Chapter 1
Systems Biology of the Beta-cell - Revisited
Flemming Pociot
‘Nature is fond of hiding herself’, Heracleitus.
Abstract The insulin secreting beta-cell is one of the most specialized cell types.
Almost the entire intracellular machinery is directed towards maintaining glucose
homeostasis. It has been a focus of intensive research for several decades, which
has culminated in the characterization of processes involved in synthesis and
secretion of the hormone in considerable details. The stage of knowledge of this
cell is reflected in a substantial variety of mathematical models and numerical
simulations that aim to explain major aspects of the beta-cell function (see other
chapters). These models, though answering many questions about the beta-cell
function, remain to be only isolated attempts and have not yet been integrated
into a single more unified model. Thus, there is a need to apply a holistic
approach.
Keywords Beta cell, Diabetes, Genetics, Networks, Systems Biology,
Flemming Pociot, Hagedorn Research Institute, Niels Steensensvej 1, DK-2820
Gentofte, Denmark. E-mail: fpoc@hagedorn.dk
1.1. Introduction
Cells are complex biological systems that consist of components that interact
with each other, under regulatory strategies, in response to internal and
environmental signals.
A biological system can be viewed as a set of diverse and multi-functional
components (genes, gene products and metabolites), which population level
change over time in response to internal interactions and external signals. The
interactions among the system components reflect the value one component has
on values of other components. These interactions are usually governed by a set
of biophysical laws, most of which are only partially known. Modeling involves
inference of both the interaction map (structural inference) of the system and
the mathematical formalisms that approximate the dynamic biophysical laws the
system follows (dynamic inference) (1). Both of these approaches aim at
characterizing the system at different levels of abstraction and neither of them
are trivial.
System biology requires exact knowledge of magnitudes of kinetics parameters
that characterize the components involved. This knowledge has so far been
incomplete, thus limiting the use of all models suggested. Further, development
of such models into the direction of systems biology requires that the model in
effort be closely tight to innovative and exact experimentation.
Understanding how cellular components interact in time and space is crucial for
deciphering the functions inside a living cell. Technological advances make
simultaneous detection of thousands of biological variables possible. Microarrays
2
are used to measure expression of thousands of genes simultaneously, yeasttwo-hybrid (Y2H) and affinity-purification mass spectrometry (AP-MS) assays are
used to map protein interactions, and ChIP-chip methods are used to identify
interaction between proteins and DNA, just to name a few.
The challenge will be to integrate such existing data with data such as the role of
ion channels in creating the electric activity in the beta-cell membrane, the traffic
infusion of insulin granules with plasma membrane, the role of glycometabolism
and mitochondria, to obtain precise data from living cells, and to include the
dynamic nature of these processes (see other chapters), Figure 1.1. That will
allow us to formulate a comprehensive and robust model of the main networks of
biochemical and physical processes involved in insulin secretion. Such a model is
expected to be a valuable tool in understanding beta-cell function and the
development of disease – i.e. diabetes – related to beta-cell dysfunction. It may
also open avenues to finding novel ways of treatment modalities. Additionally,
the models may be of use for testing effects of various pharmacological agents.
(FIGURE 1.1; to be placed in 30 pages color insert. Consider black and white
version here)
1.2 The Beta Cell and Diabetes
Diabetes is common and getting more common and is now one of the most
common non-communicable diseases globally.
Diabetes is a life-threatening condition. More than 250 million people live with
diabetes and the disease is associated with enormous health costs for virtually
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every society. It is estimated that 3.8 million men and women died from diabetes
in 2007, more than 6% of the total world mortality. It is further estimated that
the number of people with diabetes will reach 380 million in 2025 (2). This
means that 1 out of 14 adults world-wide will have diabetes in year 2025. It is
estimated that the world spent at least USD 232 billion in 2007 to treat and
prevent diabetes and its complications (3; 4). Diabetes is certain to be one of the
most challenging health problems in the 21st century.
Diabetes mellitus is classified on the basis of etiology and clinical presentation of
the disorder into 4 types: 1) type 1 diabetes, 2) type 2 diabetes, 3) gestational
diabetes mellitus, and 4) other specific types (5; 6).
In type 1 diabetes the beta cells of the pancreas are destroyed by the immune
system for reasons not fully understood and little or no insulin is produced. The
disease can affect people of any age, but usually occurs in children and young
adults.
