Biomolecular modelling

Lead Research Organisation: The Francis Crick Institute


Modern science demands an ever-closer union between computational and experimental disciplines. In our laboratory, we collaborate extensively with experimentalists to understand, and predict, how cells function. Clearly, this is important for understanding, monitoring and devising therapeutic interventions for most, if not all, human diseases. A key feature of our computational efforts is the production of computer simulations. These simulations aid experimental investigations by explaining how objects interact over a time period, for example, how macromolecules interact with each other to fire a signal transduction pathway or how cancer cells migrate away from a solid tumour. To treat the objects within these simulations realistically, and therefore, to be able to observe how the system of objects form emergent patterns recognisable to the experimentalist, all objects must be given all their known functionality. For example, protein objects must be given their full range of known flexibility and electrostatic potential at their surfaces and all cells must be given their known modes of physical interaction and their capacity to transmit signals to each other and objects within their environment such as the extracellular matrix.

Technical Summary

This work was supported by the Francis Crick Institute which receives its core funding from the UK Medical Research Council (FC001000), the Wellcome Trust (FC001000),and Cancer Research UK (FC001000)

A central aim of the recently established Francis Crick Institute is to encourage multidisciplinary biomedical research. My laboratory and I are motivated by this philosophy plus fundamental and challenging problems in both structural and systems biology: in particular, how macromolecules, such as proteins and DNA, interact to facilitate higher order cellular events. Much of our work involves applying the principles of physics and evolution to the design of novel computer-based algorithms that enable us to simulate cellular events, such as cancer cell migration and chromosome condensation.

The field of computational systems biology is vast, there are however unifying themes, probably the most import being how key properties emerge from the interacting parts of a system. The Biomolecular Modelling Laboratory represents only a very small group of computational scientists; hence it is important that we remain highly focused on just a few projects within the wider field of computational systems biology. Moreover, we understand the importance of collaborating with our fellow experimentalist, both within and outside this institute, if we are to address interesting questions concerning complex wild-type and disease associated cellular systems and suggest appropriate diagnostic and therapeutic interventions. Given the above there are two well defined, systems level projects which we are working on and wish to progress further over the next few years: modelling the dynamics of tumours and their microenvironments, and developing a course-grain modelling approach to understand the topology of, and the interactions between, chromosomes both during interphase and upon their condensation in prophase; diseases, such as cancer, can develop from problems associated with chromosome condensation. These two projects are supported by biological data, from a number of Crick laboratories, that we are privileged to help analyse.

Since starting my laboratory the idea has been to progress computational biology in a holistic manner, from the atomic to the tissue level. We have derived a complex set of algorithmic modules for such studies. For the study of cancer, I think that the more interesting questions to address with the aid of these modules centres on tumour dynamics. Tumours are a complex blend of different cell types displaying considerable heterogeneity, from highly somatically mutated cells to cancer stem cells. They behave in an analogous fashion to an organism, birth (driver mutations), development, homeostasis and even death (cellular apoptosis). Waste products must be removed and nutrients delivered. Understanding these mechanisms will require computational models and clear systems level approaches. The intention is to develop multiscale computational models to study the interplay between cell populations within tumours - intratumour heterogeneity. This will draw our knowledge and computational skills together - from understanding and computing the outcome of cellular pathways to modelling the interaction between different cell types in variable environments - and will span multiple spatial (nanometres to centimetres) and temporal scales (nanoseconds to days). Essential to the project will be my interest and commitment to understanding and predicting macromolecular interactions, along with their associated kinetics. For all projects, careful benchmarking is required, and for this we will be guided by the principles of machine learning.


10 25 50