scholarly journals Finding Instability in Biological Models

2014 ◽  
pp. 358-372 ◽  
Author(s):  
Byron Cook ◽  
Jasmin Fisher ◽  
Benjamin A. Hall ◽  
Samin Ishtiaq ◽  
Garvit Juniwal ◽  
...  
Keyword(s):  
Biological Models

NON-STANDARD DISCRETIZATION METHODS FOR SOME BIOLOGICAL MODELS

2009 ◽  
Vol 12 (1) ◽  
pp. 126-133
Author(s):  
Shawki A. M. Abbas ◽  
Keyword(s):  
Biological Models ◽  
Discretization Methods

scholarly journals Integrating Biosensors in Organs-on-Chip Devices: A Perspective on Current Strategies to Monitor Microphysiological Systems

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 110 ◽  
Author(s):  
Erika Ferrari ◽  
Cecilia Palma ◽  
Simone Vesentini ◽  
Paola Occhetta ◽  
Marco Rasponi
Keyword(s):  
Imaging Techniques ◽  
Biological Models ◽  
Electromechanical Properties ◽  
Human Organs ◽  
Tissue Constructs ◽  
Microphysiological Systems ◽  
Metabolic Activities ◽  
On Chip ◽  
Chip Analysis

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.

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

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
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.

scholarly journals Symbolic computer graphics and biological models

1972 ◽  
Author(s):  
Mark W. Horovitz ◽  
Donald M. Austin ◽  
Harvard H. Holmes
Keyword(s):  
Computer Graphics ◽  
Biological Models

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

Symmetry ◽  
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.

scholarly journals Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

2021 ◽  
Vol 22 (8) ◽  
pp. 4053
Author(s):  
Ewa Bączyńska ◽  
Katarzyna Karolina Pels ◽  
Subhadip Basu ◽  
Jakub Włodarczyk ◽  
Błażej Ruszczycki
Keyword(s):  
Dendritic Spines ◽  
Quantitative Data ◽  
Statistical Significance ◽  
Brain Diseases ◽  
Synaptic Connections ◽  
Brain Pathology ◽  
Biological Models ◽  
Structural Geometry ◽  
Pathological Conditions ◽  
Experimental Approaches

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.

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