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Druc discovery, diseases and the Human Cytome

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Old 09-24-2004, 07:42 AM
Peter Van Osta
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Default Druc discovery, diseases and the Human Cytome



Hi,

I just put a copy of the current version of the article I put on my
website on the Human Cytome Project in this message
([Only registered users see links. ]). Feel free to
comment.

A Human Cytome Project - an idea

Introduction

Although the completion of the Human Genome Project holds many promises
for the understanding of the genetics of man and the involvement of genes
in human diseases, the use of this information has to be viewed from
another perspective as is currently being done. Predicting the dynamics of
the cell and its fate in diseases from the genome information upwards is
likely to fail due to the complexity of metabolic processing and
environmental influences on the cellular metabolism. Pathological
processes have to be viewed from another organizational level in order to
capture the complexity of processes involved in diseases.

On Monday 1 December 2003 I posted a message about the idea of a Human
Cytome Project to the bionet.cellbiol newsgroup (Van Osta P, 2003). It
seems that it was the right moment to ask the question, as there were
already ideas emerging on the role of the cell as the final arbiter in the
production of metabolic products and also the concept of predictive
medicine by cytomics (Valet G, 2003). The Human Cytome Project is already
being discussed at scientific conferences such as Focus on Microscopy and
the European Microscopy Congress and already articles start to appear on
the idea (Valet G, 2004). As the idea of a Human Cytome Project seems to
have generated some interest in the scientific community, I decided to put
the original message and question on my personal website for reference, so
here it is.

Monday, 1 December 2003 10:57:46 +0100 Hi,

I was wondering if there is already something going on to set up a sort of
"Human Cytome Project”? In my opinion the hardware and most of the
software seems to be available to set up such a project? For the cellular
level, light-microscopy based reader technology would be very interesting
to use?

Studying and mapping the genome, transcriptome and proteome at the
organizational level of the cell for various cell types and organ models
could provide us with a lot of information of what actually goes on in
organisms in the spatio-spectro-temporal space?

I have been thinking (working) about a concept which could provide the
basic framework for exploring and managing this cellular level of
biological organization research on a large scale, but I would like to
know if there is already some thought/work going on in the direction of
setting up an initiative such as a "Human Cytome Project" ?

This is just an idea, so I am really interested to hear if there is
something in it, or even if it is not worth while what I just wrote.

Best regards,

Peter Van Osta.

The path which lead to the idea of a Human Cytome Project

I will give a bit more background to the path which for me has lead to the
idea that something of a Human Cytome Project might be feasible. The idea
for large scale screening of the dynamics of the (living) cell came when I
visited the Sanger Center in the UK in 2001 and was shown a big room
filled with DNA-sequencers. From then on I wanted to create a system which
could mean for cell-based research (cytomics) what DNA-sequencing had
meant for Human Genome research. However I did not want to create a
catalog of the cytome, but to allow for the functional exploration of the
cell in order to capture and describe the dynamics of cellular processes
and not only create a catalog of its components. The multidimensional
world of the cell requires a higher-dimensional approach than the linear
world of DNA and also a different inner- and outer resolution is needed
for each level of biological integration. Today powerful techniques to
explore the cytome are available, such as flow cytometry (Edwards B.S.,
2004) and advanced digital microscopy (Price J. H., 2003) which enables
the exploration of the cellular function and phenotype. There are now
exciting technological developments going on in what is called High
Content Screening, which will allow us to explore the cytome on a large
scale. These developments and other technological advances made me feel
confident that the exploration of the human cytome would be feasible.

I myself wanted to know if a system to explore the cytome on a very large
scale could be implemented and would work. The nice thing of working in an
industrial environment is that you can create what you dream of and see it
in operation in a real-life situation. As technologies evolve, it should
be easy to exchange components of a system or expand it with new
technologies. The system should therefore be modular and scalable, the
core of the system should be of a different design than the interface to
the outside world and they should evolve separately, only linked to each
other for the exchange of information. The core has to be able to deal
with multidimensional spaces and datasets and manage the dataflow between
modules, each module dealing with a part of the entire process, from
acquisition to data generation. The design should allow for distributed
operation, so a system could run on different platforms and interact with
components over a network. It should use open standards for its
communication with the outside world to allow for easy integration in a
heterogeneous environment. The output of the system should be a set of
linked feature hyperspaces, each describing structural and functional
aspects of the individual cell and its components.

