Departments of Surgery and Pathology, Children's Hospital and Harvard Medical School, Enders 1007, 300 Longwood Avenue, Boston, MA 02115, USA
e-mail: donald.ingber{at}tch.harvard.edu
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Summary |
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Key words: Mechanobiology, Mechanotransduction, Biocomplexity, Bioinformatics, Integrins
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Introduction |
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Another driving force behind this paradigm shift is the resurgence of
interest in mechanical forces, rather than chemicals cues, as biological
regulators. Clinicians have come to recognize the importance of mechanical
forces for the development and function of the heart and lung, the growth of
skin and muscle, the maintenance of cartilage and bone, and the etiology of
many debilitating diseases, including hypertension, osteoporosis, asthma and
heart failure. Exploration of basic physiological mechanisms, such as sound
sensation, motion recognition and gravity detection, has also demanded
explanation in mechanical terms. At the same time, the introduction of new
techniques for manipulating and probing individual molecules and cells has
revealed the importance of the physical nature of the biochemical world.
Enzymes such as RNA polymerase generate as much force as molecular motors
(Mehta et al., 1999); cells
exert tractional forces on microparticles greater than those that can be
applied by optical tweezers (Schmidt et
al., 1993
); and behaviors required for developmental control,
including growth, differentiation, polarity, motility, contractility and
programmed cell death, are all influenced by physical distortion of cells
through their extracellular matix (ECM) adhesions
(Folkman and Moscona, 1978
;
Ben-Ze'ev et al., 1980
;
Ingber et al., 1986
;
Li et al., 1987
;
Ben-Ze'ev et al., 1988
;
Ingber and Folkman, 1989
;
Opas, 1989
;
Ingber, 1990
;
Mochitate et al., 1991
;
Singhvi et al., 1994
;
Chen et al., 1997
;
Lee et al., 1997
;
Dike et al., 1999
;
Parker et al., 2002
). These
insights teach us that, if we truly want to explain biological regulation and
to confront the complexity problem, we must consider how molecular signaling
pathways function in the physical context of living cells and tissues.
But how does a physical force applied to the ECM or cell distortion change
chemical activities inside the cell and control tissue development? The answer
lies in molecular biophysics; however, it also requires that we take an
architectural perspective and consider both multi-molecular and hierarchical
interactions (Ingber and Jamieson,
1985; Ingber,
1991
; Ingber,
1997
; Ingber,
1999
; Chen and Ingber,
1999
). First, it is critical to point out that many of the enzymes
and substrates that mediate protein synthesis, glycolysis and signal
transduction appear to be immobilized on insoluble molecular networks within
the cytoskeleton that provides shape to the cell
(Ingber, 1993
;
Wang et al., 1997
;
Janmey, 1998
). Regulatory
molecules involved in DNA synthesis and RNA processing similarly associate
with analogous scaffolds in the nucleus
(Pienta et al., 1991
). This
type of `solid-state' biochemistry differs from our conventional view of cell
regulation because it is not diffusion limited and insoluble biochemical
activities may be regulated independently from those that act freely in the
cytosol. Moreover, an analogous system is observed at the tissue level, where
growth factors and tissue-remodeling enzymes are similarly immobilized on
insoluble ECM scaffolds (Folkman et al.,
1988
) or on the external surface of transmembrane ECM receptors
(Boger et al., 2001
).
In Part I of this two-part Commentary, published in the last issue of JCS
(Inger et al., 2003), I described how a mechanical model of cell structure
based on tensegrity architecture (Fuller,
1961) explains how the mechanical behavior of cells emerges from
collective interactions among multiple molecular filaments within the
cytoskeleton. A mathematical formulation of this theory has led to a priori
predictions of static and dynamic mechanical behaviors that closely mimic
those exhibited by living cells. The model also revealed that the critical
determinants behind the emergence of these complex behaviors are architecture
(three-dimensional arrangement of the elements) and the level of prestress
(isometric tension) in the cytoskeleton. Both of these parameters are, in
turn, controlled by adhesive (mechanical) interactions between cells and ECM.
