Common diseases such as allergy, autoimmunity and cancer are complex, that is,
caused by multiple interacting genes and environmental factors.
Using high-throughput methods, all genes and their products can be studied in
The challenge lies in understanding the complex gene and protein interactions
that underlie these diseases.
It has been shown that common principles, such as network theory, can be used
to understand widely different complex systems.
Despite this, there have been few applications in medical research.
The hypothesis behind this project is that complexity theory can be used to
unravel disease mechanisms and to develop predictive models of complex disease.
The models will be validated experimentally and by solving a real-world clinical
problem, namely, to find biological markers for personalized medication.
This involves the design, implementation and enhancement of complexity tools
(e.g. network theory metrics) used to assess emergent properties from experimental
studies of complex diseases, such as allergy, autoimmunity and cancer.
Graph theory plays a central role in our approach.
Complex diseases are seen from a systems perspective and the focus is on the
emergent properties of systems rather than individual components.
This involves transfer of knowledge from complexity studies in other fields,
such as language evolution.
The long-term goals are to mimic and reverse pathogenic network changes
in experimental models of complex disease.
The project is a multi-disciplinary collaboration between clinicians, experts
in complexity theory, bioinformaticians, computer scientists, molecular biologists,
immunologists and geneticists in Europe and the USA.
Thus, the project is a cross-disciplinary effort aimed at applying the science
of complexity to a specific area where complexity is a key issue.