Since 2001 I have been thinking about, and working on, the design of a
scalable system, of which the M5 framework is now operational and it
allows me to study its practical use in more detail. The predecessor of
this system dates back to the early nineties of the twentieth century
(Cornelissen F., 1993; Geerts, H., 1992; Van Osta P. 2002). This use of
digital microscopy originated from Nanovid microscopy long ago (De Mey J.,
1981) and from monitoring the Calcium flux in individual cardiomyocytes.
Why a Human Cytome Project?

Human Genome Project

The Human Genome Project (Lander ES, 2003; Venter JC, 2003) has set a new
milestone in medicine and the understanding of human biology (Guttmacher,
A., 2002; Guttmacher, A., 2003). It has answered many questions, but it
has also left us with more questions to answer and it opened new horizons
for exploration (Collins F., 2003). The results of the Human Genome
Project reveal that there are only about 34,000 genes in the human genome.
The Caenorhabditis (C. elegans) genome is comprised of over 18,000 genes.
The fruit fly (D. melanogaster) genome consists of about 13,000 genes and
as such it has fewer genes than C. elegans, although as an organism it is
far more complex. Although there is much more variation in the sizes of
the genomes, this is not reflected in the number of genes. The complexity
and diversity of organisms is not reflected in the structural complexity
of their genomes alone, but to a large extent it is hidden in the dynamics
of cellular processing and gene expression. As there is no linear relation
between the complexity of an organism and the physical structure of its
genome, there is also no one-on-one relation between the phenotype of an
organism and its genome. Relatively small differences between organisms,
such as man and chimpanzee do result in large functional differences in
gene processing and functional expression.

In the case of diseases where we have already found a genetic basis, this
does not always allow us to find an explanation for the disease process.
Although the gene defect in Huntington’s disease is known for years, the
contribution of the gene defect to the functional out come of the disease
is not yet known (Georgiou-Karistianis N, 2003). In Crohn’s disease the
gene defect found does not explain the severity of the disease (Peltekova
VD, 2004). In cystic fibrosis, the severity of the disease cannot be
linked one–on-one to genetic variation in CFTR (Grody W et al, 2003). In
breast cancer genetic variants of BRCA1 and BRCA2 do not have a consistent
level of penetration and as such their presence alone does not explain the
disease process (Ford D et al, 1998; Hartge, 2003). Although there is
evidence for the involvement of the gene for PPAR-gamma in type 2 diabetes
is, the mechanism by which it contributes to the disease process of
diabetes is not clear (Barroso I, 1999) an could not be deduced from
genetic information alone.

Many diseases of clinical importance have heterogeneous mechanisms which
lead to the disease and only in a subpopulation the diseases can be traced
back to a single gene. In most cases a multiplicity of mechanisms
contributes to the diseases process. Multiple genes and (multiple)
environmental factors contribute to the disease process and its clinical
outcome. In AD (Alzheimer’s Disease), only a minority of cases can be
linked to hereditary gene mutations. In APC (Adenomatous Polyposis Coli)
and HNPCC (Hereditary Non-Polyposis Colorectal Cancer) a genetic origin,
only accounts for about 5 percent of all cases of colorectal cancer
(Kinzler, 1996). Genes which are involved in diabetes, such as GCK
(glukokinase) , HNF1A and HNF4A (Hepatic Nuclear Factor) are linked to
less than 5 percent of cases of diabetes (Edlund, 1998, Fajans, 2001).
Genetic information has a high predictive value in only a minority of
cases.

Now we are starting to use the information coming out of the Human Genome
Project, people start to understand that the dynamics of the cell and its
fate in disease processes cannot simply be explained from is genome or its
proteome alone. Structural information alone or information from too low
an organizational level cannot sufficiently predict higher-order phenomena
as it does not sufficiently take into account interactions at higher
organizational levels and influences from outside the low-level
organizational unit.

So if the structure of the genome alone cannot explain the differences
between species, disease processes and the dynamics of the cell, where
does our functional complexity and interspecies differences come from? How
do we continue in the post-genome era to study the dynamics of the cell
and entire organisms?

