Dataset Information

Panel Data Analysis via Mechanistic Models.

ABSTRACT: Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size. Supplementary materials for this article are available online.

SUBMITTER: Breto C

PROVIDER: S-EPMC7472993 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Panel Data Analysis via Mechanistic Models.

Bretó Carles C Ionides Edward L EL King Aaron A AA

Journal of the American Statistical Association 20190601 531

Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models there ...[more]

PMID: 32905476

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Project description:Allostery pervades macromolecular function and drives cooperative binding of ligands to macromolecules. To decipher the mechanisms of cooperative ligand binding it is necessary to define at a microscopic level the structural and thermodynamic consequences of binding of each ligand to its allosterically coupled site(s). However, dynamic sampling of alternative conformations (microstates) in allosteric molecules complicates interpretation of both structural and thermodynamic data. Isothermal titration calorimetry has the potential to directly quantify the thermodynamics of allosteric interactions, but usually falls short of enabling mechanistic insight. This is because 1) its measurements reflect the sum of overlapping caloric processes involving binding-linked population shifts within and between microstates, and 2) data are generally fit with phenomenological binding polynomials that are underdetermined. Nevertheless, temperature-dependent binding data have the potential to resolve overlapping thermodynamic processes, while mechanistically constrained models enable hypothesis testing and identification of informative parameters. We globally fit temperature-dependent isothermal titration calorimetry data for binding of 11 tryptophan ligands to the homo-undecameric trp RNA-binding Attenuation Protein from Bacillus stearothermophilus using nearest-neighbor statistical thermodynamic models. This approach allowed us to distinguish alternative nearest-neighbor interaction models, and quantifies the thermodynamic contribution of neighboring ligands to individual binding sites. We also perform conventional Hill equation modeling and illustrate how comparatively limited it is in quantitative or mechanistic value. This work illustrates the potential of mechanistically constrained global fitting of binding data to yield the microscopic thermodynamic parameters essential for deciphering mechanisms of cooperativity in a wide range of ligand-regulated homo-oligomeric assemblies.

Dataset Information

Panel Data Analysis via Mechanistic Models.

Publications

Panel Data Analysis via Mechanistic Models.

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