Accelerated Simulation of Hybrid Biological Models with Quasi-Disjoint Deterministic and Stochastic Subnets

Organs-on-chip (OoC), often referred to as microphysiological systems (MPS), are advanced in vitro tools able to replicate essential functions of human organs. Owing to their unprecedented ability to recapitulate key features of the native cellular environments, they represent promising tools for tissue engineering and drug screening applications. The achievement of proper functionalities within OoC is crucial; to this purpose, several parameters (e.g., chemical, physical) need to be assessed. Currently, most approaches rely on off-chip analysis and imaging techniques. However, the urgent demand for continuous, noninvasive, and real-time monitoring of tissue constructs requires the direct integration of biosensors. In this review, we focus on recent strategies to miniaturize and embed biosensing systems into organs-on-chip platforms. Biosensors for monitoring biological models with metabolic activities, models with tissue barrier functions, as well as models with electromechanical properties will be described and critically evaluated. In addition, multisensor integration within multiorgan platforms will be further reviewed and discussed.

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Multi-core and GPU accelerated simulation of a radial star target imaged with equivalent t-number circular and Gaussian pupils

10.1117/12.2021036 ◽

2013 ◽

Cited By ~ 1

Author(s):

Alan W. Greynolds

Keyword(s):

Accelerated Simulation

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Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

Journal of The Royal Society Interface ◽

10.1098/rsif.2017.0340 ◽

2017 ◽

Vol 14 (134) ◽

pp. 20170340 ◽

Cited By ~ 6

Author(s):

Aidan C. Daly ◽

Jonathan Cooper ◽

David J. Gavaghan ◽

Chris Holmes

Keyword(s):

Bayesian Inference ◽

Bayesian Methods ◽

Sequential Monte Carlo ◽

Model Parameters ◽

Biological Models ◽

Exact Inference ◽

Modelling Studies ◽

Approximate Bayesian ◽

Approximate Bayesian Inference ◽

Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

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Symbolic computer graphics and biological models

10.1145/800154.804979 ◽

1972 ◽

Author(s):

Mark W. Horovitz ◽

Donald M. Austin ◽

Harvard H. Holmes

Keyword(s):

Computer Graphics ◽

Biological Models

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Stability Analysis and Existence of Solutions for a Modified SIRD Model of COVID-19 with Fractional Derivatives

Symmetry ◽

10.3390/sym13081431 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1431

Author(s):

Bilal Basti ◽

Nacereddine Hammami ◽

Imadeddine Berrabah ◽

Farid Nouioua ◽

Rabah Djemiat ◽

...

Keyword(s):

Mathematical Model ◽

Existence And Uniqueness ◽

Fractional Derivatives ◽

Fixed Point Theorems ◽

Existence Of Solutions ◽

Biological Models ◽

Uniqueness Of Solutions ◽

Caputo Fractional Derivatives ◽

Single Form ◽

Stability Results

This paper discusses and provides some analytical studies for a modified fractional-order SIRD mathematical model of the COVID-19 epidemic in the sense of the Caputo–Katugampola fractional derivative that allows treating of the biological models of infectious diseases and unifies the Hadamard and Caputo fractional derivatives into a single form. By considering the vaccine parameter of the suspected population, we compute and derive several stability results based on some symmetrical parameters that satisfy some conditions that prevent the pandemic. The paper also investigates the problem of the existence and uniqueness of solutions for the modified SIRD model. It does so by applying the properties of Schauder’s and Banach’s fixed point theorems.

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