The dynamics of the cell

The cell is at the crossroads of life itself, being the lowest order
functional unit operating in a functional complete way. As such the cell
is for life what the atom is for physics, the smallest biological level of
organization, operating as a functional unit. Dysfunctional cells by
whatever cause, either gene malfunction, infection, nutritional or
environmental problems will eventually cause the entire organism to lose
its functional integrity. The dynamics of cellular systems allow for the
adaptation of the cell to a wide variety of conditions and challenges, a
relatively uniform physical structure combined with a web of interacting
dynamic processes leads to the multitude of cells which we see in living
organisms. The stochastic variation of cellular processing at the
molecular level is a cause of functional uncoupling of the cytome from the
genome and ads to the variability in functional behavior between cells
(McAdams H.H., 1999; Raser J.M., 2004). Structural research alone
underestimates the complexity of dynamic processes as it does not capture
sufficiently the dynamic complexity of the cell. The dynamic interaction
of processes in multiple pathways is the centerpiece of cellular life, not
the individual components.

Disease processes

Our increase in the knowledge of the involvement of our genes and large
scale proteomics in disease processes has not lead to an increase in the
productivity of pharmaceutical research (Drews J., 2000; Huber, L.A.,
2003). The gap between the gene and the functional outcome of a disease is
too wide to bridge it from one direction only (Workman P., 2001).

The importance of the dynamics of the cell and its involvement in
pathological processes and current therapeutic efforts also requires a
better understanding of its function and phenotype in its relation to
pathological processes in diseases, such as cancer, Alzheimer disease,
infectious diseases, such as AIDS, tuberculosis (TBC), influenza (flu),
etc. In infectious diseases the environment, in this case the infectious
agents, interacts in a complex way with the host defense system of which
much remains to be explored. We must be aware of the fact that the golden
era of antibiotics is already behind us as many infectious agents (e.g.
TBC, MRSA and other bacterial diseases) are showing an increasing
resistance against most classes of antibiotics which are available today.
We have succeeded in less than a century to destroy our best weapons
against infectious diseases, due to misuse of antibiotics both by
physicians and their patients. Only the elderly remember the days when
mortality due to infections was a major cause of premature death, but the
moment is approaching when this nightmare will return.

Viral diseases (e.g. AIDS, influenza) are even harder to fight as they use
the cellular machinery of the body itself to reproduce. We need to study
the pathological process in cells in more detail and in a different way,
in order to have a chance to succeed in the new therapeutic challenges
ahead of us. Viruses, under selective pressure of modern antiviral drugs
are also showing increasing resistance to treatment. We are running out of
time in our battle against infectious diseases and a systematic approach
will only give us the answers when it will be too late. We are not setting
the agenda, but the diseases are taking the lead.

Due to modern technology, the time to respond to a new infectious
challenge is being reduced. In modern times, diseases take planes too,
which makes it even harder to fight them by classical isolation or
quarantine. Airplanes may be safe to travel with, compared to other
transport systems, but they can cause secondary mortality by transporting
pathogens over large distances at a speed unknown to previous generations,
which gives a new meaning to airborne infections. Infectious diseases may
initially go unnoticed in underdeveloped areas of the world (e.g. Ebola),
but as soon as they board a plane, it is modern technology which will give
them free access to the world. A relatively long incubation time combined
with a high mortality rate will allow a disease to spread widely and cause
a pandemic, before we even can start a treatment program. If an unknown
disease causes such a pandemic, we may run out of time before we can find
a cure as we first have to develop a diagnostic tool. A recent example
which is a model of what can happen was SARS and scientists are waiting
with fear for the next influenza pandemic. Recent outbreaks of avian flu
have given us a preview of what can happen and evidence is increasing that
the possibilities for spreading avian influenza (H5N1 type) are worse than
was assumed (Kuiken T., 2004). Most people have no idea of the role
smallpox played in the destruction of an entire civilization after it was
brought to America by the conquistadores; the speed at which people died
is beyond our current imagination. In modern times we not only have to
fear the accidental spreading of infectious diseases, but bio-terrorism
will challenge our defenses sooner or later.


