Developmental Engineering: A New Paradigm
for the Design and Manufacturing of Cell-Based Products.
Part II. From Genes to Networks: Tissue Engineering
from the Viewpoint of Systems Biology
and Network Science
Petros Lenas, Ph.D.,1,2Malcolm Moos, Jr., M.D., Ph.D.,3and Frank P. Luyten, M.D., Ph.D.4
The field of tissue engineering is moving toward a new concept of ‘‘in vitro biomimetics of in vivo tissue
development.’’ In Part I of this series, we proposed a theoretical framework integrating the concepts of devel-
opmental biology with those of process design to provide the rules for the design of biomimetic processes. We
named this methodology ‘‘developmental engineering’’ to emphasize that it is not the tissue but the process of
in vitro tissue development that has to be engineered. To formulate the process design rules in a rigorous way
that will allow a computational design, we should refer to mathematical methods to model the biological process
taking place in vitro. Tissue functions cannot be attributed to individual molecules but rather to complex
interactions between the numerous components of a cell and interactions between cells in a tissue that form a
network. For tissue engineering to advance to the level of a technologically driven discipline amenable to well-
established principles of process engineering, a scientifically rigorous formulation is needed of the general design
rules so that the behavior of networks of genes, proteins, or cells that govern the unfolding of developmental
processes could be related to the design parameters. Now that sufficient experimental data exist to construct
plausible mathematical models of many biological control circuits, explicit hypotheses can be evaluated using
computational approaches to facilitate process design. Recent progress in systems biology has shown that the
empirical concepts of developmental biology that we used in Part I to extract the rules of biomimetic process
design can be expressed in rigorous mathematical terms. This allows the accurate characterization of
manufacturing processes in tissue engineering as well as the properties of the artificial tissues themselves. In
addition, network science has recently shown that the behavior of biological networks strongly depends on their
topology and has developed the necessary concepts and methods to describe it, allowing therefore a deeper
understanding of the behavior of networks during biomimetic processes. These advances thus open the door to a
transition for tissue engineering from a substantially empirical endeavor to a technology-based discipline
comparable to other branches of engineering.
processes for bioartificial tissue formation. This methodology
is based on the empirical concepts of developmental biology
n Part I1we introduced the term ‘‘developmental engi-
neering’’ for a methodology to design in vitro biomimetic
that can be translated directly to process engineering con-
cepts and terms. According to this design methodology, the
overall process is assembled from a series of several sub-
processes, each one of these recapitulating one of the stages
of in vivo tissue development. These subprocesses lead to
the formation of intermediate tissue forms, some of them
1Department of Biochemistry and Molecular Biology IV, Veterinary Faculty, Complutense University of Madrid, Madrid, Spain.
2Aragon Institute of Health Sciences, Networking Center on Bioengineering, Biomaterials, and Nanomedicine, CIBER-BBN, Zaragoza,
3Division of Cellular and Gene Therapies, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Bethesda,
4Departement of Musculoskeletal Sciences, Katholieke Universiteit Leuven and Division Prometheus-Skeletal Tissue Engineering,
K.U. Leuven Research and Development, Leuven, Belgium.
TISSUE ENGINEERING: Part B
Volume 15, Number 4, 2009
ª Mary Ann Liebert, Inc.
exhibiting modular behavior, that is, structural stability and
robustness, determined by intrinsic factors, and therefore
could be used as building blocks of more complex tissues in
other processes; for example, the growth plate could be used
for the formation of osteochondral tissue.
