An in silico future for the engineering of functional tissues and organs

Diaz-Zuccarini,; Lawford, PV

doi:10.4161/org.6.4.13284

Cited by 12 publications

(6 citation statements)

References 53 publications

(51 reference statements)

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“…Thus, the review by Diaz-Zuccarini et al 14 introduces systems biology elucidating the recent impetus in this emerging field now playing a significant role in the understanding of a wide range of anatomical diseases or malfunctions thus seeing the remarkable influence computers have in assisting us solve problems in the health sciences. As in our previous issue we intertwine our reviews with primary articles, seeing contributions by Nerem et al, 15 Davies et al 16 and Borenstein et al, 17 who demonstrate the ability to create a wide range of functional living microenvironments using advanced biopolymers to some unique methodologies which have undergone rigorous functionality tests, to the exploration of mesenchymal stem cells for wound healing and the exploration of novel microsized devices for forming scaffolds and microenvironments essential for tissue reconstruction and development to their utility for advanced controlled and targeted therapeutics.…”

Section: And Dementioning

confidence: 98%

Engineering towards functional tissues and organs

Jayasinghe

2010

Organogenesis

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Section: And Dementioning

confidence: 98%

Engineering towards functional tissues and organs

Jayasinghe

2010

Organogenesis

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“…The inclusion of these multiple scales is a challenge, because spatial and temporal scales cannot be approached by a mean field. Instead, all single elements must be considered as single mathematical identities with their own particular dynamics: from single cells, with their regulatory and metabolic dynamics for the estimation of local single cell responses and substance concentrations, to fluid dynamics, tissues with discrete populations of cells, and finally to whole organs with averaged responses 63. In order to solve such a problem, advanced computational techniques and parallelization are required.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Quantitative Evaluation and Prediction of Drug Effects and Toxicological Risk Using Mechanistic Multiscale Models

Niklas

Ochoa

Bucher

et al. 2012

Molecular Informatics

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Integrating in vitro and in silico approaches has great potential for reducing experimental effort and delivering know-how and intellectual property in drug development. Here, we focus on a possible framework for multiscale modeling in pharmaceutical drug development. Looking at the modeling frameworks at different scales, it is obvious that choosing the proper level of complexity and abstraction is not a trivial task. At cellular level, we consider that the application of validated kinetic models of cellular toxicity mechanisms of drugs is particularly important for deriving valid predictions. These kinetic models can be applied for integrating inter-individual differences, e.g. obtained from data measured in surgical liver samples, into predictions of drug effects. Challenges identified include (i) the development of sufficiently detailed, structured organ models, (ii) definition of multiscale models that can be efficiently handled by available super-computing facilities, and (iii) availability of validated cell-type and organ-specific kinetic metabolic models. Multiscale models can streamline drug development by facilitating the design of experiments and trials, by providing and testing hypotheses, and by reducing time and costs due to less experiments and improved decision-making. In this review, we discuss the required pieces, possibilities, and challenges in multiscale modeling for the prediction of drug effects.

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“…Mixing processes in droplet microfluidics often involve three-way coupled physics of two-phase flow and mass transport (convection and diffusion) of chemical species, governed by a set of partial differential equations, which require simultaneous solutions. Advances in computer sciences and computational capabilities have made it possible to use numerical models and simulations for investigating complex physics and biological phenomena, such as those undertaken in microfluidic systems, through in silico experiments [ 26 , 27 , 28 ]. There are a number of commendable reviews on numerical modelling and simulation of two-phase and multiphase flows in microchannels and their applications [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Numerical Modelling of Mixing in a Microfluidic Droplet Using a Two-Phase Moving Frame of Reference Approach

2022

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Droplets generated in microfluidic channels are effective self-contained micromixers and micro-reactors for use in a multiplicity of chemical synthesis and bioanalytical applications. Droplet microfluidic systems have the ability to generate multitudes of droplets with well-defined reagent volumes and narrow size distributions, providing a means for the replication of mixing within each droplet and thus the scaling of processes. Numerical modelling using computational fluid dynamics (CFD) is a useful technique for analysing and understanding the internal mixing in microfluidic droplets. We present and demonstrate a CFD method for modelling and simulating mixing between two species within a droplet travelling in straight microchannel, using a two-phase moving frame of reference approach. Finite element and level set methods were utilised to solve the equations governing the coupled physics between two-phase flow and mass transport of the chemical species. This approach had not been previously demonstrated for the problem of mixing in droplet microfluidics and requires less computational resources compared to the conventional fixed frame of reference approach. The key conclusions of this work are: (1) a limitation of this method exists for flow conditions where the droplet mobility approaches unity, due to the moving wall boundary condition, which results in an untenable solution under those conditions; (2) the efficiency of the mixing declines as the length of the droplet or plug increases; (3) the initial orientation of the droplet influences the mixing and the transverse orientation provides better mixing performance than the axial orientation and; (4) the recirculation inside the droplet depends on the superficial velocity and the viscosity ratio.

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An in silico future for the engineering of functional tissues and organs

Cited by 12 publications

References 53 publications

Engineering towards functional tissues and organs

Engineering towards functional tissues and organs

Quantitative Evaluation and Prediction of Drug Effects and Toxicological Risk Using Mechanistic Multiscale Models

Numerical Modelling of Mixing in a Microfluidic Droplet Using a Two-Phase Moving Frame of Reference Approach

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