Genetic Network Modelling

The bench biologist is in a continual search mode for some new gene interaction. What new gene can be a, not the, cause of say prostate cancer. At the other extreme is the systems biologists who use their mathematical models to propose reactions. The intersection of these two is not that fruitful as of yet.

In Science the authors conclude:

Models are simplified (but not simplistic) representations of real systems, and this is precisely the property that makes them attractive to explore the consequences of our assumptions, and to identify where we lack understanding of the principles governing a biological system. Models are tools to uncover mechanisms that cannot be directly observed, akin to microscopes or nuclear magnetic resonance machines. Used and interpreted appropriately, with due attention paid to inherent uncertainties, the mathematical and computational modeling of biological systems allows the exploration of hypotheses. But the relevance of these models depends on the ability to assess, communicate, and, ultimately, understand their uncertainties. 

 The process is iterative. Models are built, tested, found lacking, and then reiterated. The challenge is that these are quite complex and of massive dimensions. Perhaps methods akin to 19th Century thermodynamics may play a role, gross constructs like enthalpy and Gibbs free energy, but perhaps not.

It will take time to get these models to work properly, but they are needed as a cornerstone of true science.