Although the proposed methodology is sound, it makes
use of qualitative information regarding the developmental
phenomena. Therefore, the design of such processes requires
considerable efforts to select the information needed from the
existing literature of developmental biology. In addition, the
pieces of information encountered in various studies cannot
be processed and integrated efficiently as would be the case
for information that could be treated computationally. This
unavoidably limits the information that could be used in
process design to that which can be processed mentally by
the designer, with the danger that information or correlation
of data is left out, and the process has to be redesigned and
The current lack of rigorous formalization of the empirical
concepts prevents resolution of critical questions raised re-
cently in the literature. An important practical question is, for
example, the degree of match that is needed between the
in vitro and the corresponding in vivo processes.2It is not
feasible to transfer accurately in vitro in their entirety the
numerous interrelated in vivo conditions; we do not even
know what these are with accuracy and completeness. Are
all these conditions necessary, or is a subset sufficient to
direct the developing tissue into its natural pathway?3To
begin with, this requires a rigorous definition of what the
‘‘match’’ between in vivo and corresponding in vitro processes
means in measurable=computable terms, so that the degree
of match can be quantified and correlated. Here we will
show that this degree can be defined accurately in scientific
terms through the topological properties of networks of in-
teracting genes=proteins, that is, how they influence the ex-
pression of each other to form densely interconnected signals
that are activated as a whole and take over the develop-
mental process, resisting environmental noise as the macro-
scopic modular intermediate tissue develops. Next, for
engineers to develop robust manufacturing processes, they
will need to know what this relevant subset is and how to
use it to define a biomimetic process unambigouosly. We
will show that this is a much more tractable problem than
recreating all processes occurring in vivo, since not all of
these developmental mechanisms have the same importance
in optimizing the properties of a bioartificial tissue. We will
show that behind each mechanism is a network of interacting
genes overlapping with networks of other mechanisms, or
comprising part of a larger network, and that the preferential
activation of a network that corresponds to a particular
mechanism can best be addressed with detailed computa-
tional analysis of the activated gene networks to identify
conditions that could assure the modularity and robustness
of the activated gene network.
Practical questions relating the concepts of developmental
biology to design in vitro biomimetic processes cannot be
answered without a rigorous mathematical formalism unless
extensive experimentation is undertaken. One of the most
important of these questions is whether the intermediate
tissue form (or final product) displays modularity, that is,
relative independence from other tissues because of the in-
ternal interactions on which its structure depends (e.g., the
structure of the growth plate, which depends on the nega-
tive feedback loop of Indian Hedgehog (Ihh)=parathyroid
hormone–related protein (PTHrP) among differentiating
chondrocytes inside the growth plate). It is this property that
assures stability of such intermediates. Rational process de-
sign according to accepted engineering principles requires a
method to determine modularity from measurable or calcu-
lable variables instead of evaluating this property with la-
borious, empirical experimentation, which in any case would
not yield a useful quantitative model of the process. Mod-
ularity and robustness are equally desirable in the final
product, because it is destined to be implanted in vivo, and
therefore subject to uncontrollable disturbances in the local
environment. A final product not in a modular state might
well disintegrate after implantation, as shown by the failure
of chondroprogenitors to make stable cartilage in an in vivo
nude mouse model.4Indeed, the same questions are also
relevant for cells destined for implantation in a state short of
terminal differentiation (e.g., some products derived from
various types of stem cells). Even if further differentiation is
expected following administration, it is important that the
final product be in a stable, modular state corresponding to
the last production subprocess, as defined by the network of
activated genes. A rigorous definition of the final product
will be equally useful for regulatory purposes, including
such considerations as appropriate process controls, release
specifications that ensure product safety and effectiveness,
and design of comparability protocols to allow for post-
approval manufacturing changes. Here we will show how
modularity at the macroscopic level of a developing tissue or
cell arises from similarly modular design at the microscopic
level of networks of interacting genes, and that this modu-
larity can be expressed mathematically. Thus, we will be able
to define a stable, modular state with desirable properties in
precise mathematical terms, which in turn will allow the
questions posed above to be addressed. In conclusion, we
will reexamine the concepts for design of biomimetic pro-
cesses we used in Part I1from a different standpoint. Instead
of relying primarily on empirical approaches based on
known developmental pathways, we will examine how in-
teracting genes that form dense interconnected networks
can be treated computationally to answer process design
questions. We will see from where robustness of the devel-
opmental process arises, why some external perturbations
causing changes in the expression of some genes do not
disturb their natural developmental pathway, why other
perturbations do, and how the range of disturbances with no
effect on a developmental process could be determined ac-
curately instead of only through empirical experimentation.
We will then explore how computationally based process
design, evaluation, and optimization could be done to mini-
mize development time, increase accuracy, and assure the
fidelity of biomimicry needed for production of safe and ef-
We will look into the applicability of systems biology and
network science to the design of processes that are fully
biomimetic, yet simple and robust enough to be practical,
since in vivo developmental processes are by default robust.
