Parameter estimation and uncertainty quantification for systems biology models

Mitra, Eshan D.; Hlavacek, William S.

doi:10.1016/j.coisb.2019.10.006

Cited by 55 publications

(42 citation statements)

References 94 publications

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“…Model calibration is the process of adjusting model parameters to match experimental data, also known as parameter estimation/optimization when applied to parametric models. The most common form of model calibration involves a process of running many simulations (thousands to millions or more) and checking the distance between model and experimental data error using an objective function, which gives a measure of the model’s simulation “error” versus experiment; for a review see [39]. Since dynamic data for signaling models are hard to come by, the modeler often only has data for a few species, and thus model calibration often leaves a model underdetermined - multiple parameter sets fit the data equally well [40].…”

Section: Model Calibrationmentioning

confidence: 99%

Programmatic modeling for biological systems

Lubbock

López

2021

Preprint

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Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain- specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.

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Section: Model Calibrationmentioning

confidence: 99%

Programmatic modeling for biological systems

Lubbock

López

2021

Preprint

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“…Parameter estimation. The model parameters were estimated using a Bayesian framework; namely, the Metropolis-Hastings (MH) algorithm [52][53][54][55] , as we did previously 19 . We provide an extensive description of this approach in the published work.…”

Section: Construction Of the Nk Cell Degranulation Modelmentioning

confidence: 99%

An Optimal Control Approach for Enhancing Natural Killer Cells’ Secretion of Cytolytic Molecules

Makaryan

Finley

2020

Preprint

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Natural killer (NK) cells are immune effector cells that can detect and lyse cancer cells. However, NK cell exhaustion, a phenotype characterized by reduced secretion of cytolytic models upon serial stimulation, limits the NK cell’s ability to lyse cells. In this work, we investigated in silico strategies that counteract the NK cell’s reduced secretion of cytolytic molecules. To accomplish this goal, we constructed a mathematical model that describes the dynamics of the cytolytic molecules granzyme B (GZMB) and perforin-1 (PRF1) and calibrated the model predictions to published, experimental data using a Bayesian parameter estimation approach. We applied an information-theoretic approach to perform a global sensitivity analysis, from which we found the suppression of phosphatase activity maximizes the secretion of GZMB and PRF1. However, simply reducing the phosphatase activity is shown to deplete the cell’s intracellular pools of GZMB and PRF1. Thus, we added a synthetic Notch (synNotch) signaling circuit to our baseline model as a method for controlling the secretion of GZMB and PRF1 by inhibiting phosphatase activity and increasing production of GZMB and PRF1. We found the optimal synNotch system depends on the frequency of NK cell stimulation. For only a few rounds of stimulation, the model predicts inhibition of phosphatase activity leads to more secreted GZMB and PRF1; however, for many rounds of stimulation, the model reveals that increasing production of the cytolytic molecules is the optimal strategy. In total, we developed a mathematical framework that provides actionable insight into engineering robust NK cells for clinical applications.

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“…These analyses typically entail building a network, either from prior knowledge or through network inference, developing a mathematical model of the network interactions, and subsequently calibrating the model to experimental data (Jaqaman and Danuser, 2006;Raue et al, 2011;Shockley, Vrugt and Lopez, 2018). Although small networks have been studied with great success, the fact remains that for large networks many parameters remain difficult to ascertain and optimization routines yield multiple parameter sets that reproduce the protein concentration trajectories equally well (Ryan N. Eydgahi et al, 2013;Mitra and Hlavacek, 2019). This has led to a common practice whereby one or a few parameter vectors are chosen to make mechanistic predictions which can be validated by experiments with varying degrees of success (Janes et al, 2005;Albeck et al, 2008;Becker et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

Ortega

Wilson²,

Pino³

et al. 2021

Preprint

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Mathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.

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Parameter estimation and uncertainty quantification for systems biology models

Cited by 55 publications

References 94 publications

Programmatic modeling for biological systems

Programmatic modeling for biological systems

An Optimal Control Approach for Enhancing Natural Killer Cells’ Secretion of Cytolytic Molecules

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

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