Developing models to predict the response of microbial communities to changes in substrate diversityEPA Grant Number: U915557
Title: Developing models to predict the response of microbial communities to changes in substrate diversity
Investigators: Artuz, Robert J.
Institution: Stanford University
EPA Project Officer: Lee, Sonja
Project Period: January 1, 1999 through May 28, 2003
Project Amount: $102,000
RFA: STAR Graduate Fellowships (1999) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Engineering and Environmental Chemistry , Fellowship - Civil/Environmental Engineering
The objective of this research project is to grasp an understanding and develop powerful models for the degradation of contaminants that plague our environment. We lack a useful understanding of the structure of microbial communities. Consequently, we can adopt a working hypothesis: the degradation of synthetic organic chemicals is conducted by specialists. I can test this hypothesis and identify a unique relationship between the structure of microbial communities and the degradation of diverse substrates. Particularly, we can use terminal-restriction fragment length polymorphism (T-RFLP) at Stanford to fully understand microbial communities used in bioremediation technology. The strategy of this approach is to isolate rDNA from a microbial community as a template for polymerase chain reaction (PCR) amplification of 16S ribosonal ribonucleic acid (rRNA) genes. This is followed by splitting the amplified rRNA strands into fragments using restriction enzymes. Subsequently, the terminal fragment lengths can be measured using a gene sequencer. These terminal fragment lengths can be compared to a gene database that can match a specific organism to each fragment length. Database software can finally estimate the community structure of the original sample. My objective will allow me to understand how microbial community structure changes given different environmental conditions, and substrate diversity, in particular.
This research can be very useful in developing models to predict the response of microbial communities to changes in substrate diversity. For example, an industrial wastewater treatment system is an excellent place to start. If this system is loaded with a given set of substrates, what level of microbial diversity will be required to successfully treat the wastewater? Maybe we only need a handful of organisms, or maybe our handful of organisms will be overcome by the diverse contaminants. Similarly, if we begin with a diverse community, what specific community members are affected most by substrate perturbations?
I will assign an index number to a medium that can reflect its level of substrate diversity. A high index number would suggest that there are multiple hierarchical pathways required to break down a given set of substrates. This will be a difficult task, but one way of determining if two substrates are alike is to compare the enzymes being used to degrade them. If two enzymes are similar, then the substrates they degrade are most likely similar as well. Fortunately, enzymes are easier to categorize than substrates. They have unique structures and functions that should allow me to create a phylogenetic tree of enzymes. Ultimately, a substrate diversity index may be derived from this tree. I have already considered using ecological models to assign these indices such as the Shannon-Weiner Diversity Index. Once I have a firm grasp on diversity indices, my research focus will move to the laboratory. Experiments involving the aerobic degradation of substrates will allow me to find a mathematical relationship between substrate diversity and microbial diversity.