We attempt this using the concepts and terminology of these
disciplines to position developmental engineering on a solid
scientific foundation. Network science, in general, and its
application to problems in biology, in particular, is still in its
396LENAS ET AL.
infancy. Nevertheless, we present critical aspects from these
two fields that can be applied to process design so that fur-
ther discussion between developmental biologists and tissue
engineers will focus on these aspects to help crystallize this
methodology into a practical approach with immediate
Structure of the article
In section 2, we introduce systems biology. The need for
the use of its concepts and methods arises from the fact that
cell, tissue, organ, or organism functions can not only be
attributed solely to single genes or linear cascades of gene
activation but also require extensive crosstalk and feedback
of signaling pathways that form networks of interacting
genes. Because of the high complexity of the interactions in
comparison with linear cascades, it is not possible to describe
the network dynamics only by intuition, because the changes
caused by the input (e.g., addition of growth factor) are
spread throughout the whole network omnidirectionally and
iteratively until the gene expression stabilizes. Therefore, the
concepts and mathematical methods used in systems biology
to decipher what the gene=protein network does and to what
cell=tissue function its operation corresponds are of great
relevance to tissue engineering.
In section 3, we present an example from the literature of
developmental biology (segment polarity pattern formation in
Drosophila), in which systems biology methods were used to
determine the gene=protein network responsible for the for-
mation of the segment polarity pattern, a macroscopic de-
velopmental module at the tissue level. This example not only
clarifies how systems biology is used in development for the
determination of modularity of tissue forms but also indicates
how it should be used to design in vitro biomimetic processes
that simulate development in vivo. The important issue here
is that the macroscopic modularity of tissue forms is attrib-
uted to autonomous (no external factors involved) operation
of a gene=protein network that is equally modular. In other
words, the macroscopic modularity of the engineered tissue
intermediate results from corresponding modularity of a set of
genes that define that state. This set of genes is relatively
isolated from the rest of the genes=proteins of the overall
network operating in the cell, but the connections between its
member genes=proteins are strong and not easily affected by
perturbation of its parameters and initial conditions.
In section 4, we use the methodology of systems biology
presented in section 3 to design a biomimetic process for
growth plate development, which we presented briefly in
Part I. The purpose of the design is to determine the initial
conditions and parameters of the process, so that the
gene=protein modular network responsible for the macro-
scopic modularity of the columnar pattern of the growth
plate will be activated, thereby establishing the spatially
differential gene expression pattern observed in the growth
plate. We will confine discussion of mathematical modeling
of gene=protein interactions to the interactions observed
in vivo during development, and thus restrict to processes
that are biomimetic by default. In general, these concepts do
not apply to one-step concerted manufacturing schemes,
which therefore will not be examined further.
In section 5, we deal with the problem of having several
stable states, instead of one, in which a gene network can
settle as it is usual in the process of cell differentiation. The
robustness of the gene=protein network, that is, its ability to
give the correct gene expression observed in differentiated
cells despite environmental disturbances, is reflected in the
properties of the mathematical model, which gives the same
correct solution despite changes in the initial conditions, as
mentioned in section 2. This solution represents a stable
state, called an attractor because it ‘‘attracts’’ initial condi-
tions that become finalized through network dynamics. Such
a representation is useful, especially when several stable
states coexist. Which one will be realized at the end depends
on the initial conditions that could direct cells to any of the
several different stable states. Several examples are given
from the literature referring to cell differentiation, where this
representation has proven useful in organizing and ex-
plaining experimental observations. It is again the stability of
the states and the determination of the initial conditions
leading to each one that can solve the problem of process
design in an accurate computational way.
In section 6, we introduce several concepts from the sci-
ence of networks and show how they are important in pro-
cess design. Network science, as systems biology, deals with
networks. However, although systems biology focuses on
component, for example, gene or protein, interactions in
networks from the point of view of regulatory mechanisms
as described in section 2, network science is a new scientific
field that examines the topology of the networks in a more
abstract form, trying to decipher common organizational
principles among diverse networks. Work done to date
suggests strongly that the mathematical behavior of net-
works will be instrumental in providing tissue engineering
with a solid theoretical background comparable to that of
other engineering fields. In any case, design questions could
be answered with both systems biology and network science.
