Publications of Mohammad Mehrian
Bookmark and Share    
Full Text
See detailBayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up
olofsson, Simon; Mehrian, Mohammad ULg; Geris, Liesbet ULg 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 ▲]

Detailed reference viewed: 30 (3 ULg)
Full Text
See detailManaging donor-related variability in cell production by means of data-based modelling
Mehrian, Mohammad ULg

Scientific conference (2017, May 05)

Detailed reference viewed: 10 (3 ULg)
Full Text
See detailMAXIMIZING NEOTISSUE GROWTH IN A PERFUSION BIOREACTOR USING BAYESIAN OPTIMIZATION
Mehrian, Mohammad ULg; guyot, Yann; Papantoniou, Ioannis et al

Scientific conference (2017, February 01)

Detailed reference viewed: 6 (2 ULg)
Full Text
See detailModel-Based Optimization of the Medium Refreshment Regime During Neotissue Growth in a Perfusion Bioreactor
Mehrian, Mohammad ULg; 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 ▲]

Detailed reference viewed: 11 (1 ULg)
Full Text
See detailImproving Perfusion Bioreactor Yields by Using Particle Swarm Optimization
Mehrian, Mohammad ULg; guyot, Yann; Papantoniou, Ioannis et al

Scientific conference (2016, November 25)

Detailed reference viewed: 6 (1 ULg)
Full Text
See detailA reduced model developed for describing neotissue growth during dynamic bioreactor culture
Mehrian, Mohammad ULg; Guyot, Yann; Geris, Liesbet ULg

Scientific conference (2015, November 26)

Detailed reference viewed: 20 (6 ULg)
Full Text
See detailModeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks.
Mehrian, Mohammad ULg; 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 ▲]

Detailed reference viewed: 29 (7 ULg)
Full Text
See detailModeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks
Mehrian, Mohammad ULg; Arabameri, Abazar; Sedghi, Alireza et al

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

Detailed reference viewed: 33 (11 ULg)