In addition, the tensegrity model provides a physical basis to explain the
behavior of molecules in context of the structural hierarchies in which they
normally function (i.e. multicellular organisms composed of organs, tissues
and cells). Here, in Part II of the article, I discuss how combined use of
tensegrity and solid-state mechanochemistry by cells might mediate
mechanotransduction and integrate the various physical and chemical signals
that are responsible for control of cell behavior. I also explore how these
structural networks influence cellular information-processing networks to
produce characteristic phenotypes and cell fates during tissue
morphogenesis.
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Adhesion receptors as mechanoreceptors |
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The tensegrity model of cell structure described in Part I of the article
explains how the cytoskeleton responds to mechanical stress
(Ingber, 2003). In the model,
cells generate their own internal tension or prestress in the actin
cytoskeleton, which is balanced by internal microtubule struts and external
ECM adhesions. In other words, adherent cells exist in a state of isometric
tension, and thus any external mechanical load is imposed on a pre-existing
cellular force balance. Thus, the cellular response to stress may differ
depending on the level of tension in the cell, much like tuning a guitar
string alters the tone it creates when strummed. The tensegrity model is a
mechanical paradigm, and hence it does not per se explain chemical behavior in
living cells. However, it does provide a mechanism to distribute and focus
mechanical forces on distinct molecular components throughout the cell.
Because the tensegrity model indicates that the molecular filament networks
that form the cytoskeleton and link to transmembrane adhesion receptors are
the primary load-bearing elements in the cell, it provides a potential
mechanism to link mechanical stresses applied at the tissue and organ level to
changes in molecular chemistry inside the cell. Specifically, pursuit of the
tensegrity theory led to the concept that mechanical signals that are
transferred across cell surface ECM receptors, such as integrins
(Wang et al., 1993), can be
transduced into a chemical response through distortion-dependent changes in
cytoskeletal structure either locally at the site of receptor binding or
distally at other locations inside the cell
(Ingber and Jamieson, 1985
;
Ingber, 1991
;
Ingber,1997
).
In fact, results from many studies with various cell types and model
systems show that mechanical stress application to integrins can alter
cytoskeletal structure and activate signal transduction and gene expression in
a stress-dependent manner (Wang et al.,
1993; Schmidt et al.,
1993
; Wilson et al.,
1995
; Yano et al.,
1996
; Chen and Grinnell,
1997
; Glogauer et al.,
1997
; D'Angelo et al.,
1997
; Salter et al.,
1997
; Chicurel et al.,
1998b
; Lynch et al.,
1998
; Pavalko et al.,
1998
; Low and Taylor,
1998
; Chen et al.,
1999
; Schwartz et al.,
1999
; Meyer et al.,
2000
; Lee et al.,
2000
; Wozniak et al.,
2000
; Chen et al.,
2001
; Jalali et al.,
2001
; Wang et al.,
2001b
; Maroto and Hamill,
2001
; Goldschmidt et al.,
2001
; Riveline et al.,
2001
; Liu et al.,
2002
; Urbich et al.,
2002
). These studies also show that mechanochemical transduction
proceeds, at least in part, on the cytoskeletal backbone of the focal adhesion
complex that forms at the site of integrin binding
(Glogauer et al., 1997
;
Chicurel et al., 1998b
;
Chen et al., 1999
;
Meyer et al., 2000
;
Wozniak et al., 2000
;
Riveline et al., 2001
).
Analysis of mechanical signaling within the motor nerve terminal similarly
reveals that stress-dependent activation of calcium signaling is mediated by
integrins and the effect is so rapid (<10 mseconds) that forces must be
transferred directly from the integrins to adjacent calcium channels in the
synaptic adhesion complex (Chen and
Grinnell, 1997
). Mechanical forces applied to integrins, but not
to control transmembrane proteins such as metabolic acetylated-low density
lipoprotein (Ac-LDL) receptors, growth factor receptors or HLA antigens, also
activate intracellular cAMP signaling and recruit components of the protein
synthesis machinery (e.g. ribosomes and polyA-mRNAs) directly at the site of
integrin binding (Chicurel et al.,
1998b
; Meyer et al.,
2000
; Riveline et al.,
2001
). In addition, forces applied to integrins produce a
stress-dependent increase in focal adhesion assembly that is mediated through
the small GTPase, Rho (Riveline et al.,
2001
; Balaban et al.,
2001
; Galbraith et al.,
2002
), and the sustained response of endothelium to fluid shear
stress appears to require continuous formation of new integrin-binding
complexes (Jalali et al.,
2001
). Importantly, stress-activated signals transmitted by
integrins are distinct from signals elicited by integrin receptor clustering
alone (Chicurel et al., 1998b
;
Meyer et al., 2000
), although
both signaling mechanisms appear to require focal adhesion formation and,
thus, associated cytoskeletal rearrangements
(Plopper et al., 1995
;
Miyamoto et al., 1995
).
