Estimation of probability distributions of parameters using aggregate population data: analysis of a CAR T-cell cancer model

Celia Schacht; Annabel Meade; H.T. Banks; Heiko Enderling; Daniel Abate-Daga; Celia Schacht; Annabel Meade; H.T. Banks; Heiko Enderling; Daniel Abate-Daga

doi:10.3934/mbe.2019365

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 7299-7326. doi: 10.3934/mbe.2019365

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Research article

Estimation of probability distributions of parameters using aggregate population data: analysis of a CAR T-cell cancer model

1.
Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695, USA
2.
H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA

Received: 28 March 2019 Accepted: 01 August 2019 Published: 09 August 2019

In this effort we explain fundamental formulations for aggregate data inverse problems requiring estimation of probability distribution parameters. We use as a motivating example a class of CAR T-call cancer models in mice. After ascertaining results on model stability and sensitivity with respect to parameters, we carry out first elementary computations on the question how much data is needed for successful estimation of probability distributions.
- aggregate data,
- CAR T-cell therapy,
- cancer model,
- inverse problems,
- design of experiments
Citation: Celia Schacht, Annabel Meade, H.T. Banks, Heiko Enderling, Daniel Abate-Daga. Estimation of probability distributions of parameters using aggregate population data: analysis of a CAR T-cell cancer model[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 7299-7326. doi: 10.3934/mbe.2019365

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Abstract

In this effort we explain fundamental formulations for aggregate data inverse problems requiring estimation of probability distribution parameters. We use as a motivating example a class of CAR T-call cancer models in mice. After ascertaining results on model stability and sensitivity with respect to parameters, we carry out first elementary computations on the question how much data is needed for successful estimation of probability distributions.

References

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