Research article

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

  • 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.

    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

    Related Papers:

  • 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.


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    [44] H. T. Banks and K. L. Rehm, Experimental design for vector output systems, CRSC-TR12-11, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, April, 2012; Inverse Probl. Sci. En., 22 (2014), 557–590.
    [45] H. T. Banks and K. L. Rehm, Experimental design for distributed parameter vector systems, CRSC-TR12-17, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, August, 2012; Appl. Math. Lett., 26 (2013), 10–14.
    [46] H. T. Banks, S. Dediu, S. L. Ernstberger, et al., Generalized sensitivities and optimal experimental design, CRSC-TR08-12, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, September, 2008, revised November, 2009; J. Inverse Ill-pose. P., 18 (2010), 25–83.
    [47] B. M. Adams, H. T. Banks, M. Davidian, et al., Model fitting and prediction with HIV treatment interruption data, CRSC TR05-40, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, October, 2005; Bull. Math. Biol., 69 (2007), 563–584.
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