For degenerative diseases, such as Alzheimer disease and cancer, birth
defects, cardiovascular diseases, and nerve degeneration it is the
cellular machinery itself which fails. One of the most promising domains
of research today is stem cell research, which has to deal with the
functional and structural characteristics of cells which are being
studied. Gene therapy holds many promises for the therapy of life
threatening diseases, but in order to improve gene therapy we will need a
better understanding on what goes on inside the cell and what the
consequences are on the cellular metabolism when we modify its function by
inserting genes. At this moment monogenic diseases are the target for gene
therapy, but in the future parts of pathways may need reconstruction. The
gene is the means to achieve the ultimate goal to change the cellular
metabolism to cure a disease. At this moment the cell is the target for
many therapeutic efforts to come to a causal therapy of diseases, which we
can now only treat with external substitution, such as diabetes.

Disease models in pharmaceutical research

At the end of the drug discovery pipeline, there are patients waiting for
treatments, company presidents and shareholders waiting for profit and
governments trying to balance their health care budget. For pharmaceutical
and biotech companies, the critical issue is to select new molecular
entities (NME) for clinical development that have a high success rate of
moving through development to drug approval. Finding new drugs (which can
be patented to protect the enormous investments involved) and at the same
time reducing unwanted side effects is vital for the industry. The cost to
develop a single drug which reaches the market is tremendous and is mainly
due to the high failure rate of the drug discovery and development
process. Pharmaceutical companies are always trying to reduce this
failure rate in order to bring the enormous costs down involved in drug
discovery and development. Only about 1 in 10,000 drugs makes it from
early pre-clinical research to the market, which is not an example of a
highly efficient process. The current focus of the pharmaceutical industry
on blockbuster drugs is a consequence of the mismatch between the soaring
costs and the profits required to keep the drug discovery and development
process going. If the industry cannot bring the costs down, it may as well
try to raise its income by changing its price policy, but this shifts the
solution for the problem from in- to outside the company and places the
burden on the national health care systems. Companies which were more
successful in the past, achieved a higher efficiency even without the
availability of extensive genomic data, technology and data alone are not
sufficient to improve the drug discovery process (Drews J. 1999, Omta
S.W.F., 1995).

We have seen an enormous investment in research at the infra-cellular
level in the past ten years and at the same moment have witnessed a
disproportional decline in the productivity of research and development in
drug discovery. The pharmaceutical industry has yet to find a way to
reduce its high attrition rates (Kola I., 2004). The consolidation in the
pharmaceutical industry will not solve this problem in the long run, as it
only reduces the costs but does not improve scientific productivity; it
only postpones the moment of truth. The scientists themselves will have to
find new ways to improve their productivity; management cannot do this in
their place. Society tries to protect itself against the adverse effects
of new drugs (e.g. Thalidomide) by increasingly stringent regulations but
the currently used methods in the discovery process for new drugs cannot
keep pace with these new requirements. However, increasingly strict
regulations do not explain all the problems pharmaceutical research is
facing today. There is a fundamental problem with studying
disease-relevant mechanisms as the pharmaceutical industry has been
investing heavily in studying the bricks, instead of looking at the
building as a whole. You could also think of it as a pointillist painting,
of which we have been looking at the individual dots, instead of looking
at the entire painting. Another analogy is that we are trying to explain
the tidal patterns of the oceans, by studying a water molecule and
ignoring the moon. We have to look at biological phenomena at the
appropriate scale of integration and from a functional point of view in
order to get a grip on the development of pathological processes.

The early stage disease models don’t work as they should do and do not
provide enough predictive power. One can study cellular components, like
DNA and protein as such, but this will not reveal the complex interactions
going on at the cellular level of biological integration or in other
words, the cytome . Both medicine and pharmaceutical research would
benefit from using more cell oriented disease models and even higher-order
models, instead of using infra-cellular models to try to describe complex
pathological processes at a molecular level and getting lost in the maze
of molecules which are the building blocks of cells.

A highly defined oligo-parametric infra-cellular disease model used in
High Throughput Screening (HTS) which in its setup ignores the complexity
of higher order biological phenomena, may produce beautiful results in the
laboratory, but fails to generate results of sufficient predictive power
to avoid considerable financial losses later on in the drug discovery
pipeline (Bleicher KH et al., 2003). A living cell may be a less well
defined experimental environment for the biochemist, but it will provide
us with the additional modulating influences on our disease models which
are lost in lower-order disease models. A traditional cell culture (CHO-
and HeLa cells) in the laboratory may not yet mimic the physiological
conditions in an entire organism, so our approach to cell-based research
(and beyond) requires some redesign also.