Cytoskeletal restructuring in response to external stress through integrins
also normally requires maintenance of cytoskeletal tension generation (i.e.
cellular prestress) (Chicurel et al.,
1998b
; Wang et al.,
2001a
; Balaban et al.,
2001
). Forces transmitted across transmembrane integrin receptors
thus appear to be converted into chemical and electrical signals inside the
cell as a result of their transmission across discrete cytoskeletal linkages
and associated changes in the cytoskeletal force balance
(Fig. 1).
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Although there has been less emphasis on the role of other transmembrane
adhesion receptors in mechanotransduction, it is clear that they also
preferentially mediate transmembrane force transfer to the internal
cytoskeleton relative to non-adhesion receptors. For example, E-selectin
mechanically couples to the internal cytoskeleton by forming a specialized
adhesion complex that shares some components with focal adhesions
(Yoshida et al., 1996).
Cadherins that link to the actin cytoskeleton also mediate transmembrane
mechanical coupling in epithelial cells
(Potard et al., 1997
). Thus,
similar mechanochemical conversion mechanisms may proceed in junctional
adhesion complexes and focal adhesions; however, this remains to be
demonstrated directly.
Mechanical and chemical signal integration at the whole cell
level
One of the most important insights from the tensegrity model is that it
suggests that mechanoregulation is not based on changes in the activity of any
single mechanoreceptor or transduction molecule. Instead, mechanical signal
processing and integration proceeds at the level of the whole cell
(Ingber, 1999), because this
is the level at which the cellular force balance is established
(Ingber, 2003
). For example,
application of mechanical stress to integrins produces the same intracellular
increase in cAMP within both round and spread endothelial cells
(Meyer et al., 2000
). Yet, the
round cells integrate this signal with other inputs and switch on an apoptosis
response, whereas the spread cells proliferate
(Fig. 2) (Chen et al., 1997
). Apoptosis
rates also differ depending on the three-dimensional organization of tissue
architecture, which again is associated with changes in cell shape and ECM
mechanics (Weaver et al.,
2002
). Other cellular behaviors, including differentiation,
motility, and contractility, can be similarly altered by changing cell shape
or ECM rigidity (Li et al.,
1987
; Ben-Ze'ev et al.,
1988
; Opas, 1989
;
Singhvi et al., 1994
;
Mochitate et al., 1995; Lee et al.,
1997
; Dike et al.,
1999
; Parker et al.,
2002
). In general, in the case of most adherent cells, spread
cells grow, retracted cells differentiate and fully round or detached cells
undergo apoptosis, even though all may be stimulated with optimal levels of
growth factors and ECM binding (Fig.
2). Thus, large-scale distortion of the cell produces signals that
are distinct from those elicited by binding or stressing individual adhesion
receptors. In this manner, the physical state of the whole cell facilitates
higher-order signal integration that ultimately determines the physiological
response.
|
Analysis of the mechanism by which cell distortion switches cells between
different phenotypes has confirmed that cytoskeletal structure and prestress
both contribute to this response. Cell cycle progression and motility
(lamellipodia formation) can be inhibited by disruption of the actin
cytoskeleton or inhibition of cytoskeletal tension generation
(Iwig et al., 1995;
Bohmer et al., 1996
;
Huang et al., 1998
;
Parker et al., 2002
), whereas
cytoskeletal disruption alone promotes apoptosis
(Flusberg et al., 2001
).