For example, the mathematical model presented in section 2
from the point of view of systems biology, which includes
several regulatory mechanisms in the details of protein-to-
protein=gene interactions, has a corresponding abstract
model consisting only of gene-to-gene interactions described
exclusively by the network topology (see section 2).
In section 7, we present as an example an in vitro biomi-
metic process of pancreatic induction in endodermal cells by
mesoderm, where we try to answer the process design
questions from the point of view of network science.
2. Systems Biology Relates Cell/Tissue Functions
to Underlying Gene/Protein Interactions
Systems biology: From component interactions
to systems behavior
The genocentric paradigm of biology, which placed the
gene and its function as primary in biological studies, has
provided an enormous amount of data concerning the indi-
vidual cell components and their functions. However, only
limited information about functions can be extracted directly
from the genome. Biological functions cannot be attributed to
individual molecules but rather to complex interactions be-
tween the numerous components of a cell for cell functions
or between cells for tissue functions and so on, spanning the
levels of hierarchical organization of organisms. For exam-
ple, most diseases are not caused by a single gene defect but
rather by a malfunctioning network of interacting genes and
FROM GENES TO NETWORKS 397
their coded proteins. More than 100 genes have been iden-
tified as contributing to the coronary artery disease.5It is
therefore apparent that instead of dealing with single genes,
in this case we instead have to generate information con-
cerning the behavior of these genes in an ensemble of 100
interacting genes and proteins in a functional network. Even
collections consisting of a small handful of components may
display a behavior markedly different from those of the in-
dividual components. A very simple example is the mark-
edly cooperative binding of oxygen to a system of four
hemoglobin subunits, starkly different qualitatively from the
noncooperative binding displayed by myoglobin. Of more
direct relevance to cell fate decisions taking place during
development, a cascade of three MAP kinases displays
stimulus–response characteristics profoundly different from
those expected for a single component.6A model composed
of a set of ordinary differential equations based on accepted
principles of enzyme kinetics,7with parameters that may be
determined by experiment or estimated, predicts that in
contrast to a graded stimulus–response relationship for a
single component, the cascade exhibits switch-like, ‘‘all
off=all on’’ behavior.
Currently, there is a gradual emergence of a new para-
digm, which treats biological phenomena from the systems
point of view with a bottom-to-up approach, trying to de-
cipher the system properties from the properties and inter-
actions of the components,8instead of analyzing the system
to its components. The path for this change has already led
to the development of systems biology. Systems biology
seeks to understand how all the individual components of a
biological system interact in time and space to determine the
function of the system, be it cell, tissue, organ, or organism. It
makes use of the large amount of data from molecular bi-
ology and genomics to develop mathematical models of the
complex function of such systems. Systems biology will
change research practice and lead to the integration of in-
formation from the molecular up to the organism level. For
example, in experiments designed to elucidate the under-
lying pathophysiology of a disease, data collection and
interpretation are of equally important. For complete inter-
pretation in terms of physiology to be achieved, the use of
mathematical models to integrate huge amounts of data
describing gene expression, protein function, cell function,
and whole-body physiology are needed.9Clinical efficacy of
drugs can be also predicted using physiological models of
disease and disease processes.10Several applications in
health have been already published. Gadkar et al. have de-
veloped a mathematical model of the pathogenesis of type 1
diabetes, and they used it to study the effects of anti-CD40L
therapy, determining optimal treatment protocols.11Rull-
mann et al. have developed a mathematical model to describe
the inflammatory and erosive processes in afflicted joints of
people suffering from rheumatoid arthritis, including in this
several processes such as the life cycle of inflammatory cells,
endothelium, synovial fibroblasts, and chondrocytes, as well
as their products and interactions, since it is actually the
interplay between these processes that determines the clini-
cally relevant measures for inflammation and erosion.12The
authors used the model to predict the therapeutic effect of
modulating several molecular targets.