Studying subcomponents of cellular pathways ignores the functional unity
of the biological processes in the cell and the functional interactions
between pathways. Without a better understanding of the phenotypic and
functional outcome in the cell, the failure rate of the drug discovery
process will remain high and very costly. There is a predictive deficit in
the current oligo-parametric disease models used in pharmaceutical
research which necessitates complex and expensive studies later on in the
drug development pipeline to make up for the predictive deficit. The
popular techniques to explore and analyze low-dimensional data at high
speed are based on the idea that this would provide all the data with
sufficient predictive power to allow for a bottom-up approach to drug
discovery. The current methods allow gathering low-dimensional data at
high speed and volume, but their predictive power is too low. The
knowledge gathered at the infra-cellular level has to be viewed in its
relation to the (living) cell and the biological and non-biological
processes influencing its function and health, which requires a top-down
functional and phenotypical approach rather than a bottom-up descriptive
approach. Complex disease processes cannot be explained by simple
oligo-parametric low-level models. A high-speed oligo-parametric disease
model does not equal high predictive power. It is not the ability to study
a simplified disease model at high speed which will allow us to succeed,
but we must study and verify the functional outcome of the disease process
itself.

A game of chess is not described by naming its pieces, but by the
interaction of the actions of both players or in other words the flow of
actions and reactions, described in a space-time continuum and if we add
the color it is a spatio-spectro-temporal flow of events. The individual
pieces or moves do not explain the final outcome of the game, only when
the entire process is analyzed from a functional point of view we can
understand the reason why one player wins or loses. You have to study a
game of chess at the appropriate organizational level in order to
understand it or you will fail to find an explanation for the outcome of
the game.

Cytomics

Cytomes can be defined as cellular systems and the subsystems and
functional components of the body. Cytomics is the study of the
heterogeneity of cytomes or more precisely the study of molecular single
cell phenotypes resulting from genotype and exposure in combination with
exhaustive bioinformatics knowledge extraction (Davies E, 2001).

In order to get the broader view on pathological processes, we should move
on to the phenotypical and functional study of the cellular level or the
cytome in order to understand what is really going on in important disease
processes. Although the genome and proteome level have their predictive
value in order to understand the processes involved in disease (and
health), the cytome level has the potential to put everything in the right
perspective. By integrating the knowledge from the genome and proteome, we
could give guidance to the exploration of the cytome, which was not
possible before this knowledge was available. The cytome level will also
provide guidance to focus the research at the genome and proteome level
and so creating a better cross-level understanding of what is going on in
cells. Some would see this as taking a step back from the current
structural and systematic descriptive approach, but it is mainly a matter
of integrating research at another level of biological integration and
looking in a different way to the web of interactions going on at the
cellular level. Biological processes do not exist in a void, but they are
a part of a web of interactions, rather than being an island on their own.

In recent years the tools have matured to start studying the cellular
level of biological integration, but the tools are still used in the same
way as if they were derived from low-content high-throughput phenomena as
this is still the dominant research model. The tools to generate and
explore a high-dimensional feature space are still scattered and not
brought into line with the exploration of the cytome.

Functional processing in cellular pathways

The interconnection of genome, proteome and cytome data will be necessary
in order to allow for an in-depth understanding of the processes and
pathways interacting at the cellular level. A monocausal approach will
have to be replaced with a poly- and pluricausal approach in order to
understand and explain the phenomena going on at the cellular level.
Pluricausal means causal contributions at different levels, such as genes,
other cells and environmental influences. Polycausal means multiple causal
contributions at the same biological level, such as polygenic diseases or
multiple agonistic and antagonistic environmental influences. The concept
of a multithreaded, weighed causality is needed in order to study the web
of interactions at the cellular level.

A cause (e.g. a single gene defect, a bacteria) can have multiple
consequences and as such be poly-consequential, which is the mirror
situation of a single consequence being caused by multiple causes
(co-causality or co-modulation) acting either synergistic or antagonistic
(e.g. a disease with both a genetic an environmental component). In
reality, a pathological condition is a mixture of those extremes (e.g. a
bacterial or viral infection and the host’s immune system) and as such a
simple approach is not likely to succeed in unraveling the mechanism of a
disease. With the current systematic and descriptive approach however, we
get lost in the maze of molecular interactions, as we are looking at too
low a level of biological integration and we get lost in a maze of
structures and interactions. The cell is the lowest acceptable target, not
its single components, like DNA or proteins.

