At the first glance, it may not be obvious why a cell should have anything to benefit from deciding to kill itself. But in a multicellular organism, cells often need to be replaced. An average homo sapiens turns over about 3 kg of her body weight each day, through cell death and proliferation. If a cell were to lose its proper judgement and stop responding to death signals, it would remain and possibly proliferate at the expense of the other cells and the organism. We have a word for it: cancer.
Therefore, scientists have spent lots of effort trying to understand how cell death is regulated. Most of the time, these efforts have centered on specific genes and proteins. Researchers have been able to remove or inhibit one protein, say, and found that cell death decreases. They have meticulously mapped together interacting proteins in models with arrows, that resemble at best a mechanical contraption where each protein is a cogwheel and the rotation of one is directly proportional to another, etc.
The MIT Cell Decision Process Center is populated by scientists that feel that the nonlinear dynamics in the cell can only be understood with more mathematically sophisticated methods. Yet at the same time, they believe that little comes of speaking in general terms about complexity (as I am prone to do) without backing it up with rock-solid biological data. They have embarked on a quest to extract enormous amounts of very detailed information from the cells’ interior, that can serve as a basis for modelling. In the words of Peter Sorger, the centre’s director: “In its emphasis on formal numerical models, systems biology breaks with the tradition in genetics and molecular biology of anecdotal and pictorial models. However, the experimental emphasis in is also critical because it is only through experimentation that models can be tested for their accuracy. “
This is a completely reductionist approach to the cell, implying that the system can best be understood in terms of its components. Such approaches tend to be very cumbersome, because they need to generate huge quantities of data to determine the dynamics of many components at once. It is research by the Verdun doctrine: throw more people and equipment at the problem, and it will eventually surrender. It is the opposite of trying to find an incisive point where a key hypothesis can be tested. It is often productive research, but in the end it’s not really a lot of fun to do.
Do I want to work at this centre? Well, they seem to be the largest and best place in the world where the anatomy of the cell’s brain is being explored. But their actual work consists of data-grinding. They do fun things too, mainly in methods development – for example, they have developed a set of weighing scales capable of telling the weight of cell substructures and nanoparticles. But I continue to hope that the organising principles of the cell’s brain can be understood with a holistic approach, aimed at finding the rules that govern it.