Dissipation of cytoskeletal prestress also abrogates the effect of mechanical
stress on gene expression in endothelial cells
(Chen et al., 2001
). In
addition, ECM-dependent changes in the cellular force balance alter
cytoskeletal tension generation (T. Polte and D.E.I., unpublished),
cytoskeletal structure (Mochitate et al.,
1991
) and focal adhesion formation
(Balaban et al., 2001
), as well
as cell growth, differentiation, motility and apoptosis in response to
chemotherapeutic agents (Li et al.,
1987
; Ben Ze'ev et al.,
1988
; Opas, 1989
;
Mochitate et al., 1991
;
Weaver et al., 2002
). These
findings are consistent with the use of tensegrity by cells, and they show
that, although local forces can produce local responses in cells (e.g. focal
adhesion formation) (Chicurel et al.,
1998b
; Riveline et al.,
2001
; Balaban et al.,
2001
), global structural alterations at the level of the whole
cytoskeleton govern the cell's physiological response to mechanical stress. A
medically relevant example is the application of fluid shear stress to
cultured endothelial cells. This activates multiple signal transduction
pathways; however, signaling stops once the cells remodel their cytoskeletons
and realign themselves with the flow, even though they experience the same
shear stress on their surface membranes
(Davies, 1995
). Thus, it is
the ability of the endothelium to align its cytoskeleton along physiological
stress field lines that prevents pathological responses, such as expression of
inflammatory cytokines, under normal flow conditions.
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Mechanochemistry at the molecular level |
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When the shape of a molecule is altered, its biophysical properties change.
For example, theoretical studies predict that extending or decompressing a
microtubule will change the critical concentration of tubulin (a thermodynamic
parameter that controls the balance between monomer and polymer) and thereby
promote microtubule assembly (Hill and
Kirschner, 1982). If cells use tensegrity, then ECM tethers and
microtubule struts function in a complementary manner to resist cytoskeletal
tension [Fig. 3 top; also see
Fig. 2B in Part I
(Ingber, 2003
)]. Thus, when
integrins are pulled, microtubules will be decompressed, and microtubule
polymerization should be promoted (Fig.
3 bottom). In fact, this precise response has been demonstrated in
various types of cultured cell, including nerve cells and smooth muscle cells
(Joshi et al., 1985
;
Buxbaum and Heidemann, 1988
;
Dennerll et al., 1988
;
Dennerll et al., 1989
;
Putnam et al., 1998
;
Putnam et al., 2001
;
Kaverina et al., 2002
).
Although altering stresses across integrins does not alter microtubule
polymerization in liver cells, the steady-state level of soluble tubulin
monomer changes in a manner consistent with a similar stress-induced change in
the critical concentration of tubulin
(Mooney et al., 1994
).
Mechanical forces transmitted across adhesion receptors may therefore alter
intracellular biochemistry by altering thermodynamic parameters locally (i.e.
within load-bearing cytoskeletal scaffolds) in living cells
(Ingber and Jamieson, 1985
;
Ingber, 1997
). A similar form
of mechanochemistry may occur on ECM scaffolds in the extracellular milieu, as
shown, for example, by the requirement of cell tension for fibronectin fibril
assembly (Schwarzbauer and Sechler,
1999
).
|
Altering molecular shape through application of stress to integrins and
cytoskeletal filaments might also alter kinetic parameters. Imagine a spring
fixed at its base that vibrates at a certain frequency: change the size, shape
or center of gravity of this spring, and its vibration frequency will be
altered, much like a metronome. Changing the shape of the molecule (e.g.
through cytoskeletal distortion) will similarly alter its kinetic behavior
and, hence, alter its chemical rate constant
(Fig. 3 bottom). When
stress-sensitive ion channels experience mechanical stress through their
cytoskeletal linkages, they similarly alter the rate of their opening or
closing. The use of tensegrity and prestress by proteins to stabilize their
shapes at the molecular level (Ingber,
2003) may facilitate this response and provide a means to couple
physical distortion owing to large-scale forces, such as gravity, at the
macroscopic (tissue and organ) level to molecular shape changes on the
nanometer scale, resulting in altered chemical reaction rates
(Ingber, 1999
;
Chen and Ingber, 1999
).
