Biomedical Engineering Reference
In-Depth Information
network approaches that to a certain extent mimic learning processes can be used to
reduce computational costs significantly by substituting complex simulations at one
or more levels of a multiscale model. Since they can easily incorporate additional
data as it becomes available from a variety of sources and adapt to them (via re-
training) they present a powerful tool for the integration of information from
mechanical, biological and other sources. Neural networks do not rely on any a-priori
defined rules but ''discover'' them and as such may aid in the development of potent
mechanobiological theories based on large amounts of diverse data, both experi-
mental and theoretical, that may be difficult to condense analytically.
The advances made have provided and continue to provide insight into many
aspects relevant for tissue engineering: Cellular models accounting for the
dynamic nature of the cytoskeleton and focal adhesions improve our understanding
of the cell's interaction with its immediate surrounding. This includes measure-
ment tools so that these active models can help interpret experimental data. Models
of regenerative processes in vivo allow the investigation of environmental factors
on tissue differentiation processes. These frameworks help to uncouple individual
mechanisms, such as the contribution of tissue structure to bulk mechanical
properties, and test hypotheses related to these mechanisms that could not be tested
directly experimentally. Computational techniques and engineering approaches to
problem solving in general can benefit in particular processes such as scaffold
design, where a large number of coupled criteria have to be balanced in order to
achieve a desired product. Here, simulation techniques serve both purposes:
Investigation and understanding of the biophysical environment and its biological
consequences on the one hand, and, more traditionally yet not simpler, a robust
quantification to facilitate decisions on design parameters on the other. The
diversity of current modelling techniques reflects that of the length scales, ques-
tions, applications and challenges in the field of tissue engineering.
6.1 Outlook
Challenges in computational mechanobiology that offer opportunities for further
development are abundant. From a basic research perspective, models need to be
developed to advance our understanding of how cells and tissues perform in a
mechanically challenging environment as well as why certain regenerative
approaches work and others don't. In order to be able to provide such insights
several challenges need to be addressed:
• Models should reflect individual aspects of a problem more accurately and
mechanistically to provide quantitative understanding, predictive ability and
allow the testing of hypotheses via e.g. numerical knock-out simulations.
• Models addressing problems in tissue engineering need to consider multiple
concepts,
such
as
transport,
electromechanical
stimulation,
effects
of
the
chemical environment and reactions occurring therein.
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