Gene sequence analysis and computer modeling to identify and screen new proteins
BEEM researchers will sequence microbial cultures and enzymes that result from lab reserach in fermentation and biodegradation, and will also work with existing genomic libraries. This work results in large-scale genomic "parts lists" and making sense of these enormous datasets requires the use of sophisticated computational tools. The aim is to use bioinformatics methods on the sequencing data to construct a model of exactly what is going on at a cellular and molecular level within the cultures, i.e. to get a clear picture of the metabolism and cell interactions involved in the processes of interest. This will allow researchers to discover new enzymatic pathways and will suggest ways to optimize the desired microbial activities. The BEEM team includes metabolic modeling experts who are leaders in this field and will develop and apply new bioinformatics tools to extract meaning from these large metagenomic datasets.
The first step in this process is the analysis and annotation of metagenomic sequence data. Next, computer models of metabolic networks are constructed based on sequence similarity, physiology and biochemical information from the whole community and enzyme screens. The reconstructed network is then used to develop genome-scale models of whole community function as well as pathway-specific models. These models elucidate the metabolic capabilities of the community and how to optimize these microbial cultures and enzymes. This work will be useful for:
- visualization and mining gene expression data
- identification and reconstruction of novel metabolic pathways responsible for the cultures' observed biotransformation activity
- identification of imporatnt metabolic interactions between members of the microbial community
- optimization of the culture's activity through the addition of either substrates that promote growth or other organisms that provide these substrates
Metabolic models will help to identify the individual metabolic reactions that may not be detected in the enzyme screens, but are required for the overall culture activity. This information will feed back to the lab by suggesting ways to optimize the cultures' performance that can be tested in microcosms. At the same time, the models will be continuously refined based on the biochemical and physiological studies performed in the lab. Ultimately, this framework will enable the a priori prediction of novel biotransformation capabilities of the cultures, identify the optimal consortia with the desired activities, and facilitate the development of viable bioproducts and bioprocesses.