Type 2 diabetes is characterized by insulin resistance and relative insulin
deficiency. The specific reasons for developing these abnormalities are not yet
known. In type 2 diabetes, beta-cell deterioration occurs due to a combination of
genetics, low-grade inflammation, and glucose- and lipo-toxicity (7; 8). The
diagnosis of type 2 diabetes usually occurs after the age of 40 years, but could
occur earlier especially in populations with high diabetes prevalence.
Gestational diabetes mellitus is a carbohydrate intolerance of varying degrees of
severity, which starts or is first recognized during pregnancy. Women who have
had gestational diabetes mellitus have increased risk of developing type 2
diabetes in later years (9).
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Other specific types of diabetes includes monogenic forms with most of these
affecting beta-cell functions (i.e. maturity-onset of diabetes in the young
(MODY)) (10).
The insulin producing tissue is known as the islets of Langerhans. There are
approximately 1 million islets in a normal human pancreas. They are named after
the German pathologist Paul Langerhans (1847-1888) who discovered them in
1869 (11). There are five types of cells in an islet where the most abundant (6080%) cell type is the beta cell that produce insulin (12). The glucose metabolism
is under strict control. Despite intake of large amounts of carbohydrates or
several days of starvation, plasma glucose levels are maintained within a very
narrow window. Insulin is a key regulator of the glucose homeostasis and has
several important metabolic effects to assist this function, Figure 1.2.
(FIGURE 1.2; Insert black and white version here)
The cell can be considered an open system exchanging material with its
environment. In this sense, a living entity has a dynamic relationship with its
surroundings, and to fully understand beta-cell function, it might be critical to
study simultaneously the other cell types of the islet of Langerhans. This has not
been addressed thoroughly, i.e. few studies have evaluated beta-cell function as
part of a biological system comprising all islet-cell types, despite the fact that
often islets are used for experimental studies as opposed to isolated beta cells.
The cell provides spatial organization through its membranes and other
structures and much of this is not encoded in DNA. It could be argued that
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various interactions between molecules are defined by the laws of chemistries,
and that if one can determine which biomolecules are supplied by the information
given in DNA, then one can deduce the behavior of the cell.
1.3 Genetics of Diabetes – From GWA to NWA Studies
Both type 1 and type 2 diabetes are polygenic, multifactorial diseases, i.e.
several genes contribute to disease risk, which in combination with
environmental factors may cause clinical disease, whereas MODY forms are
monogenic.
Recently, very large genetic studies, so called genome-wide association (GWA)
studies, have revealed a large number of susceptibility genes in both type 1 and
type 2 diabetes (13; 14). Interestingly, several of the potential candidate genes
might be implicated in beta-cell function. In MODY forms all known disease genes
are directly involved in beta-cell function (10). Other chapters deal with this in
details. Here it suffice to say that the fundamental aim of genetics is to
understand how an organism’s phenotype is determined by genotype and implicit
in this is predicting how changes in DNA sequence are affecting phenotypes,
Figure 1.3.
(FIGURE 1.3; Insert black and white version here)
GWA studies encompass a number of challenges, which include 1) statistical
power; 2) biological interpretation, e.g. which gene is the ‘right’ one; 3) in
complex traits there may be many marker interactions (15). The latter has not
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been thoroughly addressed in current GWA scans. Nevertheless, GWA studies
provide a rapid and high coverage method to map genetic interaction networks
at large scale, although this is often not recognized. For detailed discussion of
GWA studies data, see chapter by Lyssenko and Groop.
Additionally, a simple linear interpretation of DNA information may no longer be
sufficient. For example much of the genome is transcribed producing many
functional non-coding transcripts (16; 17); and higher-level structures and
processes in the cell, such as nuclear organization, the structure of DNA, and
chromatin remodeling, are intrinsic to transcription regulations (18). So although
DNA is vital and central to heritable information, this information has limited
meaning except in the context of the cell and the additional rules and codes that
it provides.
