476206 Biotechnological and Health Applications of Multiscale ME (Metabolism and protein Expression) Models

Sunday, November 13, 2016
Continental 4 & 5 (Hilton San Francisco Union Square)
Laurence Yang, Bioengineering, University of California, San Diego, La Jolla, CA

Research Interests:
  • Novel systems biology algorithms to analyze Biomedical Big Data--massive and diverse datasets capturing complex biological interactions and are difficult to analyze using conventional data analysis methods.
  • Multiscale models of metabolism, protein expression, and regulation, and multiscale optimization of bioprocesses.
  • Antimicrobial strategies using systems biology models and multiple –omics data (genomics, transcriptomics, proteomics, epigenomics.
  • Computer-aided microbe design for value-added chemicals, nutrients for animal feed, and therapeutic proteins.

Teaching Interests:

  • 6 years of Teaching Assistant experience for Process Dynamics and Control
  • Certification for completion of a 15-hour course on teaching university courses (at UCSD)
  • I make appropriate use of active learning techniques, where student learning is facilitated using in-class clicker (real-time survey) questions on complex concepts that activate existing knowledge, identify misconceptions, and improve knowledge retention. Additional modern pedagogical methods will be used, including the inverted classroom and a balance of formative (ongoing or real-time) assessment and summative (exams, final presentations, etc.) assessment.

Peer-Reviewed Journal Articles

  1. E Brunk, KJ George, J Alonso-Guttierrez, M Thompson, E Baidoo, G Wang, L Yang, D McCloskey, T Baath, C Petzold, J Monk, EJ O'Brien, HG Martin, A Feist, P Adams, JD Keasling, BO Palsson, and TS Lee (2016) Characterizing Strain Variation in Engineered E. coli Using a Multi-omics Based Work ow. Cell Systems 2: 335-346.
  2. L Yang, J Tan, EJ O'Brien, JM Monk, D Kim, HJ Li, P Charusanti, A Ebrahim, CJ Lloyd, JT Yurkovich, B Du, A Drager, A Thomas, Y Sun, MA Saunders, and BO Palsson (2015) A systems biology de nition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc Natl Acad Sci USA 112:10810-10815.
  3. L Yang, S Srinivasan, R Mahadevan, and WR Cluett (2015) Characterizing metabolic pathway diversi cation in the context of perturbation size. Metab Eng 28: 14-122.
  4. K Zhuang, L Yang, WR Cluett, and R Mahadevan (2013) Dynamic strain scanning optimization: an ecient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnol 13:8.
  5. L Yang, WR Cluett, and R Mahadevan (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13:272-281.
  6. S Garg, L Yang, and R Mahadevan (2010) Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling. BMC Research Notes, 3:125.
  7. L Yang, R Mahadevan, and WR Cluett (2008) A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks. Comput Chem Eng, 32:2072-2085.

Submitted and pre-print articles

  1. L Yang, D Ma, A Ebrahim, CJ Lloyd, MA Saunders, and BO Palsson. solveME: fast and reliable solution of nonlinear ME models. Under review.
  2. L Yang, A Ebrahim, CJ Lloyd, MA Saunders, BO Palsson. dynamicME: Dynamic simulation and re nement of integrated models of metabolism and protein expression. Under review.
  3. L Yang, JT Yurkovich, CJ Lloyd, A Ebrahim, MA Saunders, BO Palsson. Principles of proteome allocation are revealed using proteomic data and genome-scale models. Submitted.
  4. D Ma, L Yang, RMT Fleming, I Thiele, BO Palsson, MA Saunders (2016) Quadruple-precision solution of genome-scale models of Metabolism and macromolecular Expression. arXiv Preprint arXiv:1606.00054v1.

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