Inverse problems in population balances: Growth and nucleation from dynamic data

Mahoney, Alan W.; Doyle, Francis J.; Ramkrishna, Doraiswami

doi:10.1002/aic.690480508

Cited by 35 publications

(22 citation statements)

References 20 publications

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“…(Mahoney et al, 2002; Muralidar and Ramkrishna, 1986; Muralidar and Ramkrishna, 1989; Patruno et al, 2008; Patruno et al, 2009; Sathyagal et al, 1995; Wright and Ramkrishna, 1992). While these studies can provide invaluable guidance, their methods cannot be directly applied to address the challenges of inverse problems resulting from cell population balance models.…”

Section: Solving the Inverse Problemmentioning

confidence: 99%

Single-cell behavior and population heterogeneity: Solving an inverse problem to compute the intrinsic physiological state functions

Spetsieris

Zygourakis

2012

Journal of Biotechnology

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The dynamics of isogenic cell populations can be described by cell population balance models that account for phenotypic heterogeneity. To utilize the predictive power of these models, however, we must know the rates of single-cell reaction and division and the bivariate partition probability density function. These three intrinsic physiological state (IPS) functions can be obtained by solving an inverse problem that requires knowledge of the phenotypic distributions for the overall cell population, the dividing cell subpopulation and the newborn cell subpopulation. We present here a robust computational procedure that can accurately estimate the IPS functions for heterogeneous cell populations. A detailed parametric analysis shows how the accuracy of the inverse solution is affected by discretization parameters, the type of non-parametric estimators used, the qualitative characteristics of phenotypic distributions and the unknown partitioning probability density function. The effect of finite sampling and measurement errors on the accuracy of the recovered IPS functions is also assessed. Finally, we apply the procedure to estimate the IPS functions of an E. coli population carrying an IPTG-inducible genetic toggle network. This study completes the development of an integrated experimental and computational framework that can become a powerful tool for quantifying single-cell behavior using measurements from heterogeneous cell populations.

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Section: Solving the Inverse Problemmentioning

confidence: 99%

Single-cell behavior and population heterogeneity: Solving an inverse problem to compute the intrinsic physiological state functions

Spetsieris

Zygourakis

2012

Journal of Biotechnology

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“…It has found application for the identification of both aggregation kinetics as well as growth/nucleation kinetics. Mahoney et al (2002) developed a strategy for inverse problem solution to identify the growth and nucleation kinetics under aggregation/breakage-free conditions. Under nucleation and growth conditions, the solutions can be characterised employing the method of characteristics, with each solution emerging from either the initial condition or the boundary condition.…”

Section: Inverse Problems In Population Balancesmentioning

confidence: 99%

Process systems modelling and applications in granulation: A review

Cameron

Wang

Immanuel

et al. 2005

Chemical Engineering Science

152

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“…This new approach is strongly related to the di erential method if applied to reaction kinetics. The computational steps for the example considered below are even identical but the incremental framework gives further insight into the model structure and provides a unifying framework for other stepwise identiÿcation methods such as that of Mahoney et al (2002).…”

Section: Introductionmentioning

confidence: 98%

Incremental and simultaneous identification of reaction kinetics: methods and comparison

Bardow

Marquardt

2004

Chemical Engineering Science

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Inverse problems in population balances: Growth and nucleation from dynamic data

Cited by 35 publications

References 20 publications

Single-cell behavior and population heterogeneity: Solving an inverse problem to compute the intrinsic physiological state functions

Single-cell behavior and population heterogeneity: Solving an inverse problem to compute the intrinsic physiological state functions

Process systems modelling and applications in granulation: A review

Incremental and simultaneous identification of reaction kinetics: methods and comparison

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