The biomolecular components and architecture of synthetic biology are typical of soft matter systems (e.g. macromolecules, polymers, lipids) the study of which led to the Nobel Prize in Physics in 1991 to PG deGennes.  These are characterised by a large number of modes of motion (ways of moving and flexing), operating over a wide range of length and time scales. These modes typically affect each other strongly, resulting in multi-scale behaviour: the systems self-organize and show what can be referred to as ‘‘emergent properties’’.

Simulating large numbers of biomolecules for long enough times to model this emergent behaviour is the challenging task of coarse-grained modelling. This raises a number of issues. Unlike say, individual chemical bonds, these larger aggregates of biological matter are not necessarily stiff (the stiffer a system the faster it relaxes to equilibrium) which is where the name soft matter comes from. The soft interactions and implied weak forces that restore any perturbation away from equilibrium means that it takes a very long (simulation) time for the system to get to its final equilibrium state. In addition the number of complex modes of relaxation typically  grow very fast as one increases the size of the system being modeled, so that looking at even slightly bigger length scales forces one to invest orders of magnitude more computation time.

Resolving all of these issues requires iterative application of coarse-graining techniques for modeling biological macromolecules appropriate to describe the phenomena at the relevant scales. These coarse-graining methods require judicious choice of which aspects of a system are important for the phenomena that one would like to capture by the strategic focus of the models on particular details.  

At its most coarse-grained, we can view the components of the cell as interacting in a complex interplay of feedbacks, ultimately setting up a flow of energy that creates order out of randomness.

Coarse-grained models have been an important part of our recent progress in the understanding of cellular processes, and will play a key role in synthetic biology by using in-silico studies on multiple scales to speed up and optimize the process of  synthetic bioengineering.

Project lead: Professor Tannie Liverpool (theoretical soft and biological matter, stochastic modelling of chemical reactions)