A new approach to classify human disease that both appreciate the uses and
limits of reductionism and incorporate the tenets of the none-reductionist
approach of complex system analyses is therefore essential. Obviously, all
disease phenotypes reflect consequences of variation in complex genetic
networks operating within a dynamic environmental framework. Cellular
networks are modular, consisting of groups of highly interconnected proteins
responsible for specific cellular functions. Disease represents the perturbation or
breakdown of a specific functional module caused by variation in one or more of
the components producing recognizable developmental and/or physiological
dynamic instability. Such a model offers a simple hypothesis for the emergence
of complex or polygenic disorders: A phenotype often correlates with inability of
a particular functional module to carry out its basic function. For extended
modules, many different expression combinations of perturbed genes might
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incapacitate the module, as a result of which variations in expression of different
genes may to lead to the same clinical phenotype. This correlation between
disease and functional modules, i.e. moving from GWA to NWA (network-wide
(pathway) association) studies can also help understanding cellular networks by
helping us to identify which genes are involved in the same cellular function or
network module (19; 20). Importantly, this association of disease with functional
modules may also influence our choice of rational therapeutic targets. It may
also tell us which perturbations are deleterious and which are not.
1.4 Why Systems Biology?
A system biology approach aims to devise models based on the comprehensive
qualitative and quantitative analyses of diverse constituents of a cell or tissue,
with the ultimate goal of explaining biological phenomena through the interaction
of all its cellular and molecular components.
This is based on the analysis of large-scale datasets, such as those generated by
DNA microarrays and proteomics. The model is subsequently refined through
introduction of perturbations in the system and a new round of large-scale
gene/protein analysis. System biology is thus an interactive process in which
researchers propose models based on large datasets, make predictions departing
from the model, and then conduct additional large-scale experiments to test the
prediction and refine the model.
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As system biology progresses, multifactorial diseases, such as diabetes may be
understood in terms of failure of molecular components to cooperative properly.
Consequently, multifactorial diseases may be approached and treated in a much
more rational and effective way (21). The starting point for this is the notion that
any biological property is the result of the interaction in time and space of a large
set of different molecules, cells, organs and/or organisms. The iterative cycle of
model-driven experimentation with experimental data-driven modeling, in
combination with novel systems analysis tools, constitute the very heart of
system biology. Biological systems are endowed with two features of great
interest: function as an emergent property and robustness (22). A function
derives as an emergent property when it is not present in the individual
components of the systems, but emerges when the various parts interact
following an appropriate organizational design. Robustness is the ability to
maintain stable functioning despite internal and external perturbation.
Robustness is not absolute and cells are, in general, robust in the face of
frequently occurring perturbations but fragile when dealing with rare events.
Moreover, robustness has a cost in terms of allocation of resources, e.g. to
glucose sensing, insulin synthesis and secretion. The evolutive acquisition of
robustness appears to be one main source of complexity for biological systems.
1.5 Systems Biology – How?
To identify the structure and function of intracellular networks, it is important to
keep in mind that the beta cell is a well-organized system having its own
9
components strategically positioned and regulated in a functionally independent
modular manner.
This form of internal organization has been selected throughout evolution and
further by differentiation to successfully carry out the increasing complexity of
maintaining glucose homeostasis - but also as a ‘safety switch’ where diverse
reactions can take place without being deleterious to the cell. Connectivity
among such functional modules is the key feature that makes the cell operate as
an integrated system, allowing internal functions to influence one another.
Identifying functional modules is thus crucial for understanding intracellular
functions.
In systems biology, basically two main approaches are considered for this, the
bottom-up and the top-down approaches (23; 24). The bottom-up approach
(from modules to networks) is basically a reductionist method and strongly
promoted by the concepts and technology of biochemistry and molecular biology.
The concept of this approach is the idea of initially aggregating detailed biological
knowledge about individual components and quantitative information about the
molecular interaction into appropriate molecules and then to interconnect these
into architectures suitable for holistic analysis of the system of interest.
Depending on any frame work of choice, e.g. deterministic or stochastic,
continuum or discrete modeling approaches, the first step involves verbal level
modeling, where necessary information about the system is collected. This is
followed by the model setup and subsequent solution of equations, performing
parameter sensitivity analysis. This process yields sufficient information about
new experimental designs, which can then be used for the quantification of
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individual components and their dynamic behavior. Parameter estimation can
then be followed which paves the way for the testing and validation of the model.
The final result is cycled until a satisfactory result is obtained. This modeling
cycle is the key to the success of bottom-up or reductionist model building.
