Biomedical Engineering Reference
In-Depth Information
complete (meaning all cells and not only a sample should be checked), real-time and
predictive of further clinical therapeutic effect, conventional cell biology techniques
are ruled out. Sasaki et al. [ 38 ] discuss the potential of image-based quality assess-
ment by implementing machine learning models to connect biological phenomena
with the measurements. After storage and quality assurance of the cell population,
the timely administration of the appropriate concentrations of cells in the correct
location is another crucial point where computational modelling can be an interesting
tool. Geris et al. [ 39 ] have investigated the administration of MSCs in and at the
fracture site of atrophic non-unions by means of a computational model and have
corroborated their simulation results through comparison with the results from a pilot
experiment (which was based on the in silico predictions).
3.2 Computational Tools for Process Design
Nature uses a very complex system of regulatory mechanisms compounded by a
huge amount of redundancy. Systems biology and bio-informatics are just
beginning to unlock the huge amount of information that is hidden within the
human genome. From this huge amount of info a limited number of functional
regulators (targets or markers) needs to be distilled that are indicative of the
progress of the biological process in vitro and can hence be used to control the TE
manufacturing process. These regulators are not necessarily restricted to biological
parameters but can also be properties of the carrier structure and culture envi-
ronment. These regulators are part of an intricate network that is too complex to be
interpreted without the help of in silico modelling.
An additional challenge when dealing with biological processes is how to extract
knowledge on these regulators from the relatively few process states that can be
measured on-line [ 40 ]. In this context, the monitoring and control of bioreactor
systems will be crucial at the research stage of product development, in order to
identify these key regulators and to establish standardised production methods [ 41 ].
A mathematical model of the process is a cornerstone for modern control approa-
ches such as model-based predictive controllers (MPC). Therefore, a complete
design of an automatic bioreactor system should include the development of a good
model, which should be complete enough to fully capture the process dynamics at
interest and should also be capable of allowing the predictions to be calculated but at
the same time, it should be intuitive and permit theoretic analysis [ 42 ].
Various types of models can be used as long as they allow accurate predictions of
the most important process output(s) and are compact enough to be implemented in
the bioreactor system. In many control applications black box models are used (e.g.
impulse response models, step response models, transfer function models, state space
models, neural networks, etc.) that describe the process under consideration based on
data of dynamic experiments (dynamic data-based models). They have the advantage
that they are compact, allow accurate predictions of the process behaviour and are
easy to implement in a model-based control framework. However, an important
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