{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608_url.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=manifest.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.sci","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=firm_ode.zip","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608_url.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000608?filename=BIOMD0000000608.png"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Vijayalakshmi Chelliah"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"disease":["Tuberculosis"],"levelVersion":["L3V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000608"],"publication_pubmed":["24074340"],"isPrivate":["false"],"repository":["BioModels"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Palsson2013 Fully integrated immune response model (FIRM)"],"publication_year":["2013"],"submissionId":["MODEL1603310000"],"first_author":["Sirus Palsson"],"publication_authors":["Sirus Palsson, Timothy P Hickling, Erica L Bradshaw-Pierce, Michael Zager, Karin Jooss, Peter J O'Brien, Mary E Spilker, Bernhard O Palsson, Paolo Vicini"],"publication":["24074340,\n Background
The 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.Results
A 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.Conclusions
The 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.. null, 7.\n Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA, USA. Paolo.Vicini@pfizer.com."],"submitter_mail":["viji@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000608"],"pubmed_abstract":["Background
The 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.Results
A 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.Conclusions
The 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.","In this paper we present a model of the macrophage T lymphocyte interactions that generate an anti-tumor immune response. The model specifies i) induction of cytotoxic T lymphocytes, ii) antigen presentation by macrophages, which leads to iii) activation of helper T cells, and iv) production of lymphoid factors, which induce a) cytotoxic macrophages, b) T lymphocyte proliferation, and c) an inflammation reaction. Tumor escape mechanisms (suppression, antigenic heterogeneity) have been deliberately omitted from the model. This research combines hitherto unrelated or even contradictory data within the range of behavior of one model. In the model behavior, helper T cells play a crucial role: Tumors that differ minimally in antigenicity (i.e., helper reactivity) can differ markedly in rejectability. Immunization yields protection against tumor doses that would otherwise be lethal, because it increases the number of helper T cells. The magnitude of the cytotoxic effector cell response depends on the time at which helper T cells become activated: early helper activity steeply increases the magnitude of the immune response. The type of cytotoxic effector cells that eradicates the tumor depends on tumor antigenicity: lowly antigenic tumors are attacked mainly by macrophages, whereas large highly antigenic tumors can be eradicated by cytotoxic T lymphocytes only.","A key issue for the study of tuberculosis is to understand why individuals infected with Mycobacterium tuberculosis (Mtb) experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we have previously developed a temporal mathematical model that qualitatively and quantitatively characterizes the cellular and cytokine control network during infection. In this work we extend that model to a two compartmental model to capture the important processes of cellular activation and priming that occur between the lung and the nearest draining lymph node. We are able to reproduce typical disease progression scenarios including primary infection, latency or clearance. Then we use the model to predict key processes determining these different disease trajectories (i.e. identify bifurcation parameters), suggesting directions for further basic science study and potential new treatment strategies."],"pubmed_title":["The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.","The human immune response to Mycobacterium tuberculosis in lung and lymph node.","Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model.","Mathematical model of clonal selection and antibody production."],"pubmed_authors":["De Boer R J RJ, Hogeweg P P, Dullens H F HF, De Weger R A RA, Den Otter W W","Marino Simeone S, Kirschner Denise E DE","Palsson Sirus S, Hickling Timothy P TP, Bradshaw-Pierce Erica L EL, Zager Michael M, Jooss Karin K, O'Brien Peter J PJ, Spilker Mary E ME, Palsson Bernhard O BO, Vicini Paolo P","Bell G I GI"],"additional_accession":[]},"is_claimable":false,"name":"Palsson2013 - Fully-integrated immune response model (FIRM)","description":"\n \n Palsson2013 - Fully-integration immune response model (FIRM)\n \n \n FIRM (The Fully-integrated Immune Response Modeling) is a hybrid construct incorporating multiple existing models of the immune system [De Boer et al., (1985);Bell, (1970); Marino and Kirschner, (2004) ]. FIRM used a pharmacokinetic / pharmacodynamic modelling approach to combine previously published individual models of humoral and cellular response with antigen exposure. This integrated model has a potential to simulate a range of responses under a variety of conditions, for example, the immune response against tuberculosis infection, blood borne pathogen infection, Spontaneous tumour rejection and influence of regulatory T cells (Treg) on tumour rejection. \n The SBML model provided here was generated from the matlab code (provided by the authors). The matlab to SBML conversion was done using MOCCASIN version 1.1.0. This model describes the immune response against tuberculosis (TB) infection and reproduces figure 7 of the reference publication.\n \n Note:The following minor edit to the original matlab code was done during the conversion to SBML: The model had two parameters named k3 and K3. To avoid case-insensitive issues during the conversion, K3 was changed to K3s in the original matlap code before using the conversion software. The matlab code of the model provided by the authors (with the above change) can be obtained from the curation tab.\n \n \n \n This model is described in the article:\n \n The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.\n \n Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, O'Brien PJ, Spilker ME, Palsson BO, Vicini P.\n BMC Syst Biol. 2013 Sep 28;7:95.\n Abstract:\n \n \n BACKGROUND:\n The 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.\n RESULTS:\n\n A 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.\n CONCLUSIONS:\n\n The 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.\n \n \n \n \n This model is hosted on \n BioModels Database\n and identified by: \n MODEL1603310000.\n To cite BioModels Database, please use: \n BioModels:\n Content, Features, Functionality and Use.\n \n\n To the extent possible under law, all copyright and related or\n neighbouring rights to this encoded model have been dedicated to\n the public domain worldwide. Please refer to \n CC0\n Public Domain Dedication for more information.\n\n\n","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2016-03-31"},"accession":"BIOMD0000000608","cross_references":{"pubmed":["24074340","5500468","3156189","15038983"],"biomodels__db":["MODEL1603310000","BIOMD0000000608"],"go":["GO:0006955"],"taxonomy":["40674"]}}