Detection of arterial wall abnormalities via Bayesian model selection.
ABSTRACT: Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.
SUBMITTER: Larson K
Gradual loading ameliorates maladaptation in computational simulations of vein graft growth and remodelling.
Project description:Vein graft failure is a prevalent problem in vascular surgeries, including bypass grafting and arteriovenous fistula procedures in which veins are subjected to severe changes in pressure and flow. Animal and clinical studies provide significant insight, but understanding the complex underlying coupled mechanisms can be advanced using computational models. Towards this end, we propose a new model of venous growth and remodelling (G&R) based on a constrained mixture theory. First, we identify constitutive relations and parameters that enable venous adaptations to moderate perturbations in haemodynamics. We then fix these relations and parameters, and subject the vein to a range of combined loads (pressure and flow), from moderate to severe, and identify plausible mechanisms of adaptation versus maladaptation. We also explore the beneficial effects of gradual increases in load on adaptation. A gradual change in flow over 3 days plus an initial step change in pressure results in fewer maladaptations compared with step changes in both flow and pressure, or even a gradual change in pressure and flow over 3 days. A gradual change in flow and pressure over 8 days also enabled a successful venous adaptation for loads as severe as the arterial loads. Optimization is used to accelerate parameter estimation and the proposed framework is general enough to provide a good starting point for parameter estimations in G&R simulations.
Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.
Project description:Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cells in each sample; (ii) the number of time points; (iii) the highest-order moment of protein fluctuations used for inference; (iv) the moment-closure method used for likelihood approximation. We find that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein-DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions.
Accurate and reliable estimation of kinetic parameters for environmental engineering applications: A global, multi objective, Bayesian optimization approach.
Project description:Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed into a routine challenge in the field of Environmental Engineering. Among the main issues identified with parameter estimation, the model-data calibration approach is a crucial, yet an often overlooked and difficult optimization problem. Here, a novel and rigorous global, multi objective, and fully Bayesian optimization approach that overcomes challenges associated with multi-variate, sparse and noisy data, as well as highly non-linear model structures commonly encountered in Environmental Engineering practice is presented. This optimization approach allows an improved definition and targeting of the compromise solution space for all multivariate problems, allowing efficient convergence, and a Bayesian component to thoroughly explore parameter and model prediction uncertainty. This global optimization approach outperformed, in terms of parameter accuracy and precision, standard, local non-linear regression routines and overcomes issues associated with premature convergence and addresses overfitting of different variables in the calibration process. •A sequential single, multi-objective, and Bayesian optimization workflow was developed to accurately and reliably estimate unstructured kinetic model parameters.•The global, single objective approach defines the global optimum (the best compromise solution) and "extreme" parameter solutions for each variable, while the global, multi-objective approach confirms the "best" compromise solution space for the Bayesian search to target and convergence is assessed using the single objective results.•The Approximate Bayesian Computational approach fully explores parameter and model prediction uncertainty targeting the compromise solution space previously identified.
A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.
Project description:Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.
Hepatosplanchnic haemodynamics and renal blood flow and function in rats with liver failure.
Project description:BACKGROUND: Massive liver necrosis, characteristic of acute liver failure, may affect hepatosplanchnic haemodynamics, and contribute to the alterations in renal haemodynamics and function. AIMS: To investigate the relation between hepatosplanchnic haemodynamics, including portal systemic shunting, and renal blood flow and function in rats with acute liver failure. METHODS: Liver failure was induced in male Wistar rats by intraperitoneal injection of 1.1 g/kg of D(+)-galactosamine hydrochloride. The parameters assessed included; systemic, hepatosplanchnic, and renal blood flow (57Co microsphere method); portal-systemic shunting and intrarenal shunting (consecutive intrasplenic, intraportal, or renal arterial injections of 99mTc methylene diphosphonate and 99mTc albumin microspheres); arterial blood pressure and portal pressure; renal function; and liver function (liver function tests and 14C aminopyrine breath test). RESULTS: Progressive liver dysfunction was accompanied by the development of a hyperdynamic circulation, a highly significant decrease in renal blood flow and function, and an increase in intrarenal shunting 36, 42, and 48 hours after administration of D-galactosamine. The alterations in renal blood flow and function were accompanied by significant increases in portal pressure, portal venous inflow, and intrahepatic portal systemic shunting in galactosamine treated rats compared with controls. There was a significant correlation between changes in renal blood flow and changes in portal pressure, intrahepatic portal systemic shunting, and deterioration in liver function (r = 0.8, p < 0.0001). CONCLUSIONS: The results of this study suggest that both increased intrahepatic portal systemic shunting and hepatocyte impairment may contribute to alterations in renal haemodynamics and function.
Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model.
Project description:Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters (n?=?110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.
Project description:This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimates in white and gray matter, but PERK is consistently at least $140\times $ faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.
Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.
Project description:Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).
A Sequential Process Model for Cognitive Diagnostic Assessment With Repeated Attempts.
Project description:When diagnostic assessments are administered to examinees, the mastery status of each examinee on a set of specified cognitive skills or attributes can be directly evaluated using cognitive diagnosis models (CDMs). Under certain circumstances, allowing the examinees to have at least one opportunity to correctly answer the questions and assessments, with repeated attempts on the items, provides many potential benefits. A sequential process model can be extended to model repeated attempts in diagnostic assessments. Two formulations of the sequential generalized deterministic-input noisy-"and"-gate (G-DINA) model were developed in this study. The first extension uses the latent transition analysis (LTA) approach to model changes in the attributes over attempts, and the second extension constructs a higher order structure of latent continuous variables and latent attributes to account for the dependences of the attributes over attempts. Accurate model parameter estimation and correct classifications of attributes were observed in a series of simulations using Bayesian estimation. The effectiveness of the developed sequential G-DINA model was demonstrated by fitting real data from a longitudinal mathematical test to the developed model and the longitudinal G-DINA model using the LTA approach. Finally, this article closes by discussing several important issues associated with the developed models and providing suggestions for future directions.
The evaluation of haemodynamics in cirrhotic patients with spectral CT.
Project description:OBJECTIVE: To evaluate haemodynamics in cirrhotic patients with portal hypertension using spectral CT imaging. METHODS: 118 cirrhotic patients with portal hypertension were included in the study group (further divided into Child-Pugh A, B and C subgroups). The control group consisted of 21 subjects with normal liver functionality. All subjects underwent three-phase spectral CT scans. Material decomposition images with water and iodine as basis material pairs were reconstructed. The iodine concentrations for the hepatic parenchyma in both arterial and portal venous phases were measured. The arterial iodine fraction (AIF) was obtained by dividing the iodine concentration in the hepatic arterial phase by that in the portal venous phase. AIF values from the study and control groups were compared using analysis of variance and between subgroups using a post-hoc test with Bonferroni correction, with a statistical significance of p<0.05. RESULTS: The AIF was 0.25±0.05 in the control group, and 0.29±0.10, 0.37±0.12 and 0.43±0.14 in the study group with Child-Pugh Grades A, B and C, respectively. The difference in AIF between the control and study groups was statistically significant. The differences were statistically significant between the subgroups with multiple comparisons except between the control group and the Child-Pugh A group (p=0.685). CONCLUSION: AIF measured in spectral CT could be used to evaluate the liver haemodynamics of cirrhotic patients. ADVANCES IN KNOWLEDGE: The AIF, provided by spectral CT, could be used as a new parameter to observe liver haemodynamics.