One example of the bottom-up approach to systems biology is the silicon cell
program (see chapter 25 and http://www.siliconcell.nl) (25). Here are e.g.
metabolic pathways like glycolysis models built from kinetics rate laws in vitro. In
vitro measurements of enzyme kinetics allow for an exact characterization and
manipulation of quantitative parameters and will yield a reasonably steady-state
depiction of glycolysis. Although, the reductionist approach is powerful in building
logically simple hypothesis and devising ways to test them, it is very difficult to
reconstitute a model for a whole biological system by combining the pieces of
information it generates. Using a reductionist approach, the entire system model
must be reconstituted by combing information about every molecular step in the
system. Any missing pieces of information may block the reconstitution of the
system. Therefore, the bottom-up approach requires essentially complete
information including the dynamic behavior of each step, to build a system
model. Also, reductionism by definition focuses on information essential to a
simplify question and intentionally discards extra information. The major
difficulty in applying this strategy, however, is the definition of criteria for the
demarcation of these modules to guarantee a certain level of autonomy. For the
time being these modules are most often defined from an empirical, text book
driven decomposition of the network into subsystems performing particular
physiological functions. Because of the absence of a rigorous definition of these
subunits the question remains whether the fundamental organization of the
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biological networks or multiorgan systems is modular at all or distributed, or
whether it is probably best described as being a little bit of both.
The top-down approach is basically linked to a high throughput reductionisms
(e.g. assigning biological function to the genome of an organism). Another
aspect, however, is characterized by exhaustive, simultaneous descriptions of
biological systems such as global profiling (transcriptome, proteome,
metabolome, interactome, fluxome etc.). Such broad and detailed information
about a biological system provide us with a view different from reductionism – a
view of how the system behaves as a whole. In a top-down approach, the
primary focus is planning an execution of large-scale experiments to generate a
lot of information about the genome, proteome, metabolome etc. The
experimental design is therefore a crucial part that determines whether this
strategy will be successful or not. Perturbation experiments are performed and
followed by the design of further experiments, new time theories etc. Next step
involves the large-scale data generation of “omics” data and following data
analysis new networks are inferred, which give an idea about the structure and
interaction between the players in the system and a general impression of its
performance.
There is also a possibility of studying the modularity in such reconstructed
networks by studying the interaction of sub-networks within the networks and
pinning down their autonomous nature or lack of it. The approach is useful but
only if its pitfalls are appreciated. One example is the use of Bayesian networks
(which assumes the absence of feedback) for those biological regulatory
networks that are known to abound in feedback. A second example is the
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common description of cellular regulation only in terms of gene networks,
although it is clear that proteins, signal transduction and metabolism are
involved in this regulation in addition to mRNA. An example of a top-down
approach is the study of the gut microbial-mammalian interactions on the
metabolic profiles of the host organism (26). Here, the application of
metabonomics has revealed specific metabolic phenotypes associated with
different microflora (24). This illustrates that an important source of metabolomic
variability in the host will be missed if only the host genome is studied.
However, there should be no controversy about the need of a mixed
complimentary approach, but only about the relative importance in context with
the existing knowledge related to a given problem. The two models pursue
different goals: A bottom-up model is constructed to be locally correct
(describing individual reactions by correct rate laws and parameters), while a
top-down model, on the other hand, is optimized for a good global fit to in vivo
behaviour. In a model of limited size, it is unlikely that both requirements will be
fulfilled at the same time. Once the problem has been formulated, the purpose
and the scope of the model and the related known information about the
different aspects of structure, and regulation of the system can be studied. If the
known outweigh the unknown then the bottom-up approach can be taken with
confidence. But in the case where there are a large number of unknowns the topdown approach is the logical way to bridge the gap between the knowns and the
unknowns. The ultimate goal of such a hybrid approach is that the
characterization of the behavior of parts of the system should be consistent with
the expected and/or observed behavior of the system as a whole. The top-down
13
approach is to deconstruct the system into smaller parts. The bottom-up
approach is to reconstitute elemental steps into larger parts. If the result of
these approaches meets in the middle, and if they are consistent in terms of
links between modules, multiple functions of elements etc., we can be confident
that we are on the right track. In other words, we can use information from the
reductionist approach as constraints in large-scale model building and vise versa.
This endeavor is possible only with strong coordination between experimental
and modeling efforts. It is important that both areas are tightly linked and
function in tandem as one single effort.
Biological networks that have been studied extensively usually consist of many
intermediate steps between the initial response (to a signal) and the outcome.
