CLUSTER 2: Meta-’omics and systems biology
To understand microbial communities in any natural or engineered systems to the level where informed control can be applied requires the development and application of a range of cutting edge, high resolution, genomics, imaging and analytical technologies. SCELSE's cross-cluster research allows the structure, function, dynamics and interactions of the millions of different microorganisms in complex biofilm communities to be analysed. Cluster 2 applies a comprehensive top-down approach to identify all members of these communities, what they do, and how the responses of the entire biofilm community reflect the conditions of their environment and the performance of the systems. This approach is spearheaded by analysis of all members of the community, their genes, the expression of these genes, and the protein and metabolic profile at any given time.
The biofilm composition is determined by metagenomic sequencing. This approach, allied to bioinformatics, is also used to analyse community transcription, metaproteomics (whole community protein identification) and for analysis of small molecules by metabolomics (including stable isotope mass spectrometry) to reveal the expression and modification of all gene products, and identify key points of control of community output. The overall communal biology is assessed using a systems biology approach that identifies the network of interactions within the community, the responses to environmental and engineering conditions and hence key points of regulation and control of community output. The 3D spatial distribution and activity of the various members within biofilm communities are determined using in situ fluorescent tagging techniques, and high-resolution imaging and microscale sensors. This combined whole community experimental approach provides unprecedented understanding of community composition and properties of environmental and engineered biofilms. The data on community activity are used to develop ecological models that explain the behaviour and emergent properties of biofilm communities. These are calibrated with laboratory model biofilm systems (Cluster 3) and at the process scale (Cluster 1) to translate the fundamental understanding of biofilm behaviour to process management strategies. Crucially, translation from fundamental knowledge to engineering solutions requires the robust quantitative analysis of this cluster.
The massive information base generated by SCELSE's cross cluster research approach, will be deposited into a new "Virtual Biofilm" International Database. The database will contain increasingly expansive information on the inter-relationships across engineering parameters, process outcome and biofilm structure-function, with each successive round of Cluster 1 to 4 experimentation. The database will also provide the basis for devising predictive "rules" for optimising process outcome. Development of software that can effectively mine the database supports the formulation of new hypotheses on how to optimally manipulate model, environmental and engineered biofilm systems. Such databases generate an increasingly defined framework for the continuous refinement of model microbial communities at relevant microscales, for the discovery of novel biological networks and the operational conditions set for the bioprocesses and development of new engineering platforms.
This is the first time that such a key tool for design and optimisation of biofilm-driven processes is being developed.