Clearly we need to adopt a more multidisciplinary approach.  In the past biology was focused mainly at the micro-level; our frame of reference was the single molecule or cell. But today scientists are increasingly interested in the interaction between cells. Topics like signal transduction are coming to the fore. This means that we need to take a more holistic approach; the focus is shifting to system biology.

 

For example, scientists today are trying to take a genome-wide approach to research.  This means assessing the possible involvement of thousands of different genes in a particular cellular process, in the hope of reducing those interactions to mathematical models.  But to accomplish that you need cooperation between a range of different disciplines: genomics, metabolomics, transcriptomics, proteomics, and off course mathematics.  The volume of data that needs to be collected and analysed has become absolutely tremendous, and has spawned new disciplines such as bioinformatics and computational biology.   That is the key challenge for us at the Institute: if we want to remain competitive then we will need broader cooperation between the disciplines.  That is why we are mixing different disciplines in our teams.  And we’re hiring new people from other disciplines.  The point is to build this multidisciplinary approach in our model; an ad hoc approach will not work.  Also, we are training our people in new skills, especially bioinformatics and the software tools needed to analyse these volumes of data.

Ultimately this is taking us away from the purely descriptive biology.  Biology is becoming more quantitative; we’re measuring more and trying to reduce that data to mathematical models.

NERF—it stands for Neuroelectronics Research Flanders—is an extreme example of this trend toward multidisciplinary research.  It illustrates it perfectly since the multidisciplinary approach is built-in the very concept: NERF tries to link biology and microelectronics. We had been talking to IMEC (ed. note. Leuven-based IMEC is Europe’s largest independent research centre in nanoelectronics and nano-technology) for some time, exploring opportunities in the interface between microelectronics and biology. Eventually we concluded that the key opportunity lies in the nervous system.

 

It may surprise people but we actually know very little about the nervous system. We know it is steered via electric impulses between nerve cells.  In the past we looked at it in reductionist terms; we tried to understand the cell-pulse-cell sequence in isolation.  But it is clearly far more complicated than that. To understand what is going on we need to look simultaneously at multiple impulses, entire trains of impulses, all with significant variation in Hertz.  Nerve cells have a huge number of different links with other cells.  A brain cell, for example, has about 25,000 different contacts with other cells. Compare that to an ordinary cell which has about 7 or 8 connections with other cells. The complexity, in other words, is astounding.  A nerve cell tentacle goes on the hunt for other cells to connect with but somehow needs to know that it cannot connect back to itself or connect with the same cell twice—otherwise a short-circuit results. While we’ve recently discovered how a tentacle avoids connecting to its own cell, we still don’t understand how it manages to pick out a unique cell.  

 

With NERF we want to map the electronic signals that a nerve cell receives and sends out.  We want to study this in vivo (in the living organism) and map it completely: how many contacts the cell has, what goes in where at what Hz and what goes out where at what Hz. This is the microelectronic part of the job. The biological part is concerned with the genetics: which genes are involved in this process of input-processing-output?  

 

At IMEC we’re building a lab for five research groups, to be staffed by about 50 people. Important to note is that this is a long term plan. We have a 10-20 year horizon. At present this is all still blue sky thinking. But if we succeed, then the potential applications could be tremendously useful. For example, paralysis due to head injury is due to a broken transmission somewhere in the nervous system. If we can simulate the transmissions in the brain then we could possibly solve the problem. Or take neurodegenerative diseases like Alzheimer’s or Parkinson’s: these diseases are the result of neurons dying but we do not understand why. We do not know which genes are involved because we have not yet succeeded in measuring this. Ultimately we may be able to tinker with memory and cognitive abilities, although that probably takes us to a 50 year horizon. Some of it is still science fiction but the roadmap is clearly beginning to take shape.