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

doi:10.1016/j.jtbi.2018.08.037

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

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

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 certain 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 analysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.

Original language	English
Pages (from-to)	31-46
Number of pages	16
Journal	Journal of Theoretical Biology
Volume	458
DOIs	https://doi.org/10.1016/j.jtbi.2018.08.037
State	Published - 7 Dec 2018

Keywords

Disease modeling
Identifiability
Latin hypercube sampling
Partial rank correlation coefficient
Sensitivity analysis

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Gallaher, J., Larripa, K., Renardy, M., Shtylla, B., Tania, N., White, D., Wood, K., Zhu, L., Passey, C., Robbins, M., Bezman, N., Shelat, S., Jay Cho, H., & Moore, H. (2018). Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma. Journal of Theoretical Biology, 458, 31-46. https://doi.org/10.1016/j.jtbi.2018.08.037

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title = "Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma",

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 certain 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 analysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.",

keywords = "Disease modeling, Identifiability, Latin hypercube sampling, Partial rank correlation coefficient, Sensitivity analysis",

author = "Jill Gallaher and Kamila Larripa and Marissa Renardy and Blerta Shtylla and Nessy Tania and Diana White and Karen Wood and Li Zhu and Chaitali Passey and Michael Robbins and Natalie Bezman and Suresh Shelat and {Jay Cho}, Hearn and Helen Moore",

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AU - Gallaher, Jill

AU - Larripa, Kamila

AU - Renardy, Marissa

AU - Shtylla, Blerta

AU - Tania, Nessy

AU - White, Diana

AU - Wood, Karen

AU - Zhu, Li

AU - Passey, Chaitali

AU - Robbins, Michael

AU - Bezman, Natalie

AU - Shelat, Suresh

AU - Jay Cho, Hearn

AU - Moore, Helen

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AB - 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 certain 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 analysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.

KW - Disease modeling

KW - Identifiability

KW - Latin hypercube sampling

KW - Partial rank correlation coefficient

KW - Sensitivity analysis

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JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

ER -

Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma

Abstract

Keywords

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