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Tissue morphogenesis in context |
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Thus, although macroscale forces may be an obvious cause of tissue
patterning in bone (Koch,
1917) and large muscles, variations in force distribution on the
microscale may similarly guide morphogenesis in other living tissues
(Ingber and Jamieson, 1985
;
Huang and Ingber, 1999
). This
possibility is supported by the recent finding that epithelial branching
morphogenesis in embryonic lung can be selectively inhibited or accelerated by
preventing or enhancing tension generation (cytoskeletal prestress),
respectively (Moore et al.,
2002
). Application of tensional forces through the ECM also
directly promotes capillary outgrowth
(Korff and Augustin, 1999
) as
well as axon elongation in nerve cells
(Bray, 1984
). In fact, the
tensegrity principle could explain pattern formation in various tissues and
organs in species ranging from mammals
(Ingber and Jamieson, 1985
;
Joshi et al., 1985
;
Van Essen, 1997
;
Galli-Resta, 2002
) to
paramecium (Kaczanowska et al., 1995) and fungi
(Kaminsky and Heath, 1996
), as
well as loss of tissue morphology during cancer formation
(Ingber et al., 1981
;
Ingber and Jamieson, 1985
;
Pienta and Coffey, 1991
). It
also may provide a molecular basis for gravity sensing
(Ingber, 1999
;
Yoder et al., 2001
) and
control of circadian rhythmicity
(Shweiki, 1999
) in both
animals and plants. In addition, tensegrity may help to explain why cellular
components that are not directly involved in actomyosin-based tension
generation, such as microtubules, intermediate filaments and ECM, can
contribute significantly to contractile function in various cell types,
including cardiac myocytes, vascular smooth muscle and skeletal muscle
(Northover and Northover,
1993
; Tsutsui et al.,
1993
; Lee et al.,
1997
; Tagawa et al.,
1997
; D'Angelo et al.,
1997
; Eckes et al.,
1998
; Gillis,
1999
; Wang and Stamenovic,
2000
; Keller et al.,
2001
; Balogh et al.,
2002
; Loufrani et al.,
2002
) as well as to control of permeability barrier function in
endothelia (Moy et al.,
1998
).
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Confronting the biocomplexity problem |
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Tensegrity similarly teaches us that we cannot consider individual
molecules or molecular binding interactions in isolation. Collective behavior
within supramolecular assemblies, higher-order architecture and mechanical
forces also have to be considered (Ingber,
2003). Moreover, the mathematical tensegrity formulation explains
how complex behaviors (in this case mechanical) can emerge through
multi-component interactions or, in simple terms, how the whole can indeed be
greater than the sum of its parts. Thus, work on cellular tensegrity suggests
that existing `complexity' theories may be limited because they fail to
consider the physicality of the network (e.g. material properties of the
elements that connect interacting nodes, attractive/repulsive interactions,
internal force balances and three-dimensional architecture).
Tensegrity also explains how hierarchical structures may form from systems
within systems (molecules within cells within tissues within organs) and yet
still exhibit integrated mechanical behavior
(Ingber, 2003). In addition,
it reveals how robust behaviors, such as mechanical stiffness and shape
stability, can be generated from `sloppy' parts (e.g. flexible molecular
filaments), which is a key feature of both complex networks and living systems
(Csete and Doyle, 2002
). In
fact, tensegrity experts in control theory a field that focuses on how
the design of one component is influenced by the dynamics of all other
components to achieve some global property of the system have
identified prestressed tensegrity structures as the ultimate `smart' materials
whose shapes can be actively adjusted and controlled
(Skelton and Sultan, 1997
).
Both material architecture and feedback information architecture (control
mechanisms) are jointly determined in these tensegrity structures because they
are innate in the design, just as they are in living cells. Thus, tensegrity
may represent the `hardware' behind living systems.
But what about the software? This leads us to the problem of how structural networks affect information-processing networks at the level of the whole cell, where tensegrity seems to exert its effects on signal integration. Experiments show that, although individual cells can receive multiple simultaneous inputs, they can rapidly integrate these signals to produce just one of a few possible outputs or phenotypes (e.g. growth, quiescence, differentiation or apoptosis). But studies on mechanoregulation raise a fundamental question: how can a gradual change in a physical parameter over a broad range, such as cell shape (distortion from round to spread), be translated into these distinct cell fates?
Cell biologists tend to view signal transduction in terms of linear
signaling pathways that lead to one particular outcome. However, the
information conveyed by the signal transduction machinery is often distributed
among numerous pathways, and the same stimulus can lead to many different
responses. For example, activation of a single signaling receptor can induce
scores of genes (Fambrough et al.,
1999), and the same signaling molecule may elicit entirely
different effects (e.g. growth versus apoptosis), depending on the cell type,
the activity state of other regulatory proteins and the physical context in
which it acts. Thus, the concept of linear signaling pathways is inappropriate
(Strohman, 1997
;
Coffey, 1998
). Instead, these
characteristic phenotypes that cells exhibit during development represent
emergent behaviors that arise within a complex signaling network comprising
many interacting components.