We are looking at the alphabet, not even words or sentences, nature is not
a dictionary, but it is a novel. We should study the flow of events in a
cell with more power, not only the building blocks. As an example, Mendel
did not need to know about DNA in order to formulate his laws of
inheritance and he did not know that the discovery of the physical carrier
of inheritance, DNA, would confirm his views later on, but his laws are
still valid as such. Certainly physics was not at the stage it was in the
20th century when Newton formulated the law of gravity. When Einstein
formulated his relativity theory, he did not have modern physics at his
disposal. His theory does not fit well to the quantum level, but does
explain phenomena at a higher level of functional integration and as such
is an appropriate model. What we find should not be in contradiction to
what structural descriptive research discovers, but we should not wait for
its completion to start working on the problems we are facing in medicine
and health care today.

How to explore and find new directions for research

If there were no consequences on the speed of exploration in relation to
the challenges medicine is facing today, the situation would of course be
entirely different. In many cases, the formulation of an appropriate
hypothesis is very difficult and the resulting cycle of formulating a
hypothesis and verifying it is a slow and tedious process. In order to
speed up the exploration of the cytome, a more open and less deterministic
approach will be needed. Analytical tools need to be developed which can
find the needle in the haystack, without a priori knowledge or in other
words we should be able to find the black cat in a dark room, without
knowing or assuming that there is a black cat. An open and
multi-parametric exploration of the cytome should complement the more
traditional hypothesis driven scientific approach, so we can combine speed
with in-depth exploration in a two-leveled approach to cytomics. The
multi-parametric reality which we need to deal with requires a more
multi-factorial exploration than the way we explore the cellular level at
this moment.

We now close our eyes to much of the complexity we observe; because our
disease models are not up to the challenge we are facing today. Feeling
happy with simple answers for simple questions in low-complexity disease
models will not help us at the end of the drug discovery pipeline. The
value of a disease model does not lie in the technological complexity of
the machinery we use to study it, but in its realistic representation of
the disease process we want to mimic. The residual gap between the model
and the disease is in many cases too big to allow for valid conclusions
out of experiments with current low-level disease models. Due to deficient
early-stage disease models, the attrition rate in pharmaceutical research
is enormous. The easy targets to treat are found already, we need a
paradigm shift to tackle the challenge ahead of us. What to do and the way
to go?

The phenotypical and functional characterization of the (human) cytome is
the ultimate goal of an endeavor on the scale of a Human Cytome Project.
It is the prerequisite to come to a better understanding of disease
processes and to improve treatments for complex and life threatening
diseases.

To study the cytome will require a broad multidisciplinary a multinational
approach, which will involve scientists from several countries and various
disciplines to work on problems from a functional and phenotypical point
of view and top-down, instead of bottom-up. Both academia and industry
will have to work together to avoid wasting too much time on scattered
efforts and dispersed data. The organizational complexity of a large
multi-center project will require a dynamic management structure in which
both academia and the industry participate in organizing and synchronizing
the international effort. Managing and organizing such an endeavor is a
daunting task and will require excellent managerial skills from those
involved in the process, besides their scientific expertise (Collins F.S.,
2003b).

The challenges of Human Cytome Project will not allow us to concentrate on
only a few techniques or systematically describing individual components,
but we must keep a broad overview on the cell and its function and
phenotype by multi-modal exploration. We will need an open systems design
in order to be able to exchange data and analyze them with a wide variety
of exploratory and analytical tools in order to allow for creating a broad
knowledgebase and proceed with the exploration of the cytome without
wasting too much time on scattered data. On the experimental side,
standardization of experimental procedures and quality control is of great
importance to be able to compare and link the results from multiple
research-centers.

Managing and analyzing data in a multidimensional linked feature space or
hyperspace will require a change in the way we look at data analysis and
data handling in order to succeed. At the moment we expect that an oligo-
or even mono-parametric analysis will allow us to draw conclusions with
sufficient predictive power to work all the way up to the disease
processes in an entire organism. We are using disease models with a
predictive deficit, which allow us to gather data at great speed and
quantity, but in the end the translation of the results into efficient
treatment of diseases fails in the majority of cases. The cost of this
inefficient process is becoming a burden, which both society and the
pharmaceutical industry will not be able to support indefinitely. As "the
proof is in the pudding", not in its ingredients, we have to improve the
productivity of biomedical and pharmaceutical research and broaden our
functional understanding of disease processes in order to prepare
ourselves for the challenges facing medicine and society.

