Flow Cytometry

A flow cytometer measures properties at the single cell level. Therefore culture variability can be detected and subpopulations with properties of interest can be detected. We also use automated flow cytometry to monitor and control cultures in bioreactors. Automated flow cytometry allows one to automatically retrieve a sample from a bioreactor, treat it reproducibly with desirable stains for quantification of cellular parameters, and analyze the stained sample. This procedure eliminates the sample to sample variation instrinsic in manual staining as well as allows continuous monitoring of a bioreactor.

For an introduction to flow cytometry, try the web-based training course available from Becton Dickinson
Recent publications of interest:
Gilbert A, Srienc F. Optimized evolution in the cytostat: a Monte Carlo simulation. Biotechnol Bioeng [Epub ahead of print]
Sitton G, Srienc F. Growth dynamics of mammalian cells monitored with automated cell cycle staining and flow cytometry. Cytometry A. 2008 Jun;73(6):538-45.
Sitton, G, Hansgate, A, Srienc, F. Transient gene expression in CHO cells monitored with automated flow cytometry. Cytotechnology. 2006 Sept. 52(1):13-24.
Kacmar J, Carlson R, Balogh SJ, Srienc F. Staining and quantification of poly-3-hydroxybutyrate in Saccharomyces cerevisiae and Cupriavidus necator cell populations using automated flow cytometry. Cytometry A. 2006 Jan;69(1):27-35.

Biopolymers: Polyhydroxyalkanoate Production

Polyhydroxyalkanoate (PHAs) are biodegradable polymers which are synthesized by microbial organisms. The metabolic pathways that produce the polymers as well as controlling the properties of the synthesized polymers are of particular interest. Various feeding strategies and polymer production control mechanisms are employed to achieve our goals.

Recent publications of interest:
Gorke JT, Okrasa K, Louwagie A, Kazlauskas RJ, Srienc F. Enzymatic synthesis of poly(hydroxyalkanoates) in ionic liquids. J Biotechnol. 2007 Nov 1;132(3):306-13.
McChalicher CW, Srienc F. Investigating the structure-property relationship of bacterial PHA block copolymers. J Biotechnol. 2007 Nov 1;132(3):296-302.
Pederson, EN., McChalicher, CMJ., Srienc, F. Bacterial Synthesis of PHA Block Copolymers.
Biomacromolecules, 2006 Jun;7(6):1904-11.

Metabolic/Population Modeling and Metabolic Engineering

We use elementary mode analysis as a tool to determine the minimum number of reactions necessary for cellular growth. If a reaction is determined to be unnecessary for cellular growth, then the gene responsible for producing the enzymes that perform that reaction can be knocked out. By limiting the number of enzymes to only the minimum required, the cell should produce an optimized amount of biomass. Furthermore, we use population balance models, which are the most accurate models as they account for culture variability at the single cell level, to predict culture behavior. In order to use population balance models, one must integrate a tool such as flow cytometry in order to extract the necessary growth information from experiments.

Recent publications of interest:
Trinh CT, Unrean P, Srienc F. A Minimal Escherichia coli Cell for most Efficient Ethanol Production from Hexoses and Pentoses. Appl Environ Microbiol. 2008 Jun;74(12):3634-43.
Trinh, C.T., Carlson, R., Wlaschin, A. and Srienc, F. Design, construction and performance of the most efficient biomass producing E. coli bacterium. 2006 Nov;8(6):628-38.
Wlaschin AP, Trinh CT, Carlson R, Srienc F.The fractional contributions of elementary modes to the metabolism of Escherichia coli and their estimation from reaction entropies. Metab Eng. 2006 Jul;8(4):338-52.
Carlson, R., and Srienc, F. Effects of recombinant precursor pathway variations on poly[(R)-3-hydroxybutyrate] synthesis in Saccharomyces cerevisiae. J Biotech. 2006 Jul 25;124(3):561-73.

Systems Biology

Systems biology is an important approach to biology research. In particular, we are interested in the single cell variability, the presence of subpopulations, and the effect on microarray results. To deconvolute the average gene expression patterns in terms of its pure subpopulations, we are developing strategies to probe mRNA transcripts using cDNA microarrays from a small number of identical Saccharomyces cerevisiae and Escherichia coli cells.

Recent publications of interest:
Sangurdekar DP, Srienc F, Khodursky AB.A classification based framework for quantitative description of large-scale microarray data.
Genome Biol. 2006;7(4):R32.