SYSTEMS BIOTECHNOLOGY

Biotechnology is undoubtedly recognized as 21st century-leading technology due to the potentials of producing high-profit pharmaceuticals, replacing commercial chemical products, and generating new markets with innumerable products from biodiversity. Biotechnology also meets the needs for clean technology through biodegradable products and environment-friendly bioprocess. Until now, biotechnology processes have been developed through labor- and time- intensive way. High cost and time taking development process has been unavoidable due to complexity of cellular networks, difficulties in biological experiments, and unpredictable outcomes. 

Rational reengineering of biology for the purpose of bioremediation, bioenergy or biorefinery requires deep understanding of all functional interactions of relevant components within native cell(s). The rational circuit design is becoming critical to develop bioprocesses for overproduction of bioproducts. The high throughput experimental tools conducting thousands of analyses in parallel, genome-wide experiments and rapid accumulation of biological data provide a foundation for profound understanding of biological process. Integration of multiple omics data and in silico modeling and simulation of biological networks thereby is becoming critical to design and re-engineer microorganisms for the efficient industrial production of recombinant proteins, biofuels, and a variety of bioproducts. This means biotechnology process can be developed in rational and systematic way – we referred this as ‘systems biotechnology’ - rather than traditional trial and error approach. Systems biotechnology can provide deep insight of identification of target gene or pathway for overproducing desired products and influence the speed and efficiency of process development.

We are conducting integrative analysis of genomic architecture and composition, transcriptome and proteome structure/function, protein-protein and protein-DNA interactions and metabolic/regulatory networks to re-engineer industrial microorganisms. A key aspect of our approach is to use the power of systems biology to understand how complex biological processes operate. Our systems approach will reduce time and labor significantly to develop biotechnology processes for pharmaceuticals and biorefinery products such as biopolymers and key chemical products.

DEVELOPMENT OF MICROBIAL CELL FACTORY

- Escherichia coli

Derivatives of E. coli B have been the major workhorse for production of recombinant foreign proteins and various biomaterials including biofuels in the labs and in industry. Strain REL606 has been used for experimental evolution studies, while BL21(DE3) is a favorite of molecular biologists, structural biologists, protein engineers, and production managers of the bioindustry. B strain often shows phenotypes distinct from those of K-12 - faster growth rate in minimal media, lesser production of acetate, superiority in foreign protein expression, and lesser tendency for degrading foreign proteins during purification. We have determined genome sequences of B strains - REL606 which has been applied to long-term experimental evolution study and BL21(DE3) which has been a cell-factory for overproducing recombinant proteins, biofuels, and a variety of bioproducts on an industrial scale. Until now, the bioprocess optimization using B has been carried out in a rather trial-and-error manner. This inefficient approach has been unavoidable, because of insufficient information about the metabolism and physiology of B. Therefore, systematic understanding of cellular physiology and metabolism of the strain is essential not only to determine culture condition and, but also to design recombinant hosts.

• • Jeong H, Lee S-W, Kim SH, Kim E-Y, Kim S, and Yoon SH “Global functional analysis of butanol-sensitive Escherichia coli and its evolved butanol-tolerant strain” J. Microbiol. Biotechnol. 27(6):1171–1179 (2017.06) 
• • Kim S, Jeong H, Kim E-Y, Kim JF, Lee SY, and Yoon SH “Genomic and transcriptomic landscape of Escherichia coli BL21(DE3)” Nucleic Acids Res. 45(9): 5285-5293 (2017.05)  
• • Yoon SH, Han MJ, Jeong H, Lee CH, Xia XX, Lee DH, Shim JH, Lee SY, Oh TK, and Kim JF “Comparative multi-omics systems analysis of Escherichia coli strains B and K-12” Genome Biology, 13:R37 (2012) Hanbitsa
• • Yoon SH, Kim SK, and Kim JF “Secretory production of recombinant proteins in Escherichia coli” Recent Patents on Biotechnology 4:23-29 (2010)
• • Yoon SH, Jeong H, Kwon S-K, and Kim JF “Chapter 1. Genomics, biological features, and biotechnological applications of Escherichia coli B: “Is B for better?!””, pp. 1-17, In: Systems Biology and Biotechnology of E. coli, Lee SY (ed.) Springer, Berlin, Germany (2009. 4)
• • Jeong H, Barbe V, Lee CH, Vallenet D, Yu D-S, Choi S-H, Couloux A, Lee S-W, Yoon SH, Cattolico L, Hur C-G, Park H-S, Ségurens B, Kim SC, Oh TK, Lenski RE, Studier FW, Daegelen P, and Kim JF “Genome sequences of Escherichia coli B strains REL606 and BL21(DE3)” J. Mol. Biol. 394:644-652 (2009)  
• • Barrick JE, Yu D-S, Yoon SH, Jeong H, Oh TK, Schneider D, Lenski RE, and Kim JF “Genome evolution and adaptation in a long-term experiment with Escherichia coli” Nature 461:1243-1247 (2009)
• • Yoon SH, Han MJ, Lee SY, Jeong KJ, and Yoo JS “Combined transcriptome and proteome analysis of Escherichia coli during the high cell density culture” Biotechnol. Bioeng. 81(7):753-767 (2003)

