Fong:Research

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The Fong Lab is interested in bridging the gap between fundamental understanding of biological systems and the novel applications of this knowledge for society's benefit.

Major areas of interest are:

Systems biology

Systems biology has returned systems-level perspectives to biology. This approach is based on the fundamental understanding that biological systems are complex and highly interconnected. Thus, studying single biological components in isolation is not sufficient to fully describe the functionality of the integrated system.

In practice, this discipline has been associated with high-throughput analytical tools that allow simultaneous analysis of all biological components. One of the great challenges in this area is interpretation and integration of these data.

Evolutionary biology

Using microorganisms with fast growth rates allows biological adaptation to be investigated experimentally. Experiments of this nature allow us to study fundamental aspects of biology such as how pathogens become resistant to antibiotics and the relationship between genotype and phenotype. In this area, our lab has studied:

  1. Computational methods for predicting growth behaviors of evolved E. coli
  2. Reproducibility and adaptability of E. coli evolution
  3. Molecular changes occurring during evolution using mRNA transcriptional profiling and metabolic flux analysis

Metabolic engineering

Metabolic engineering intends to intellegently alter the functionality of an organism for a desired purpose, often the production of a chemical of interest. Within a systems perspective, this is accomplished by analyzing how single modifications have effects on an entire biological system.

Progress in this area includes:

  1. Computational modeling and prediction of strain designs for chemical production.
  2. Construction and evolution of 3 strains of E. coli for production of lactic acid.
  3. Characterization of production strains using mRNA transcriptional profiling.

Novel solutions for sustainable production of biofuels

In the face of increasing energy demand, finite petroleum supplies, and urgent environmental concerns, it will be necessary to develop sustainable replacements for liquid petroleum fuels which can be quickly scaled for industrial production and incorporated into existing infrastructure. Our lab is working in a number of ways to address these issues.

Cellulosic ethanol

Cellulose makes up roughly 60% of the dry weight of all plant biomass on earth and therefore represents an extremely abundant and sustainable feedstock for the production of liquid fuels. Feedstocks such as switchgrass grown on marginal farmland, milled agricultural waste, and waste paper pulp can be biochemically converted to ethanol, thereby meeting a significant portion of the energy demand without affecting food supplies in the way that corn ethanol can.

We are working to develop cellulosic ethanol biotechnology by

  1. Optimizing biomass pretreatment processes to maximize enzyme accessibility and minimize chemical inhibition of downstream fermentation processes,
  2. Developing genome-scale constraint based models of a number of cellulolytic bacteria, including Clostridium thermocellum,
  3. Examining fermentation characteristics and gene regulation motifs in the cellulolytic bacterium Thermobifida fusca

Algal feedstocks for biofuel production

Publications

  1. Fong, S.S. "Evolutionary engineering of industrially important microbial phenotypes" in Metabolic Pathway Engineering Handbook. CRC press. In press.

    [FongCRC]
  2. Fong, S.S. "Genome-Scale Assessment of Phenotypic Changes during Adaptive Evolution" in Introduction to Systems Biology. Humana Press. In press.

    [FongHumana]
  3. Apte AA, Cain JW, Bonchev DG, and Fong SS. Cellular automata simulation of topological effects on the dynamics of feed-forward motifs. J Biol Eng. 2008 Feb 27;2:2. DOI:10.1186/1754-1611-2-2 | PubMed ID:18304325 | HubMed [Apte2008]
  4. Hua Q, Joyce AR, Palsson BØ, and Fong SS. Metabolic characterization of Escherichia coli strains adapted to growth on lactate. Appl Environ Microbiol. 2007 Jul;73(14):4639-47. DOI:10.1128/AEM.00527-07 | PubMed ID:17513588 | HubMed [Hua2007]
  5. Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, and Palsson BO. Systems approach to refining genome annotation. Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17480-4. DOI:10.1073/pnas.0603364103 | PubMed ID:17088549 | HubMed [Reed2006]
  6. Herrgård MJ, Fong SS, and Palsson BØ. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol. 2006 Jul 7;2(7):e72. DOI:10.1371/journal.pcbi.0020072 | PubMed ID:16839195 | HubMed [Herrgard2006]
  7. Hua Q, Joyce AR, Fong SS, and Palsson BØ. Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Biotechnol Bioeng. 2006 Dec 5;95(5):992-1002. DOI:10.1002/bit.21073 | PubMed ID:16807925 | HubMed [Hua2006]
  8. Fong SS, Joyce AR, and Palsson BØ. The econometrics of evolution. Nat Chem Biol. 2005 Sep;1(4):191-2. DOI:10.1038/nchembio0905-191 | PubMed ID:16408032 | HubMed [Fong2005]
  9. Fong SS, Nanchen A, Palsson BO, and Sauer U. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J Biol Chem. 2006 Mar 24;281(12):8024-33. DOI:10.1074/jbc.M510016200 | PubMed ID:16319065 | HubMed [Fong2006]
  10. Fong SS, Joyce AR, and Palsson BØ. Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res. 2005 Oct;15(10):1365-72. DOI:10.1101/gr.3832305 | PubMed ID:16204189 | HubMed [Fong2005b]
  11. Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, and Palsson BO. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng. 2005 Sep 5;91(5):643-8. DOI:10.1002/bit.20542 | PubMed ID:15962337 | HubMed [Fong2005c]
  12. Fong SS and Palsson BØ. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat Genet. 2004 Oct;36(10):1056-8. DOI:10.1038/ng1432 | PubMed ID:15448692 | HubMed [Fong2004]
  13. Fong SS, Marciniak JY, and Palsson BØ. Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model. J Bacteriol. 2003 Nov;185(21):6400-8. PubMed ID:14563875 | HubMed [Fong2003]
  14. Reed, J.L., Fong, S.S., Palsson, B.O. "Phenomics" in Microbial Diversity and Bioprospecting ASMPress 2003. (2003)

    [Reed2002]
  15. Francis K, Palsson B, Donahue J, Fong S, and Carrier E. Murine Sca-1(+)/Lin(-) cells and human KG1a cells exhibit multiple pseudopod morphologies during migration. Exp Hematol. 2002 May;30(5):460-3. PubMed ID:12031652 | HubMed [Francis2002]
  16. Lee GM, Fong SS, Oh DJ, Francis K, and Palsson BO. Characterization and efficacy of PKH26 as a probe to study the replication history of the human hematopoietic KG1a progenitor cell line. In Vitro Cell Dev Biol Anim. 2002 Feb;38(2):90-6. DOI:10.1290/1071-2690(2002)038<0090:CAEOPA>2.0.CO;2 | PubMed ID:11929001 | HubMed [Lee2002]
  17. Lee, G.M., Fong, S., Francis, K., Oh, D.J., Palsson, B.O. In situ labeling of adherent cells with PKH26. In Vitro Cell. & Developmental Biology Animal. 36(1):4-6. (2000)

    [Lee2000]
  18. Pazzano D, Mercier KA, Moran JM, Fong SS, DiBiasio DD, Rulfs JX, Kohles SS, and Bonassar LJ. Comparison of chondrogensis in static and perfused bioreactor culture. Biotechnol Prog. 2000 Sep-Oct;16(5):893-6. DOI:10.1021/bp000082v | PubMed ID:11027186 | HubMed [Pazzano2000]
All Medline abstracts: PubMed | HubMed