Computational Modeling of Mass Transport and Its Relation to Cell Behavior in Tissue Engineering Constructs - Computational Modeling in Tissue Engineering - page 82

1 Introduction
The functionality of extracellular matrix (ECM) in organ tissues is much broader
than just structural support for cells that reside within. Cells are able to interact
with ECM, both biochemically and biophysically, from structural remodeling
induced by cell proteolytic activity [ 72 ] to stem cell lineage specification via
straining of linked proteins [ 30 ]. The ECM also provides an important storage
space for signaling molecules, a feature which appeared to be crucial in tissue
morphogenesis [ 61 , 121 ]. Morphogenesis is mainly driven by the gradients in
signaling molecules (i.e. morphogens) which can arise from differences in mor-
phogen diffusivity [ 79 , 127 ] or from interstitial fluid flow-induced asymmetry in
morphogen distribution upon enzymatic release from the matrix [ 50 ]. Under-
standing the mass transport principles which underlie the formation and mainte-
nance of morphogen gradients is therefore fundamental to understand how these
gradients will direct tissue patterning.
It should be clear that recapitulating fundamental ECM properties in a tissue
engineering (TE) context relates closely to the aim of establishing a proper mass
transport environment. From basic nutrients to signaling molecules, concentration
gradients might exist for any soluble medium component that is consumed or
produced by the cells [ 42 ]. The effects they can elicit on cell behavior are
numerous and have proven to be a function of absolute concentrations, the range of
operation and slope [ 46 ]. To measure such gradients, the use of biosensors [ 1 ] and
tracer molecules [ 127 ] has been previously reported. Experimental quantification
is however not always straightforward and can even become too challenging for
more complex (in vivo-like) setups. A powerful tool that makes quantification
easier and can predict gradient magnitudes for the even most complex situations
[ 86 ], lies in the combined use of experiments and mathematical modeling [ 24 ].
Mathematical models help in establishing relations and insights between
evolving solute profiles and specific cell behavior [ 80 ]. Such models have proven
their applicability in unraveling important mechanisms and dynamics of experi-
mental observations [ 25 , 37 ]. Their usability ranges from the establishment of
numerical interactions between influencing parameters [ 130 ] to optimization of
culture conditions for nutrient transport [ 111 ] and modeling-based TE carrier
design [ 16 ].
In light of the design of biomaterial carriers, special attention should be
attributed to the environmental remodeling abilities of a cell. Triggered by their
proteolytic activity cells can break down ECM components for migration or
modify tissue architecture in response to biophysical or biochemical forces [ 97 ].
These changes have however important consequences on the transport and activity
of autocrine and paracrine signaling molecules, both directly and indirectly [ 115 ].
Implementation of structural biomaterial remodeling in mathematical models has
contributed to a better comprehension of its active role in cell signaling [ 120 ].
Translating these remodeling principles in a TE strategy has led to the develop-
ment of biomaterials crosslinked by enzyme-degradable peptide sequences which