Publications of Mohammad Mehrian
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See detailMulti-objective optimization of cost-efficient neotissue growth inside 3D scaffolds using evolutionary algorithms
Mehrian, Mohammad ULiege; olofsson, Simon; Misener, Ruth et al

Scientific conference (2018, March 26)

Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. Computer models can facilitate the design of optimal production process conditions leading ... [more ▼]

Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. Computer models can facilitate the design of optimal production process conditions leading to robust and economically viable products. We developed a computational model describing the neotissue growth (cells + their ECM) inside 3D scaffolds in a perfusion bioreactor. Here we apply multi-objective optimization (MOO) to maximize neotissue growth whilst minimizing the cost coming from medium refreshment and associated labor. [less ▲]

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See detailComputational modelling of human mesenchymal stem cell proliferation and extra cellular matrix production in 3D porous scaffolds in a perfusion bioreactor
Mehrian, Mohammad ULiege; Papantoniou, Ioannis; Lambrechts, Toon et al

Scientific conference (2018, March 26)

3D porous scaffolds are frequently used in tissue engineering (TE) applications in combination with bioreactor systems because of their ability to induce reproducible culture conditions that can control ... [more ▼]

3D porous scaffolds are frequently used in tissue engineering (TE) applications in combination with bioreactor systems because of their ability to induce reproducible culture conditions that can control specific cell behavior such as proliferation and extracellular matrix (ECM) production. A computational model describing neotissue growth inside 3D scaffolds in a perfusion bioreactor was developed, with neotissue being considered the combination of cells and their extra cellular matrix. In the model, the speed of neotissue growth depends on the flow-induced shear stress, curvature and the local concentrations of oxygen, glucose and lactate. The goal of this study is to make a distinction between the cell and the ECM fraction within the neotissue in the model to allow for a more detailed validation and optimization of the process. [less ▲]

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See detailMaximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization
Mehrian, Mohammad ULiege; Guyot, Y.; Papantoniou, I. et al

in Biotechnology and Bioengineering (2017)

In regenerative medicine, computer models describing bioreactor processes can assist in designing optimal process conditions leading to robust and economically viable products. In this study, we started ... [more ▼]

In regenerative medicine, computer models describing bioreactor processes can assist in designing optimal process conditions leading to robust and economically viable products. In this study, we started from a (3D) mechanistic model describing the growth of neotissue, comprised of cells, and extracellular matrix, in a perfusion bioreactor set-up influenced by the scaffold geometry, flow-induced shear stress, and a number of metabolic factors. Subsequently, we applied model reduction by reformulating the problem from a set of partial differential equations into a set of ordinary differential equations. Comparing the reduced model results to the mechanistic model results and to dedicated experimental results assesses the reduction step quality. The obtained homogenized model is 105 fold faster than the 3D version, allowing the application of rigorous optimization techniques. Bayesian optimization was applied to find the medium refreshment regime in terms of frequency and percentage of medium replaced that would maximize neotissue growth kinetics during 21 days of culture. The simulation results indicated that maximum neotissue growth will occur for a high frequency and medium replacement percentage, a finding that is corroborated by reports in the literature. This study demonstrates an in silico strategy for bioprocess optimization paying particular attention to the reduction of the associated computational cost. © 2017 Wiley Periodicals, Inc. [less ▲]

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See detailBayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up
olofsson, Simon; Mehrian, Mohammad ULiege; Geris, Liesbet ULiege et al

Scientific conference (2017, October 01)

We consider optimising bone neotissue growth in a 3D scaffold during dynamic perfusion bioreactor culture. The goal is to choose design variables by optimising two conflicting objectives: (i) maximising ... [more ▼]

We consider optimising bone neotissue growth in a 3D scaffold during dynamic perfusion bioreactor culture. The goal is to choose design variables by optimising two conflicting objectives: (i) maximising neotissue growth and (ii) minimising operating cost. Our contribution is a novel extension of Bayesian multi-objective optimisation to the case of one black-box (neotissue growth) and one analytical (operating cost) objective function, that helps determine, within a reasonable amount of time, what design variables best manage the trade-off between neotissue growth and operating cost. Our method is tested against and outperforms the most common approach in literature, genetic algorithms, and shows its important real-world applicability to problems that combine black-box models with easy-to-quantify objectives like cost. [less ▲]

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See detailManaging donor-related variability in cell production by means of data-based modelling
Mehrian, Mohammad ULiege

Scientific conference (2017, May 05)

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See detailMAXIMIZING NEOTISSUE GROWTH IN A PERFUSION BIOREACTOR USING BAYESIAN OPTIMIZATION
Mehrian, Mohammad ULiege; guyot, Yann; Papantoniou, Ioannis et al

Scientific conference (2017, February 01)

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See detailModel-Based Optimization of the Medium Refreshment Regime During Neotissue Growth in a Perfusion Bioreactor
Mehrian, Mohammad ULiege; guyot, Yann; Papantoniou, Ioannis et al

Scientific conference (2017, January 08)

Computational models are interesting tools to facilitate the translation from the laboratory to the patient. In regenerative medicine, computer models describing bioprocesses taking place in bioreactor ... [more ▼]

Computational models are interesting tools to facilitate the translation from the laboratory to the patient. In regenerative medicine, computer models describing bioprocesses taking place in bioreactor environment can assist in designing process conditions leading to robust and economically viable products. In this study we present a low-cost computational model describing the neotissue (cells + extracellular matrix) growth in a perfusion bioreactor set-up. The neotissue growth is influenced by the geometry of the scaffold, the flow-induced shear stress and a number of metabolic factors. After initial model validation, a Genetic Algorithm optimization technique is used to find the best medium refreshment regime (frequency and percentage of medium replaced) resulting in a maximal amount of neotissue being produced in the scaffold in a 28 days of culture period. [less ▲]

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See detailImproving Perfusion Bioreactor Yields by Using Particle Swarm Optimization
Mehrian, Mohammad ULiege; guyot, Yann; Papantoniou, Ioannis et al

Scientific conference (2016, November 25)

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See detailA reduced model developed for describing neotissue growth during dynamic bioreactor culture
Mehrian, Mohammad ULiege; Guyot, Yann; Geris, Liesbet ULiege

Scientific conference (2015, November 26)

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See detailModeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks.
Mehrian, Mohammad ULiege; Asemani, Davud; Arabameri, Abazar et al

in Computational biology and chemistry (2014), 48

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most ... [more ▼]

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth. [less ▲]

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See detailModeling of Antitumor Drug Pharmacodynamics Using Genetic algorithm
Mehrian, Mohammad ULiege

Conference (2013)

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See detailModeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks
Mehrian, Mohammad ULiege; Arabameri, Abazar; Sedghi, Alireza et al

in Modeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks (2013)

Detailed reference viewed: 38 (11 ULiège)
See detailMinimization of THD and Transmission Losses Using GA SVC controller
Mehrian, Mohammad ULiege

Conference (2012)

Detailed reference viewed: 17 (2 ULiège)