The observation that gradual variations in a single control parameter (cell
shape) can switch cells between distinct gene programs (cell fates) is
reminiscent of a phase transition in physics. Gradual changes in temperature,
for example, produce abrupt macroscopic changes between qualitatively discrete
stable states (e.g. liquid versus gas or solid). Sui Huang in my group,
therefore, explored the possibility that cell fates can be viewed as `cellular
states' and that the switches between these states may represent biological
phase transitions (Huang,
1999; Huang and Ingber,
2000
). To explain this type of qualitative behavior, he viewed the
cell's molecular signaling machinery as a dynamic information-processing
network. In this manner, he was able to describe the collective behavior of
the cell's signaling molecules and their relationship to cell fate switching
without focusing on the properties of the individual molecular components.
This path led to the suggestion that cell fates can be viewed as common
end-programs or `attractors' that self-organize within the cell's dynamic
regulatory networks (Huang,
1999
). To visualize attractors, think of a ball traveling on a
complex landscape, where stable cell states are represented by valleys
(`basins of attraction') separated by unstable transition regions or
`mountainous' terrain (Fig. 5).
A ball (or cell) located at the lowermost point in one of these valleys (the
attractor) will tend to remain there. Displacement to another part of the
landscape will move the ball away from the valley, but small perturbations
will generally cause it to roll back down to its own starting point in the
same valley. Under the influence of a larger perturbation, however, the ball
could move over a mountainous peak in the landscape. At this point it is
irrevocably committed to rolling down the other side of the hill until it
reaches another attractor in a neighboring valley and, hence, takes on a
different stable phenotype. Interestingly, Waddington used this very metaphor
over 40 years ago to explain developmental control
(Fig. 6), without knowing about
genes, interactions or regulatory networks
(Waddington, 1956
).
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|
The challenge here is that these hills and valleys are not physical structures or energy wells (as in potential energy diagrams), rather they respectively represent unstable and stable states relative to the cell's dynamic information-processing network. The formation of the attractors is an emergent property that depends on the dynamic constraints imposed by the functional interconnections (e.g. gene-gene, gene-protein or protein-protein interactions) in the network. For example, when activated by some stimulus, the internal information state of the cell will shift through various cell states (e.g. gene and protein activation profiles) depending on specific pre-programmed regulatory interactions between the various signaling components that make up its signaling network (e.g. protein A will inhibit B, C will stimulate D, and E will turn on if F and G are both present). Because these interactions are hard-wired, certain states are impossible. For example, A and B cannot both be activated simultaneously if one inhibits the expression of the other; conversely, C and D must both turn on if one activates the other. The key is that these regulatory interactions constrain how these networks change over time. Extensive analysis of theoretical networks has revealed that even highly complex networks will dynamically change until they converge on a limited number of possible common end states these are the attractors (Kauffmann, 1993).
Computer simulations of dynamic networks reveal that it is necessary to
alter the activities of multiple network elements in order to switch the
network between different attractor states
(Huang, 2000). Phenotypic
transitions in living cells should therefore require simultaneous modification
of the activity status of multiple regulatory molecules. This prediction is
consistent with the observation that external stimuli that trigger pleiotropic
effects, such as soluble mitogens, ECM adhesion and cell distortion, can
induce similar cell fate transitions
(Huang and Ingber, 1999
). It
should be noted that the few `master switch' genes that have been identified,
such as MyoD (Walsh and Perlman,
1997
) or PPAR
(Morrison
and Farmer, 1999
), do not drive standard phenotypic transitions
between growth, apoptosis, etc. Rather, they trigger a transition to the
differentiation attractor of an entirely different cell type, and even these
trigger genes must activate a large set of other genes to produce this
effect.
