.Industrial & engineering chemistry research · 2021Article
- QSPcc reduces bottlenecks in computational model simulations.Communications biology · 2021Article
- Biomarker-Guided Individualization of Antibiotic Therapy.Clinical pharmacology and therapeutics · 2021Review
- A multi-approach and multi-scale platform to model CD4+ T cells responding to infections.PLoS computational biology · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.
resultsA dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.
conclusionsThe final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.
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