Gallaher2018 - Tumor–Immune dynamics in multiple myeloma
The paper describes a model on the key components for tumor–immune dynamics in multiple myeloma. Created by COPASI 4.25 (Build 207) This model is described in the article: Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma Jill Gallaher, Kamila Larripa, Marissa Renardy, Blerta Shtylla, Nessy Tania, Diana White, Karen Wood, Li Zhu, Chaitali Passey, Michael Robbins, Natalie Bezman, Suresh Shelat, Hearn Jay Choo, Helen Moore Journal of Theoretical Biology 458 (2018) 31–46 Abstract: In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma. This model is hosted on BioModels Database and identified by: MODEL1907050001 To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma
- Jill Gallaher, Kamila Larripa, Marissa Renardy, Blerta Shtylla, Nessy Tania, Diana White, Karen Wood, Li Zhu, Chaitali Passey, Michael Robbins, Natalie Bezman, Suresh Shelat, Hearn Jay Cho, Helen Moore
- Journal of Theoretical Biology , 8/ 2018 , Volume 458 , pages: 31-46 , DOI: 10.1016/j.jtbi.2018.08.037
- Helen Moore E-mail address: dr.helen.moore@gmail.com Current affiliation: AstraZeneca, Waltham, MA 02451, USA
- In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.
Submitter of this revision: Jinghao Men
Modellers: Jinghao Men
Metadata information
isDescribedBy (1 statement)
hasTaxon (1 statement)
isVersionOf (1 statement)
hasProperty (2 statements)
Experimental Factor Ontology multiple myeloma
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| Gallaher2018.xml | SBML L2V4 representation of tumor–immune dynamics in multiple myeloma model | 112.99 KB | Preview | Download |
Additional files |
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| Gallaher2018.cps | CPS file of the model in COPASI | 131.92 KB | Preview | Download |
| Gallaher2018.sedml | auto-generated SEDML file | 1.14 KB | Preview | Download |
- Model originally submitted by : Jinghao Men
- Submitted: Jul 9, 2019 4:55:25 PM
- Last Modified: Jul 9, 2019 4:55:25 PM
Revisions
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Version: 6
- Submitted on: Jul 9, 2019 4:55:25 PM
- Submitted by: Jinghao Men
- With comment: Automatically added model identifier BIOMD0000000743
: Variable used inside SBML models
| Species | Initial Concentration/Amount |
|---|---|
| Tc Activated Mature Cytotoxic T-Lymphocyte ; CD8-positive, alpha-beta cytotoxic T cell |
464.0 mmol |
| Tr regulatory T cell ; CD4+ CD25+ Regulatory T Cells |
42.0 mmol |
| M M Protein |
4.0 mmol |
| N Natural Killer Cell ; mature natural killer cell |
227.0 mmol |
| Reactions | Rate | Parameters |
|---|---|---|
| Tc => | compartment*dc*Tc | dc = 0.02 1/d |
| => Tc | compartment*rc*(1-Tc/kc)*Tc | kc = 800.0 1; rc = 0.013 1/d |
| Tr => | compartment*dr*Tr | dr = 0.0757 1/d |
| => M | compartment*sm | sm = 0.001 1/d |
| => N | compartment*sn | sn = 0.03 1/d |
| => N; Tc | compartment*rn*(1-N/kn)*acn*Tc/(bcn+Tc)*N | rn = 0.04 1/d; kn = 450.0 1; bcn = 375.0 1; acn = 1.0 1 |
| M => ; N, Tc, Tr | compartment*M*(anm*N/(bnm+N)+acm*Tc/(bcm+Tc)+acnm*N*Tc/((bnm+N)*(bcm+Tc)))*((1-amm*M/(bmm+M))-arm*Tr/(brm+Tr))*dm | bnm = 150.0 1; amm = 0.5 1; bmm = 3.0 1; brm = 25.0 1; anm = 5.0 1; acnm = 8.0 1; arm = 0.5 1; bcm = 375.0 1; dm = 0.002 1/d; acm = 5.0 1 |
| N => | compartment*dn*N | dn = 0.025 1/d |
| => Tr; M | compartment*rr*(1-Tr/kr)*amr*M/(bmr+M)*Tr | amr = 2.0 1; kr = 80.0 1; bmr = 3.0 1; rr = 0.0831 1/d |
| => Tc; N, M | compartment*rc*(1-Tc/kc)*(amc*M/(bmc+M)+anc*M/(bnc+M))*Tc | bmc = 3.0 1; kc = 800.0 1; anc = 1.0 1; rc = 0.013 1/d; amc = 5.0 1; bnc = 150.0 1 |
(added: 09 Jul 2019, 13:21:10, updated: 09 Jul 2019, 13:21:10)