From the above examples, it becomes evident that the
methods of systems biology are relevant when we have to
integrate information at one level (e.g., gene expression), to
find answers to questions referring to a higher level (e.g.,
pathophysiology at the level of organism or drug effect to the
patient). There are many such questions in biology and
medicine that are now being approached with systems bi-
Systems biology in tissue (developmental) engineering
In the case of tissue engineering, such questions are also
critical for the process design, since the essence of the task is
to find in vitro conditions in which the integration of gene
and cell interactions will lead to differentiated cells that are
in a stable state, or bioartificial tissues that are functionally
integrated and robust entities. A random distribution of
cells in scaffolds, even if cell viability is retained by the so-
phisticated methods=tools of tissue engineering, is much less
likely to closely resemble an authentic living tissue that ex-
hibits properties that arise from sequential cell interactions
that occur during natural development and are qualitatively
different.14Internal integration of developmental modules
through cellular interactions that makes this cell collection
behave as a distinct unit signifies that these entities are au-
tonomous (see Part I). Such a living entity has distinct
properties, which are qualitatively different from the prop-
erties of its component cells. For example, the control of
growth plate elongation is not a chondrocyte property but a
property of the growth plate module arising from the inter-
action between chondrocytes participating in the negative
feedback loop of Ihh=PTHrP.15Similarly to the intermedi-
ate modular tissue forms, tissues in their final form constitute
integral entities with properties arising from interactions
between their cells. For example, glucose homeostasis in the
liver is a function of the liver as a whole, not of isolated
hepatocytes, which emerges from the metabolic cooperation
of glycolytic (periportal) hepatocytes, which take up glucose
during the absorptive phase, and gluconeogenic (perive-
nous) hepatocytes, which release glucose during the post-
absorptive phase.16Another example is the controlled release
of insulin, which is not a function of beta cells but a function
of the organized beta cells within the islet structure.17,18
Perhaps the clearest example is the neural tissue; its signal
processing functions are based on the topology of the syn-
aptic network instead of on single neurons.19
Systems biology in development
In development, systems biology aims to extract the gen-
eral design rules of the network of interactions of genes,
proteins, and=or cells that are responsible for integrating the
behavior of components—genes, proteins, or cells into the
system, the most important property being the modularity=
robustness of cell states or intermediate tissue forms.20This
effort has already provided important information allowing
detection of general architectural characteristics of the net-
works in development, such as the feedback loops that en-
sure the progression of development and the repressing
interactions that participate in spatial control.21As numerous
recent studies show (e.g., Refs.22–28), it is clear that systems
biology has already gained wide acceptance by develop-
For many signaling pathways controlling cell specifica-
tion, sufficient information now exists to construct and test
398LENAS ET AL.
models built from several individual pathways. These
models open the door to a rigorously scientific, quantitative
description of modular behavior observed in empirical ex-
periments by developmental biologists for decades. This will
allow direct experimental evaluation of modular properties
in a tissue engineered in vitro, instead of approaches relying
only on macroscopic phenomena, which might require ex-
tensive experimental work. This is no longer just a theoretical
possibility: robust models have predicted behavior of sys-
tems as complex as the developing fly and frog embryo re-
liably23,24; these will be discussed further. This suggests
strongly that existing technology can model individual de-
velopmental modules determined to be needed in a given
manufacturing scheme to guide the nature and extent of
externally imposed controls and also set limits for measur-
able parameters consistent with process design objectives.
Below, we will describe how systems biology is applied to
development in vivo, what kinds of questions can be an-
swered and what methods are used (section 2), and then how
to transfer these concepts and methods to the design of
in vitro biomimetic processes (section 3), thereby transform-
ing the questions of section 2 to process design questions.
The example presented in section 3, selected because of its
simplicity, makes clear how a multicellular system can be
robust because of the cell-to-cell interactions that maintain
intracellular gene=protein interaction networks leading to
spatially differentiated gene expression. This is the aspect of
development most relevant to developmental engineering.
The model presented is based on mathematics no more
complicated than ordinary differential equations. This makes
the incorporation of process design parameters, such as ini-
tial cell concentrations and cell-to-cell interaction through
diffusing signaling proteins, a fairly straightforward exercise.
