We use computational and mathematical modelling combined with the recombinant DNA tools of synthetic biology to study and engineer new genetic systems. With a focus on analysis to extract simple principles and mathematical models we study:
Genetic regulatory networks form dynamical systems that are driven by cellular physiology and the growth environment. These networks also affect (directly or indirectly) cell physiology, consuming resources, and generating off-target regulatory effects. These complex interactions are not well characterised and have hampered attempts to accurately design and engineer dynamical genetic systems. We develop analytical methods based on multi-channel fluorescence measurement and mathematical and biophysical modelling to characterise the behaviour of gene network components. These techniques aim to extract parameters of genetic components and build models that can accurately predict the dynamics of networks constructed from them. To this end we consider the population of cells, their environment, and the genetic constructs as a single large dynamical system and analyse it using methods from machine learning, evolutionary algorithms and parameter inference.
Coordination of populations, colonies, biofilms, and ecosystems of microbes is the next scale of synthetic biology. The principles of organisation and emergent phenomena of multicellular populations are also of basic research interest. We apply the results of our work on genetic dynamical systems to design and analyse regulatory networks operating in spatially constrained populations of microbes, such as surface growing bacterial biofilms. These experimental systems serve as both an engineering platform for application in e.g. biosensing or bioprocessing, and as a simple model to analyse multicellular coordination in general. Working with the Federici lab, these genetic networks are connected to downstream effectors of cell behaviour including cell growth, shape, and intercellular signalling. We use time-lapse fluorescence microscopy to track the dynamics, and develop computational models using GPUs to simulate these large populations of cells. Techniques including machine learning, evolutionary algorithms and parameter inference are used to analyse both models and data, with the aim of extracting simple principles to inform engineering practice.