Académie royale de Médecine de Belgique

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Résumé de Liesbet Geris (Université de Liège)

FROM BIG DATA TO COMPLEX DATA AND IN SILICO MEDICINE: A BONE TISSUE ENGINEERING CASE STUDY

by Liesbet GERIS (Biomechanics Research Unit – ULg, Biomechanics Section – KU Leuven).  

Tissue engineering (TE), the interdisciplinary field combining biomedical and engineering sciences in the search for functional man-made organ replacements, has key issues with the quantity and quality of the generated products. Protocols followed in the lab are mainly trial and error based, requiring a huge amount of manual interventions and lacking clear early time-point quality criteria to guide the process. As a result, these processes are very hard to scale up to industrial production levels.  In many engineering sectors, in silico modelling is used as an inherent part of the R&D process. In this talk  I will discuss a number of (compute intensive) examples demonstrating the contribution of in silico modelling to the bone tissue engineering process.

A first example that will be discussed is the simulation of bioreactor processes.  Currently, only a limited number of online read-outs is available which can be used to monitor and control the biological processes taking place inside the bioreactor.  We developed a computational model of neotissue growth inside the bioreactor that, in combination with the experimental read-outs, allow for a quantification of the processes taking place inside the bioreactor.  Scaffold geometry (curvature-based growth), fluid flow (Brinkman equation) and nutrient supply were simulated to affect the growth rate of the neotissue.  The model captured the experimentally observed growth patterns qualitatively and quantitatively. Additionally, the model was able to calculate the micro-environmental cues (mechanical and nutrient-related) that cells experience both at the neotissue-free flow interface and inside the neotissue. In order to allow for incorporation of the model in the controller of bioreactor processes, a reduced version of the model has been developed.  This reduced version also allows for rigorous optimisation of the bioreactor settings based on biological and economical arguments.

The second example pertains to the assessment of the in vivo bone regeneration process.  As normal fractures lead to successful healing in 90-95% of the cases, people in need of tissue engineering solutions often suffer from severe trauma, genetic disorders or comorbidities.  One of these genetic disorders affecting the bone regeneration process is neurofibromatosis type I. Starting from an established computational model of bone regeneration, we examined the effect of the NF1 mutation on bone fracture healing by altering the parameter values of eight key factors which describe the aberrant cellular behaviour of NF1 affected cells. We show that the computational model is able to predict the formation of a non-union and captures the wide variety of non-union phenotypes observed in patients. A sensitivity analysis by “Design of Experiments” was used to identify the key predictors of the severity of the non-union.

I will conclude the lecture with a general perspective on in silico medicine and its challenging for the years to come.