Stochastic dynamical modelling for prokaryotic gene regulatory networks

StoMP is a BBSRC MATSYB research network. The aim of this network is to create a strong UK research community at the mathematics/physical science/microbiology interface, in the area of stochastic dynamical modelling of gene regulation.

The aims of StoMP are to:

* organise a series of research and dissemination-focused workshops
* run a website and mailing list to co-ordinate UK efforts in this area
* provide funding for a number of collaborative research visits between network members
* create new research collaborations, especially between microbiologists and modellers, leading to collaborative publications over a 3-year timescale
* prepare several collaborative grant applications to BBSRC and EPSRC over a 3-year timescale

Our research objectives are to:

* bring together UK microbiologists, mathematicians and physical scientists, to use stochastic dynamical modelling for "real-life" problems pertaining to bacterial response and behaviour — specifically, contribute to understanding stochastic effects in gene regulation in response to environmental stresses, in communal living (biofilm formation and quorum sensing) and in gene transfer (especially plasmid regulation)
* encourage microbiologists to make measurements that can be directly used to test models for stochastic gene expression — advance existing techniques and develop new ones in stochastic dynamical modelling, through sharing of expertise between modellers

Our dissemination objectives are to:

* disseminate stochastic dynamical modelling to relevant scientific communities, including biologists, mathematicians, computer scientists and physicists
* disseminate cutting-edge software for stochastic simulation
* increase links with the overseas research community in this area by inviting overseas researchers to workshops and other events
* increase links with the UK health-care and food industries by inviting representatives to participate in network workshops

The aim of this project was not to generate new research findings but rather to create a new community at the microbiology-modelling interface. To this end, we organised 2 large conferences, 4 smaller focused workshops and 2 training-based summer schools, as well as setting up a website and mailing list.
The collaborations and interactions generated by our network contributed to 22 research papers by network members as well as numerous successful grant applications.