Mechanobiological Understanding of Internal and External
Variability-Generated Uncertainty in Stem Cell Bioprocesses
Understanding the complex interplay between internal and external forces
affecting cellular mechanotransduction under correctly combined
conditions could be of benefit for optimal bioprocess design.Figure 2 illustrates the intrinsic disorder characteristics of
stem cell bioprocesses dominated by external forcing and internal
variability. Cellular functions are influenced not only by internal
cellular machinery but also by external mechanical cues from the
surrounding microenvironment. Forces that arise in biological systems
can be classified into two main categories: internal and external (Chen
et al., 2019; Mao and Baum, 2015; Vining and Mooney, 2017). Internal
forces are defined as forces generated by cells themselves. The ability
of cells to adhere, move, and divide requires a cytoskeleton that can
reversibly assemble into a wide range of structures. The
spatiotemporally organized forces produced between cells and their
extracellular environment, as well as intercellular forces within cell
colonies, play pivotal roles in driving these migration steps (Shuzui et
al., 2019). In this way, the regulated re-modeling of cell-substrate and
cell-cell interactions during growth aids the maintenance of cellular
homeostasis. Conversely, external forces are defined as forces, such as
tensile, compressive, or shear stresses, which are applied to cells from
their environment and culture operation (Goodwin and Nelson, 2021;
Ingber, 2018; Wall et al., 2018). These mechanical forces resulting from
both intracellularly-generated and externally-applied forces can alter
the internal equilibrium state, thereby affecting cellular
mechanobiological responses. Experimental and theoretical investigations
have determined the architecture of external noise and its causal
factors, though substantial uncertainty surrounds the importance of
mechanical forces (Charras and Yap, 2018; Ivanovska et al., 2015; Jégou
and Romet-Lemonne, 2021). When subjected to mechanical forces, cells
adopt a mechanoprotective and adaptative behavior, mechanically
explained by a strain-stiffening process, to control membrane integrity,
cell shape, and structural integrity (Doss et al., 2020; Stricker et
al., 2010; Walker et al., 2020b). The situation is fundamentally
different in signaling and regulatory networks. Interactions embedded in
such signaling networks need to be extremely dynamic and versatile to be
able to respond quickly to specific stimuli and to adapt to this
response over time (Kinney et al., 2019; Mack et al., 2004; Mendez and
Janmey, 2012). If the response time is too short, these forces are not
transmitted sufficiently, and if it is too long, these forces are
transmitted across the cytoskeleton to the nucleus, where they result in
substantial deformation. These forces and deformations can regulate
chromatin structure and transcriptional activity through a number of
mechanisms (Miroshnikova et al., 2017; Tajik et al., 2016). Both
magnitude and frequency of an external force affect the consequence
(Freund et al., 2012; Kaazempur
Mofrad et al., 2005; Mack et al., 2004). Many questions concerning the
generation of internal and external mechanical forces in gene expression
and their effects on cellular behavior remain unanswered. Since all
cellular processes, including transcription and translation, are basic
to stochasticity in biochemical processes, the fluctuations in the
amount and state of mRNA and corresponding protein levels will influence
their cellular functions (Raser and O’shea, 2005; Thomase et al., 2018).
The mechanisms by which externally applied mechanical forces control
stem cells during culture do not involve the direct and spontaneous
triggering of differentiation per se, but rather the formation of a
particular behavioral memory under dynamic microenvironment that can
generate several mechanical stimuli. Importantly, there is the added
complexity of demonstrating that the key process input variables are
critical for the resultant product quality in a combinatorial process.
In process in relation to a combinatorial operation, it is very
difficult to determine cause-and-effect interactions and their
relationship to the final product quality, since inputs may act singly,
agonistically, or cumulatively on process outputs and final process
output is a viable cell that cannot currently be comprehensively
defined.