We do not know all these steps and components for any complex system, but a
simplifying assumption can be made by recognizing that different parts of the
network operate at a different speech. For example, kinases operate on a much
faster time scale than gene regulation. Then when interpreting gene regulation
data one can assume that the cell signaling network has already responded to
the condition (is at steady stage) and that one is assaying the events relevant to
gene regulation. Also there might be events taking place at the same time scale
that are not being measured, for example chromatin modification during a
transcriptomic experiments.
1.6 Challenges of Methodological Advances
14
A further problem is that the controls put in place are specific to the lab and even
the series of experiments that a scientist may conduct. This creates a problem
for reusing knowledge of how systems behave from experiment to experiment.
Examples of data standardizations relevant to systems biology include Gene
Ontology (GO) for describing gene function (27), Minimal Information About
Microarray Experiments (MIAME), Systems Biology Markup Language (SBML)
(28) and Cell Markup Language (CellML) for describing biomolecular simulations
(29), and MIBBI (28; 30).
A prerequisite to system biology is the integration of heterogeneous
experimental data, which are stored in numerous life-science databases. The
most important tool for reaching and understanding of biology at the level of
systems is the analysis of biological models. The basis building blocks for these
models are existing experimental data, which are stored in literally thousands of
databases. It might be a common misconception that the main problems of
database integration are related to the technology that is used for these
purposes, but it has been argued that although the mastering of such technology
can be challenging, the main problems are actually related to the databases
themselves (31). These problems include technical problems as web-access
problems, problems with data extraction and lack of software interfaces,
problems with data preprocessing, in appropriate conceptualizations, and
problems with the content of databases. However, also social issues and political
obstacles may be responsible for some problems with life sciences databases
(31).
15
The dynamics of the system can be mathematically modeled, allowing prediction
of the response of the system to genetic and environmental perturbations. Data
can be used to construct co-expression networks in which the notes are
transcript levels and the edges represent correlations between transcripts. Such
model is based on the assumption that genes with correlated expression are
likely to be functionally associated (although other explanation such as linkage
and/or linkage disequilibrium or the impact of the clinically treated cell could also
result in correlations). It is also clear that many functionally associated genes
would not be correlated given that much regulation is post-transcriptional. Thus,
such networks are clearly approximations of the underlying biology, and
integration with other datasets and approaches are important. Nevertheless,
groups of genes or modules identified by co-expression modeling are significantly
enriched for functionally related genes (32).
1.7 Summary
We believe that considerable effort now should be devoted to examine the
regulated exocytosis in pancreatic beta-cells by a broad perspective rather than
focusing narrowly on individual pathways or components. This will require the
application of interdisciplinary approaches including genetics, genomics,
proteomics, metabolomics, physiology and mathematical modeling. This should
eventually enable the development of a holistic picture of the beta-cell,
integrating information from multiple scales, including genes, a transcript,
proteins, organelles, cell and tissue communications.
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1.8 Understanding Pancreatic Beta-Cell Death in Type 1 Diabetes – A
Systems Biology Approach
Clinically, type 1 diabetes is diagnosed when 70-80% of beta-cells have been lost
due to immune-mediated destruction (33). The slow destruction of beta-cells,
coupled with the autoimmune nature of the disease suggests that type 1
diabetes is potentially preventable (34).
How well do we understand how beta-cells are progressively killed by the
immune systems in type 1 diabetes to allow a targeted intervention to prevent
beta-cell loss? And is current research approaches focusing on individual
pathways adequate to inform our understanding of this? Currently, the answer to
both questions is unfortunately “no”.
When beta cells are exposed in vitro to cytokines they present functional
changes which are comparable to those observed in pre-diabetic individuals, i.e.
a preferential loss of the first phase insulin release in response to glycose,
probably caused by decrease in the docking and fusion of insulin granules to the
beta-cell membrane (35) and a disproportionate increase in the proinsulin/insulin
ratio (36). Cytokines induce stress-response genes that either protect or
contribute to beta-cell death. They also down-regulate genes related to beta-cell
function and regeneration, and trigger the expression of chemokines and
cytokines that will contribute to the attraction and activation of immune cells.
In a top-down approach gene expression studies have identified nearly 700
genes that are up- and down-regulated in purified rat beta-cells or insulin
17
producing INS-1E cells after exposure to cytokines and nearly 2,000 genes
modified by cytokines or viral infection in human pancreatic islets (37; 38)
(http://t1dbase.org/page/bcgb_enter/display/).
