Series Title: Biological Models Volume Title: From Knowledge Networks to Biological Models

2012 ◽  
Keyword(s):  
Knowledge Networks ◽  
Biological Models

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 Knowledge Network for Sustainable Local Development

2021 ◽  
Vol 13 (3) ◽  
pp. 1124
Author(s):  
Freddy Marín-González ◽  
Alexa Senior-Naveda ◽  
Mercy Narváez Castro ◽  
Alicia Inciarte González ◽  
Ana Judith Paredes Chacín
Keyword(s):  
Social Capital ◽  
Content Analysis ◽  
Local Development ◽  
Knowledge Exchange ◽  
Knowledge Networks ◽  
Knowledge Network ◽  
Deductive Inference ◽  
The Government ◽  
Exchange Network ◽  
The Relationship

This article aims to build a network for the exchange of knowledge between the government and production, community and university sectors for sustainable local development. To achieve this, the authors relied on the concepts of sustainable local development, social capital, the relationship between sectors or intersectorality, networks and interdisciplinary and transdisciplinary knowledge. Regarding the methodology, the abductive method was used. Under a documentary design, the research techniques were a content analysis of theoretical documents and the deductive inference technique. The construction of a knowledge exchange network for sustainable local development stands out as the result. It is concluded that knowledge networks for sustainable local development have positive implications in the establishment of alliances and links between the sectors that make up society.

scholarly journals Is academia becoming more localised? The growth of regional knowledge networks within international research collaboration

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
John Fitzgerald ◽  
Sanna Ojanperä ◽  
Neave O’Clery
Keyword(s):  
Economic Development ◽  
International Collaboration ◽  
International Research ◽  
Research Collaboration ◽  
Learning By Doing ◽  
Knowledge Networks ◽  
Collaboration Network ◽  
International Research Collaboration ◽  
The Past ◽  
Capability Building

AbstractIt is well-established that the process of learning and capability building is core to economic development and structural transformation. Since knowledge is ‘sticky’, a key component of this process is learning-by-doing, which can be achieved via a variety of mechanisms including international research collaboration. Uncovering significant inter-country research ties using Scopus co-authorship data, we show that within-region collaboration has increased over the past five decades relative to international collaboration. Further supporting this insight, we find that while communities present in the global collaboration network before 2000 were often based on historical geopolitical or colonial lines, in more recent years they increasingly align with a simple partition of countries by regions. These findings are unexpected in light of a presumed continual increase in globalisation, and have significant implications for the design of programmes aimed at promoting international research collaboration and knowledge diffusion.

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