The possibility that attractors exist in cellular information-processing
networks is supported by the observation that various stimuli that activate
multiple proteins across several signaling pathways often can trigger the same
cellular phenotypes. For example, differentiation can be switched on by
non-specific agents (e.g. DMSO or ethanol) in many cell types
(Spremulli and Dexter, 1984;
Messing, 1993
;
Yu and Quinn, 1994
), and
general inhibitors of protein kinases or phosphatases may both induce
apoptosis (Jacobson et al.,
1993
; Hehner et al.,
1999
). Similarly, growth factors, ECM and cell distortion all
regulate the same cell cycle intermediates, such as cyclin D1
(Baldin et al., 1993
;
Bohmer et al., 1996
;
Huang et al., 1998
). Thus,
simultaneous perturbation of multiple targets in different pathways results in
the channeling of the biochemical effects into common end-programs and hence
expression of the same set of distinct cell fates or attractor states. In
fact, mathematical models that incorporate information relating to known
regulatory interactions between different growth signaling molecules generate
a cell `cycle' as well as different quiescent (G0) states that are
highly reminiscent of those displayed by living cells
(Huang and Ingber, 2000
). Data
supporting the existence of attractors within the genomic regulatory networks
in human cells also have been recently obtained by using massively parallel
gene profiling techniques (S. Huang, G. Eichler and D.E.I., unpublished).
Thus, in contrast to existing paradigms that rely on explanations in terms
of specific factors and linear signaling pathways, the functional state of the
cell appears to `self-organize' as a result of the architecture and dynamics
of its underlying regulatory network. In this context, tensegrity-based
changes in cytoskeletal structure may influence cell phenotype switching on
the basis of their ability simultaneously to alter the biochemical activities
of multiple cytoskeleton-associated signaling components throughout the cell.
Because it provides a structural basis for the formation of functionally
integrated molecular hierarchies, tensegrity might also have played a central
role in the origin of cellular life (Ingber, 2000b). Thus, in the future, it
will be interesting to combine the mathematical formulation of the tensegrity
theory described in Part I of this article
(Ingber, 2003) with dynamic
network models to explore how these different types of biological network
one mechanical and the other informational coevolved so as to
allow the cell to function with the incredible efficiency it does.
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Conclusion |
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The dominant view in cell biology is that cell behavioral control is governed by soluble factors and insoluble adhesive ligands, which exert their effects by ligating cell surface receptors and thereby activating signal transduction cascades inside the cell. The tensegrity model incorporates this concept but overlays a mechanism whereby changes in the balance of mechanical forces across transmembrane adhesion receptors that link to the cytoskeleton can provide additional regulatory signals to the cell. Moreover, although signal transduction is usually described in terms of linear pathways, the functional state of the cell appears to self-organize as a result of the architecture and dynamics of its underlying gene and protein regulatory networks. Computer simulations of dynamic networks suggest that multiple targets in different pathways must be simultaneously perturbed to switch the network between a limited number of different stable end-programs (attractor states), such as growth, differentiation and apoptosis. Mechanical distortion of living cells (a generalized stimulus) and binding of specific growth factors and ECM proteins to their respective cell surface receptors all switch cells between these same discrete cell fates. The tensegrity model suggests that it is precisely because force-induced changes in cytoskeletal mechanics and chemistry can alter the activities of many signaling components at once that generalized cell distortion can produce these same discrete changes in cellular phenotype. The tensegrity principle also provides another perspective on the complexity problem in that cell mechanical behaviors similarly appear to self-organize through collective network interactions, but in this case through use of mechanical (cytoskeletal) networks, rather than gene or protein signaling networks.
In conclusion, perhaps the greatest impact of the tensegrity model is based
on how it has helped to change the frame of reference in cell biology. In the
past, we focused exclusively on the molecular components. In contrast,
tensegrity describes how molecules function collectively as components of
integrated, hierarchical systems in the physical context of living cells and
tissues. It also further expands the frame of reference by adding `tone'
(tension) and `architecture' (three-dimensional design) into the calculation.
This shift in perspective has led to explanations for behaviors that could not
be explained with conventional reductionist paradigms. The mathematical
formulation of tensegrity theory described in Part I of this Commentary
(Ingber, 2003), while
rudimentary, also represents a computational approach that can be used to
confront the complexity challenge from a structural perspective. It already
has been successfully used to explain how complex mechanical behaviors emerge
from multi-component interactions within cytoskeletal networks. Mathematical
descriptions of dynamic networks similarly provide insights into system-wide
information processing behaviors at the genomic and proteomic levels. The
challenge now is to use these tools to gain greater insight into the
underlying principles that govern cell function and, in the future, to unite
these approaches to create a more unified description of biological
regulation.
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Acknowledgments |
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