Two transcription factors play key roles for cytokine-induced apoptosis, namely
NFκB (induced by IL-1β, TNFα) and STAT1 induced by IFNγ (39). Prevention of
NFκB activation protects beta cells in vitro against cytokines-induced apoptosis,
whereas in vivo NFκB blocking protects beta cells from diabetogenic agents (40).
Intriguingly, NFκB has mostly anti-apoptotic effect in other cell types (41), and
recent observations in non-obese diabetic (NOD) mice indicate that inhibition of
NFκB activation in beta cells accelerates the development of diabetes (42).
Comparison between IL-1 induced NFκB in beta-cells (where the transcription
factor has pro-apoptotic effect) and fibroblast (where it has anti-apoptotic effect)
show that cytokine-induced NFκB activation in insulin producing cells is more
rapid, intense and sustained than in fibroblast, leading to a more pronounced
activitation of the downstream genes (43). These findings suggest that the NFκB
mediated anti- or pro-apoptotic effect in vitro are cell and context dependent.
Activation of NFκB in beta-cells in vivo will play a pro- or anti-apoptotic role
depending on the animal model of diabetes studied and possibly on the time
window utilized for the NFκB inhibition.
Systemic STAT1 depletion protects against diabetogenic agents (44) and
spontaneous development of diabetes in NOD mice (45). This suggests an
imbalance between deleterious and protective mechanisms leads to progressive
beta-cell loss in type 1 diabetes and that this, to a large extend, take place inside
the beta cells and affect the interaction with the invading cells from the immune
system. Thus, it can be speculated that prevention of human type 1 diabetes will
18
require hitting multiple targets, i.e. preventing activation of pro-apoptotic betacell gene networks, supporting beta-cell defense/regeneration and
arresting/regulating the autoimmune assaults.
Furthermore, the mathematical language has been applied to describe the
dynamics of the early pathogenetic events where interaction between the
immune system and the beta cell leads to beta-cell dysfunction and development
of type 1 diabetes (46). Still, these attempts are very simple, but seem
promising in describing the multifactorial nature of the disease. A mathematical
formalism allows for a more comprehensive description of the biological problem
and can reveal non-intuitive properties of the dynamics.
Also animal models of human type 1 diabetes have served a prominent function
in the development of current ideas of pathogenesis and approaches to therapy.
Despite translational obstacles in going from observations in rodents to human
studies, animal models may still be useful in a system biology approach in order
to identify disease-relevant biological pathways and/or interactions between
such. The following example serves to illustrate the complexity of spontaneous
disease development in one such model, i.e. the BioBreeding (BB) rat, and how
simple intervention (perturbation of disease network) may lead to extensive
changes in beta-cell protein expression pattern. A transplantation model was
used since destruction of islets in situ in the pancreas is not synchronized in time
and space, and to enable proteomic studies of diabetes development and islet
destruction in vivo. Extensive proteomics work has been performed using this
model (47). Although clinical symptoms of (type 1) diabetes in are abrupt in both
man and rodents, the clinical presentation is preceded by a period of variable
length, during which the islets are inflamed individually and gradually destroyed.
19
In other words, the destruction of islets in situ in the pancreas is not
synchronized in time and space. The spontaneous development of diabetes and
destruction of islets in situ are mirrored in the transplanted islets, which can be
excised for further studies. To provide minimal influence on the spontaneous
diabetes development only 200 neonatal BB-diabetes prone (DP) rat islets were
transplanted under the kidney capsule of BB-DP rats (syngeneic transplantation)
(47).
Proteome studies demonstrated that beta-cell destruction could be characterized
by a limited number of highly significant modules of co-expressed proteins, see
Figure 1.4a. Interestingly, these islet protein expression patterns were predictive
also for diabetes development as they could identify and differentiate nondiabetic rats with ‘diabetic’ and ‘non-diabetic’ protein expression patterns, 1.5.
(FIGURE 1.4; to be placed in 30 pages color insert.)
In a separate study it was concluded that prophylactic insulin treatment
administered in this transplantation model considerable decreased the incidence
of diabetes and significantly reduced inflammation of the islets in situ and in the
islet graft (48). Interestingly, prophylactic insulin treatment led to a substantial
perturbation in protein expression patterns, Figure 1.4b. This illustrates the
importance of analyzing modules and network interactions of genes and proteins
in order to understand and characterize beta-cell function.
(FIGURE 1.5; to be placed in 30 pages color insert.)