Building the multidimensional matrix of the web of cross-relations between
the different levels of biological organization, from the genome, over the
proteome, cytome all the way up to the organism and its environment, while
studying each level in a structural (phenotype) and functional way, will
allow us to understand the mechanisms of pathological processes and find
new treatments and better diagnostics tools. A systematic descriptive
approach without a functional complement is like running around blind and
it takes too long to find out about the overall mechanisms of a
pathological process or to find distant consequences of a minute change in
the pathway matrix. The project should be designed in such a way that
along the road intermediate results would already provide beneficial
results to medicine and drug development. Intermediate results could be
derived from hotspots found during the process and worked out in more
detail by groups specializing in certain areas. As such the project could
consist of a large scale screening effort in combination with specific
topics of immediate interest. The functional exploration of pathways
involved in pathological processes, would allow us to proceed faster
towards an understanding of the process involved in a disease. A dual
approach which on one side focuses on certain important diseases (cancer,
AD, … ) and on the other side a track which focuses on cellular
mechanisms such as cell cycle, replication, cell type differentiation.

We should also get serious on a better integration of functional knowledge
gathered at several biological levels, as the scattered data are a problem
in coming to a better understanding of biological processes. The current
data storage models are not capable of dealing with heterogeneous data in
a way which allows for in-depth cross-exploration. Data management systems
will need to broaden their scope in order to deal with a wide variety of
data sources and models. Storage is not the main issues, the use and
exploration of heterogeneous data is the centerpiece of scientific data
management. Data originating from different organizational levels, such as
genomic (DNA sequences), proteomic (protein structure) and cytomic (cell)
data should be linked. Data originating from different modes of
exploration, such as LM, EM, NMR and CT should be made cross-accessible.
Problems to link knowledge originating from different levels of biological
integration is mainly due to a failure of multi scale or multilevel
integration of scientific knowledge, from individual gene to the entire
organism, with appropriate attention to functional processes at each
biological level of integration. References

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the future of genomics research, Nature, 2003, 422:835-847 * Collins
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M., Geuens, G., Nuydens, R., De Brabander, M., High resolution light
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Medicine - A Primer. New England Journal of Medicine, 2002;
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era. Editorial: New England Journal of Medicine, 2003, 349:996-998 *
Hartge P., Genes, hormones, and pathways to breast cancer, New Engl J
Med. 2003 Jun 5;348(23):2352-4. * Huber LA., Is proteomics heading in
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of the human genome, Nature. 2001 Feb 15;409(6822):860-921 * McAdams
HH, Arkin A., It's a noisy business! Genetic regulation at the
nanomolar scale, Trends Genet. 1999 Feb;15(2):65-9. * Price J. H.,
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R. F., Rabinovich A., Reed J. C., and Heynen S., Advances in Molecular
Labeling, High Throughput Imaging and Machine Intelligence Portend
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cation transporter genes are associated with Crohn disease, Nat Genet.
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24. * Valet G, Trnok A, Cytomics - New Technologies: Towards a
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Osta P, A Human Cytome Project ?, Dec. 1 2003,
[Only registered users see links. ]
* Van Osta P., Geusebroek J.M. , Ver Donck K., Bols L., Geysen J., ter
Haar Romeny B. M., The principles of scale space applied to structure
and colour in light microscopy, Proceedings of the Royal Microscopical
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Targets. 2001 May;1(1):33-47.

Meetings

* Focus On Microscopy –2004
* ISAC XXII Montpellier – 2004
* European Microscopy Congress –2004 * EWGCCA –2004

Links

* Towards a Human Cytome Project
* Draft: Human Cytome Project
* Cytomics - Info
* Functional genomics
* Cyttron
* Prediction in Cell-based Systems (Predictive Cytomics) * Biomedical
Structural Research


Email: [Only registered users see links. ] remove the _NOJUNK_ before sending
an email.

The author of this webpage is Peter Van Osta, MD.

First draft on 1 December 2003

Latest revision on 19 September 2004

================================================== ===============

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