3. The Mathematical Properties of Interacting Gene/
Protein Networks Provide a Rigorous Formalism
of Developmental Modularity That Is Suitable
for Process Design
Intermediate modular tissue forms are the main targets of
process design in developmental engineering. If robust forms
appear in an in vitro process, then the process is biomimetic
in that it has emulated successfully the sort of modularity
observed in vivo. They can therefore easily be kept stable
in vitro without the need of elaborate explicit external control
because their structure depends on intrinsic factors, and
therefore remains stable in the face of environmental noise
unavoidable in an in vitro environment. They can be further
assembled with other tissue forms as building blocks for the
formation of more complex tissues. The major process design
question becomes ‘‘how can we ensure that robust tissue
forms appear during the in vitro process and, if they do not,
how should we modify the process design’’? In other words,
under which conditions do stable, modular tissue forms
appear and persist? Though modular behavior is not neces-
sarily related to easily observable macroscopic patterns, in
some cases, macroscopic observations of process intermedi-
ates may provide evidence of modularity. One example of
this is the columnar pattern of the growth plate. However,
this is a a posteriori information, helping to confirm appro-
priate process design, but not facilitating it. More useful in-
formation would be provided by methods that express
modularity and robustness in a formulation suitable for the
connection of design parameters to robustness of bioartificial
tissue and thus determine the values of parameters and ini-
tial conditions of the in vitro process that leads to a modular
and robust formation and maintenance of bioartificial tissue.
The segment polarity pattern of Drosophila
is formed by the operation of a modular,
robust gene=protein network
A particularly instructive example of a developmental
module where such a formulation of modularity=robustness
was achieved is the segmentation appearing during devel-
opment of the fruit fly, Drosophila, one of the best understood
developmental mechanisms. It will be apparent through the
analysis of this example that macroscopic modularity of in-
termediate developmental multicellular forms observed
empirically cannot be attributed to single genes or signaling
pathways acting as separate entities. Modularity, as a global
property of the spatially extended biological system, arises
instead from the way members of a particular set of genes
influence expression of other genes in the set, forming a
complex network of mutual interactions. The gene expres-
sion network of one cell extends its action to neighboring
cells, influencing their gene expression and activating vari-
ous signaling pathways through secreted proteins. In turn,
these cells respond to the first, activating its signaling path-
ways in a specific way, so that finally the gene expression is
stabilized. The gene interaction network therefore spans the
system extended and coordinated throughout the whole
macroscopic developmental module, and determines which
genes will be expressed and in what locations in the devel-
oping organism. Just as the developmental module is robust
macroscopically, the same robustness is exhibited by the
gene=protein interaction network in its operation autono-
mously, keeping active the interactions of its components
and stabilizing the spatially differentiated gene expression
pattern. Transferring the microscopic robustness of the gene
and signal pathway network to macroscopic robustness
observed experimentally in this way connects measurable=
calculable variables referring to the gene=protein network to
phenomena that can be observed directly. Thus, one can fi-
nally connect process design parameters with the robustness
of the tissue form using a mathematical model and predict
the necessary modifications in the in vitro process.
The various parts of the body of insects develop on par-
ticular segments, layers of cells that appear during embryo-
genesis. The genes expressed in each segment specify the
correct number of body parts and the correct polarity of each
one. In Drosophila, a complex network of gene interactions
converts a single-celled Drosophila egg to a multicellular
embryo with 14 segments, forming a spatial pattern of par-
allel stripes with each segment bounded by a stripe of cells
expressing the engrailed gene, en (Fig. 1a). Various sets of
genes are expressed in space differentially, in consecutive
stages of development, before the final pattern of 14 seg-
ments appears. At each successive developmental stage, the
pattern of differential gene expression becomes more precise,
with the expression of genes at any given stage controlled by
toses expressed in the previous stage This sequential pattern
of events, characteristic of developing systems and also of
mathematical models, that describe them (see below) is
FROM GENES TO NETWORKS 399
biology usually involves the dissociation of the putative
developmental module and observation of the process in
isolation from the rest of the embryo.108A close interaction
between the efforts of biologists to isolate the modules and of
engineers to synthesize them can speed up the process of
module identification and its use in tissue engineering pro-
One of the primary tenets of biologics regulation is the
concept of the well-controlled manufacturing process. Pre-
viously, control of the manufacturing process has been ex-
erted externally. For the products discussed here, the
concept of modularity opens the door to a new paradigm
based on the robust, self-regulating nature of modular de-
velopmental systems, which we have demonstrated can be
expressed mathematically and treated computationally. We
propose that if the parameters that define the robustness of a
given product can be defined adequately during process
development and validation studies, the manufacturing
process, to a significant degree, can control itself. Thus, de-
sign efforts might focus in part on identifying sets of pa-
rameters adequate to define attractor basins sufficient to
ensure convergence toward the desired cell and tissue fate.
