Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Warne, David J.; Prescott, Thomas P.; Baker, Ruth E.; Simpson, Matthew J.

doi:10.1016/j.jcp.2022.111543

Cited by 8 publications

(2 citation statements)

References 119 publications

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“…On the other hand, hybrid multiscale models require estimating parameters that modulate several relevant processes, such as cell-cell communication, gene regulation, cell division, cell death, oxygen consumption, and interactions, resulting in highly complex models. Furthermore, it is challenging to calibrate and validate these models due to the limited availability of patient-specific data (particularly spatially resolved, serial data) and the substantial computational cost of their iterative parameter fitting techniques [25][26][27]. Consequently, it will be essential to integrate machine learning and high-performance computing (HPC) techniques when creating virtual patient models [25,28].…”

Section: Introductionmentioning

confidence: 99%

A multiscale model of immune surveillance in micrometastases: towards cancer patient digital twins

Rocha,

Aguilar,

Getz

et al. 2023

Preprint

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Metastasis is the leading cause of death in patients with cancer. Among the targets of this spread, the lung is one of the most frequent targets of metastasis. In the scientific community, many studies have been developed to study the process of cancer dissemination in the lung. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in the lung. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, uncontrolled growth, and partial and complete immune response to tumor growth. We classified the patients and identified key patient parameters that regulate immunosurveillance. We highlight the lessons derived from this analysis and outline the primary challenges in building cancer patient digital twins. These digital twins would enable clinicians to systematically dissect the complexity of cancer in each individual patient, simulate treatment outcomes, and ultimately select the most suitable treatment.

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Section: Introductionmentioning

confidence: 99%

A multiscale model of immune surveillance in micrometastases: towards cancer patient digital twins

Rocha,

Aguilar,

Getz

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Also, choosing the best error function for comparing experimental data with model output is challenging. Methods of calibrating computational biology models include nonlinear least squares regression 9 , maximum likelihood estimation (MLE), maximum a prior (MAP) estimation 10 , Markov chain Monte Carlo (MCMC) 11 , and genetic algorithms 12 , among others [13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Kumar,

Dowling,

Zartman

2023

Preprint

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Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organ’s shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections ofDrosophilawing imaginal discs, a highly informative model organ system, to study mechanisms that regulate epithelial processes that range from development to cancer. As a specific test case, the parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with time series imaging data of wing discs perturbed with collagenase. Unexpectedly, the framework also identifies multiple distinct parameter sets that generate shapes similar to wild-type organ shapes. This platform enables an efficient, global sensitivity analysis to support the necessity of both actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with fixed tissue imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This framework is extensible toward reverse-engineering the morphogenesis of any organ system and can be utilized in real-time control of complex multicellular systems.

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Diffusion equations with Markovian switching: Well-posedness, numerical generation and parameter inference

Zhang

Dai

et al. 2023

Chaos, Solitons & Fractals

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Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Cited by 8 publications

References 119 publications

A multiscale model of immune surveillance in micrometastases: towards cancer patient digital twins

A multiscale model of immune surveillance in micrometastases: towards cancer patient digital twins

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Diffusion equations with Markovian switching: Well-posedness, numerical generation and parameter inference

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