- Methanogen

Biologically produced methane gas (CH₄) is one of the major sources of greenhouse gases and can be used as carbon-neutral fuel. Methanogens catalyze the critical, methane-producing steps (called methanogenesis) in the anaerobic decomposition of organic matter. Most species of methanogens are hydrogenotrophic and use hydrogen gas (H₂) as the electron donor for the reduction of carbon dioxide (CO₂) to methane. Here, we present the first predictive model of global gene regulation of methanogenesis in Methanococcus maripaludis S2 which is a premier model for the hydrogenotrophic methanogens. 

• • Yoon SH, Turkarslan S, Reiss DJ, Pan M, Burn JA, Costa KC, Lie TJ, Slagel J, Moritz RL, Hackett M, Leigh JA, and Baliga NS “A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen” Genome Research 23(11):1839-1851 (2013.11) Hanbitsa
• • Costa KC, Yoon SH, Pan M, Burn JA, Baliga NS, and Leigh JA “Effects of H₂ and formate on growth yield and regulation of methanogenesis in Methanococcus maripaludis” J. Bacteriol. 195:1456-1462 (2013)
• • Yoon SH, Reiss DJ, Bare JC, Tenenbaum D, Pan M, Slagel J, Moritz RL, Lim S, Hackett M, Menon AL, Adams MW, Barnebey A, Yannone SM, Leigh JA, and Baliga NS “Parallel evolution of transcriptome architecture during genome reorganization” Genome Research, 21:1892-1904 (2011)

DEVELOPMENT OF PLATFORM TECHNOLOGY FOR SYSTEMS METABOLIC ENGINEERING

Modeling and simulation of cellular process is extremely helpful to organize the available metabolic knowledge and to design the right experiments. Simulation of biological systems through metabolic modeling can provide crucial information concerning cellular behavior under interested genetic and environmental conditions, and thus lead to development of efficient biotechnology process with its predicting power. We are developing algorithm(s) for integrated transcriptional and metabolic network modeling and simulation. 

• • Kim SK, Lee D-H, Kim OC, Kim JF, and Yoon SH “Tunable control of an Escherichia coli expression system for the overproduction of membrane proteins by titrated expression of a mutant lac repressor” ACS Synthetic Biology 6(9): 1766-1773 (2017.09) 
• • Dai J⁺, Yoon SH⁺, Sim HY, Yang YS, Oh TK, Kim JF, and Hong JW “Charting microbial phenotypes in multiplex nanoliter batch bioreactors” Anal. Chem. 85:5892-5899 (2013) (⁺Co-first)  
• • Nam D, Yoon SH, and Kim JF “Ensemble learning of genetic networks from time-series expression data” Bioinformatics 23:3225-3231 (2007)

PATHOGENOMICS

Pathogen infections are among the leading causes of infirmity and mortality among humans and animals in the world. Until recently, it has been difficult to compile information to understand the generation of pathogen virulence factors as well as pathogen behaviour in a host environment. The study of Pathogenomics attempts to utilize genomic and metagenomics data gathered from high through-put technologies (e.g. sequencing or DNA microarrays), to understand microbe diversity and interaction as well as host-microbe interactions involved in disease states. The bulk of pathogenomics research concerns itself with pathogens that affect human health; however, studies also exist for plant and animal infecting microbes. [from Wikipedia] 

• • Yoon SH*, Park YK, and Kim JF “PAIDB v2.0: exploration and analysis of pathogenicity and resistance islands” Nucleic Acids Res. (Published in advance, October 21, 2014)
• • Yoon SH, Park YK, Lee S, Choi D, Oh TK, Hur C-G, and Kim JF “Towards pathogenomics: a web-based resource for pathogenicity islands” Nucleic Acids Res. 35:D395-D400 (2007)
• • Yoon SH, Hur C-G, Kang HY, Kim YH, Oh TK, and Kim JF “A computational approach for identifying pathogenicity islands in prokaryotic genomes” BMC Bioinformatics 6:184 (2005)