At a minimum, these parameters can be used to evaluate the
process design space and develop classical process controls.
This approach is fully consistent with existing regulatory
paradigms. In doing so, predictability of product develop-
ment cycles, including registration with regulatory authori-
ties, might be enhanced substantially. As the concept of
correspondence between modularity and robustness of sig-
nal pathway network topology and stability, reproducibility,
and robustness of macroscopic tissue states becomes more
widely appreciated, the approaches outlined here may ulti-
mately be used not only to design manufacturing processes
but also to aid in their validation (e.g., by scanning wide
parameter values computationally to provide assurance of
The objective of tissue engineering is to produce highly
biomimetic therapeutic products with superior clinical per-
formance by proposing the important phenomena that
should be examined thoroughly and how experiments should
be designed for these phenomena to be observed (e.g., self-
organization to tissue structures). Other fundamental aspects
of tissue development, further to the ones mentioned, should
be elucidated and incorporated gradually in the biomimetic
process design as new information becomes available.
From the examples presented, it is clear that investigators in
the field of tissue engineering have started to recognize the
paradigm shift from molecular to modular biology.109The
field should therefore take the next step by preparing itself for
the corresponding technological paradigm shift,110directing
its focus to bioartificial tissue formation guided by gene net-
work studies. We believe that a paradigm designed to place
the field of tissue engineering on a solid theoretical and
technological foundation by synthesizing contemporary in-
sights into developmental biology, network science, systems
biology, and process design engineering will both create
realistic expectations in the practitioner and patient commu-
nities and promote steady progress toward dramatically im-
proved products to address currently unmet medical needs.
Partial support from the European project LSHM-CT-2007-
037862 is gratefully acknowledged. We would like to thank
Eleni Nicodemou-Lena, Jan Schrooten, and Jeroen Eyckmans
for numerous critical discussions on the biological aspects of
the article; Andreina Elena Lanzara for the preparation of the
figures and meaningful discussions for the final design of the
figures;andSteve Bauer,CaitilinHamill,Deborah Hursh,and
Terrig Thomas for critical review of the manuscripts.
No competing financial interests exist.
1. Lenas, P., Moos, M., and Luyten, F.P. Developmental
manufacturing of cell-based products. Part I: from three-
dimensional cell growth to biomimetics of in vivo devel-
opment. Tissue Eng 15B, 381, 2009.
2. Ingber, D.E., Mow, V.C., Butler, D., Niklason, L., Huard, J.,
Mao, J., Yannas, I., Kaplan, D., and Vunjak-Novakovic, G.
Tissue engineering and developmental biology: going bio-
mimetic. Tissue Eng 12, 3265, 2006.
Table 2. The Interrelation of the Concepts of Developmental Biology, Engineering,
and Systems Biology=Network Science
c Developmental biologyEngineering Impact in process Systems biology=network science
1Robustness Stability; reproducibilityManufacture; regulatory
Direct assessment of
Robust gene network; convergence
to attractors; scale-free network
Sequential activation of gene
networks; trajectory through
Gene network activation by
initial conditions set from
previous stage; switching
From cellular to multicellular
spatially extended gene networks
2Sequential stagesObservability; controllability
3 Path dependenceSemi-autonomy
5 Uncoupled interfaces Flexibility and cost
Modular gene network
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Address correspondence to:
Frank P. Luyten, M.D., Ph.D.
K.U.Leuven Research and Development
Division Prometheus-Skeletal Tissue Engineering
Department of Musculoskeletal Sciences
Katholieke Universiteit Leuven
Received: July 7, 2009
Accepted: July 9, 2009
Online Publication Date: September 14, 2009
422 LENAS ET AL.