The Brain as a complex system: scientific theory

The human brain is a structure of overwhelming complexity. Beneath its mazy surface, it is made up of nearly a 100 billion neurons, each of which communicates with other neurons through up to 100.000 neuronal connections, the synapses. About 80.000 genes have been identified in the human genome, and a large portion of these genes play a part in the development and function of the nervous system

Since ancient times, the natural sciences try to cope with the problem of complexity seen in organisms by focusing on smaller parts and simplified abstractions that were easier to understand. This strategy can be called a reductionistic approach – large and complex systems are divided into smaller, isolated fractions: A single gene is responsible for a single feature, a single mutation is responsible for a disease, a single protein has a small number of clearly delimited functions. This approach has worked very well for a long time and made many great achievements of the past possible. However, reductionism has two downsides:

It does not reflect biological reality: Biological systems are complex, which means that the properties of a biological system cannot be fully explained by an understanding of its component parts. They consist of a vast array of mutually interacting and interwoven metabolic pathways, signal transduction cascades, physiological processes and much more. System properties emerge that would not be thought possible through an observation of the single parts – e.g. human cognition emerges from the electrical activity of billions of neurons. In a reductionistic approach, a small portion of this dense network is excised and observed, whereby a multitude of interactions that lie outside the observed system is clipped off and ignored. The predictions based on such an isolated system can often be vastly different from biological reality.

It is quickly becoming unmanageable: The amount of data, concepts and publications that is produced by today’s scientific community has reached an unprecedented scale. It is becoming harder and harder for the individual researcher to make sense of the exploding collection of data. This forces scientists to focus on smaller and smaller sub-disciplines in which they can still retain some overview. In the words of Ludwig von Bertalanffy, founder of the general systems theory:

‘Modern science is categorised by its ever-increasing specialisation, necessitated by the enormous amount of data, and the complexity of techniques and of theoretical structures within every field. Thus, science is split into innumerable disciplines that continually generate new subdisciplines. As a consequence, the physicist, the biologist, the psychologist and the social scientist are, so to speak, encapsulated in their private universes, and it is difficult to get word from one cocoon to the other’.
- Ludwig von Bertalanffy, General Systems Theory

The problems with the reductionistic approach have become prevalent in the biomedical research of the past decade. As a consequence, a new consciousness for the necessity of finding alternative ways to think about biological systems has developed, giving rise to a new discipline: Systems Biology.

Systems biology is an academic field that seeks to integrate biological studies to understand how biological systems function. By studying the relationships and interactions between various parts of a biological system (e.g. metabolic pathways, organelles, cells, physiological systems, organisms etc.) it is hoped that eventually an understandable model of the whole system can be developed.
 http://en.wikipedia.org/wiki/Systems_biology

Unfortunately, the way in which scientific knowledge is stored, processed and communicated is still very much oriented on a reductionistic approach. To overcome this problem, a new information infrastructure needs to be created. The Semantic Synapse Project will demonstrate how such an infrastructure might look like, based on semantic web technology.