In Vivo, In Vitro, In Silico: Computational Tools for Product and Process Design in Tissue Engineering - Computational Modeling in Tissue Engineering - page 10

4 Discussion
As shown above, computational tools have already investigated a wide variety of
products and processes in the tissue engineering field. Whereas in the early models
the distance between the computer and the bench was quite substantial, integration
of (biological) experiments and simulation efforts are increasing. It has become
evident that imaginative and refined experimental strategies based on genetics,
imaging, quantitative and biophysical approaches, combined with the exploration
of the fullest potential of mathematical modelling are necessary to understand
cellular and developmental biology. The increased attention for this integrative
approach can be appreciated from the initiatives that have been and are being taken
by large funding agencies to promote this research, e.g. the Quantissue network
[ 54 ] (funded by ESF-RNP) and the Physiome [ 55 ]/Virtual Physiological Human
initiatives [ 56 ] (funded by agencies worldwide). The potential of this integrative
research has already been demonstrated in a number of biomedical fields [ 57 - 62 ].
For example, Faratian et al. [ 57 ] successfully used a systems biology approach to
stratify patients for personalized therapy in cancer and provided further compelling
evidence that a particular biomarker, appropriately measured in the clinical setting,
could refine clinical decision making in patients treated with a specific therapy. In
developmental biology, Von Dassow and co-workers [ 59 , 60 ] showed by means of
a computational model that the drosophila segment polarity genes constitute a
robust developmental module. The simulation results provided important insights
into the overall dynamics of the gene network and highlighted mechanistic details
that require further experimental research.
With the increasing demand for more quantitative models, there is also an
increasing attention for the determination of relevant parameter sets [ 63 - 65 ].
Precise measurements of the different parameter values is in almost all cases
impossible, either due to the fact that not all parameters represent physical pro-
cesses (even when dealing with mechanistic or white box models) or because the
physical property cannot be measured without altering the process. An example of
the latter is the use of in vitro experiments to determine properties of in vivo
processes. Classical system identification techniques, typically used in grey and
black box approaches, will determine the parameter values as to fit the model to
the system it is intended to describe. Depending on the system at hand and on the
available experimental information, estimation theory or neural networks are
commonly used concepts. Additionally, engineering concepts such as the design of
experiments and optimal experimental design are finding their way into the bio-
medical sciences to increase the amount of information that can be retrieved from
experiments while reducing the number of experimental runs required to obtain
this information. Alternatively, or better yet concomitantly, to finding appropriate
parameter values based on experimental results, many modellers apply techniques
to investigate the impact of the chosen parameter values on their simulation results
by means of sensitivity analyses. Sensitivity analyses appear under many different
forms. The most frequently used technique is the one-at-the-time (OAT) analysis