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1.9 Conclusions
Since complex intracellular systems are often composed of smaller, functionally
independent sub-network structures, this chapter has discussed different
approaches that partition a system into functional modules or reconstruct it
based on the interaction between these entities. Different algorithms may result
in different compartmentalization of the underlying structure as a whole, but
when combined effectively, these approaches should provide a global view of the
coordinated functionalities inside complex biological systems as the beta cell.
However, even though a massive amount of experimental data is currently
available and substantial biological knowledge has been gained, they remain
insufficient for the inference of the missing knowledge, in order to simulate large
scale systems at molecular resolution. There are compromises that, if properly
applied, may improve the simulation speed and reduce the dimensionality
problem and parameter space, while making only minor sacrifices in the
description accuracy of the phenomenon.
The partitioning of the system into functional or mathematical parts is not always
a trivial task. Furthermore, when validation or optimization is needed for the
sub-models, it should be kept in mind that the data are usually referred to the
complete system and not to the parts, which are indeed not independent of the
rest of the system. Alternative models, which simulate large scale systems as a
whole by incorporating information and data from genes to proteins and
enzymes, are possible when sacrificing dynamic description resolution.
Constraint-based models are widely used as top-down models for the
investigation of the metabolic capabilities under specific environmental conditions
and perturbations, and dynamic phenomena can be approximated by changing
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the constraints. Additionally, a better way to incorporate other interacting
systems such as signal pathways, and gene regulatory networks to the complex
metabolic network of the beta cell leaves room for improvement towards a multilevel integrated system.
Currently, it is possible to simulate reaction networks occurring in intracellular
processes by coupling databases of reaction kinetics to simulation packages for
huge systems of non-linear ordinary differential equations (ODEs), e.g.
programmes like Silicon Cell, Vertical Cell, E-cell or Cyber Cell. Answering the
question of how beta-cell dysfunction is related to pathophysiology of diabetes
requires an even more geometrical and comprehensive, thoroughly multilevel
understanding of living processes based on distributed data over both temporal
and spatial scales in combination with systematic extensive experimental
measurement of key parameters. The scales range from the single nanometer
(nm) to thousands of nm and from milliseconds to 5-30 minutes (see other
chapters). Clearly, that requires a distinct type of mathematical modeling and
new software for the mesoscale. Although routine in physics, it is not yet
available in the biophysical simulation community (49).
Acknowledgements
I thank Dr. Thomas Sparre for access to protein expression data from the BB rat
transplantation models, and Peter Hagedorn and Mogens Aalund for
bioinformatics and data analysis. Financial support from the European Foundation
for the Study of Diabetes (EFSD)/Juvenile Diabetes Research Foundation/Novo
Nordisk is gratefully acknowledged.
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Figure legends
Figure 1.1
Large-scale molecular, clinical and imaging data provide the ability to capture the
complexity interacting molecular networks both within and between tissues that
underlies complex phenotypes.
Reproduced from Pharmacogenomics (2009) 10(2), 203-212, with permission of
Future Medicine Ltd.
Figure 1.2
a. Insulin has several metabolic and cellular effects. b. Glucose-induced insulin
production and secretion is a tightly controlled process, which is schematically
outlined only in the figure.
Figure 1.4
Perturbation of protein expression patterns. Prophylactic insulin prevents or
delays diabetes onset and preserves islet transplants in the BioBreeding (BB)
transplantation model. See text for details on experimental design. Heat plot of a
cluster of protein expressions in transplanted islets excised at different time
points post transplantation (p07: 7 days post transplantation; p23: 23 days, etc.
pDM: at time of diabetes diagnosis). Color codes are shown to the right of each
heat plot. Red indicates high expression and blue low expression. The Y-axis
show the coordinates of the protein spots identified on a 2D-gel. Note that the
order of proteins are not the same in a and b. a. Data for spontaneous diabetes
23
development. b. Data for transplanted BB rats receiving continuous insulin
infusion.
Figure 1.5
Islet protein expression differs between diabetes-prone (DP) and diabetesresistant (DR and WF) rat strains and a ‘diabetogenic’ pattern (left) and a ‘nondaibetogenic’ pattern (right) can be recognized. The hierarchical clustering, on
top, clearly differentiates between the two groups. Red indicates high expression
and yellow low expression. Each column represents a single animal from which
the islet transplant is excised at day 48 after transplantation or at time of
diabetes diagnosis, which is around day 48 in this model.
24
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