Laurence Yang PhD

• PhD (Chemical Engineering) - University of Toronto (2012)
• MASc (Chemical Engineering) – University of Toronto (2008)
• BASc (Chemical Engineering) – University of Toronto (2006)

Research Experience

• Assistant Project Scientist, Department of Bioengineering, UCSD (2018-2019)
• Postdoctoral Scholar, Department of Bioengineering, UCSD (2014-2017)
• Scientist, Intrexon Corporation (2012-2014)

Our lab develops predictive models of cell metabolism, protein expression, and gene regulation. Specifically, we develop holistic models that integrate multiple biological processes and large-scale networks. Interpreting biological data in the context of these integrated models provides a systems-level perspective on cellular functions.

Our lab trains systems biologists from two angles. First, trainees will learn to express their biological knowledge and intuition in the form of computable, mathematical models. These models then provide an in silico platform to virtually test hypotheses and to design efficient experiments. Second, researchers have ample opportunity to deploy machine learning and distributed algorithms on big biological data sets. These algorithms can improve the accuracy of model predictions, or to help understand biological mechanisms by constructing explainable models from data.

Winter 

CHEE 340: Biomedical Engineering

Teaching experience

(Co-taught courses in the department of Bioengineering at UCSD)

• BENG 213 (Spring 2019). Systems Biology and Bioengineering III: Building and Simulating Large-Scale In Silico Models
• BENG 212 (Spring 2017). Systems Biology and Bioengineering II: Network Reconstruction

Please see complete publication list on Google Scholar.

• L Yang†, N Mih, JT Yurkovich, JH Park, S Seo, D Kim, JM Monk, CJ Lloyd, TE Sandberg, SW Seo, D Kim, AV Sastry, P Phaneuf, Y Gao, JT Broddrick, K Chen, D Heckmann, R Szubin, Y Hefner, AM Feist, BO Palsson† (2019). Cellular responses to reactive oxygen species are predicted from molecular mechanisms. Proc Natl Acad Sci USA, doi:10.1073/pnas.1905039116
• L Yang†, MA Saunders, JC Lachance, BO Palsson, J Bento† (2019). Estimating Cellular Goals from High-Dimensional Biological Data. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Accepted) | arXiv :1807.04245
• L Yang†, A Ebrahim, CJ Lloyd, MA Saunders, BO Palsson (2019). DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. BMC Systems Biology 13 (1), 2
• JC Lachance, CJ Lloyd, JM Monk, L Yang, AV Sastry, Y Seif, BO Palsson, S Rodrigue, AM Feist, ZA King, P Jacques (2019). BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Computational Biology 15 (4), e1006971
• A Anand, CA Olson, L Yang, AV Sastry, E Catoiu, KS Choudhary, PV Phaneuf, TE Sandberg, S Xu, Y Hefner, R Szubin, AM Feist, BO Palsson (2019). Pseudogene repair driven by selection pressure applied in experimental evolution. Nature Microbiology 4 (3), 386
• CJ Lloyd, A Ebrahim, L Yang, ZA King, E Catoiu, EJ O’Brien, JK Liu, BO Palsson (2018). COBRAme: A computational framework for genome-scale models of metabolism and gene expression. PLoS Computational Biology 14 (7), e1006302
• Y Gao, JT Yurkovich, SW Seo, I Kabimoldayev, A Dräger, K Chen, AV Sastry, X Fang, N Mih, L Yang, J Eichner, BK Cho, D Kim, BO Palsson (2018). Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655. Nucleic Acids Research 46 (20), 10682-10696
• ES Kavvas, E Catoiu, N Mih, JT Yurkovich, Y Seif, N Dillon, D Heckmann, A Anand, L Yang, V Nizet, JM Monk, BO Palsson (2018). Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nature Communications 9 (1), 4306
• L Yang†, JT Yurkovich, ZA King, BO Palsson (2018). Modeling the multi-scale mechanisms of macromolecular resource allocation. Current Opinion in Microbiology 45, 8-15
• I Kabimoldayev, AD Nguyen, L Yang, S Park, EY Lee, D Kim (2018). Basics of genome-scale metabolic modeling and applications on C1-utilization. FEMS Microbiology Letters 365 (20), fny241
• JT Yurkovich, L Yang, BO Palsson (2018). Toward a Proteome-Complete Computational Model of the Human Red Blood Cell. Blood 132 (Suppl 1), 4888-4888
• X Fang, A Sastry, N Mih, D Kim, J Tan, JT Yurkovich, CJ Lloyd, Y Gao, L Yang†, BO Palsson† (2017). Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. Proc Natl Acad Sci USA 114 (38), 10286-10291
• D Ma, L Yang, RMT Fleming, I Thiele, BO Palsson, MA Saunders (2017). Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. Scientific Reports 7, 40863
• JT Yurkovich, DC Zielinski, L Yang, G Paglia, O Rolfsson, OE Sigurjonsson, JT Broddrick, A Bordbar, K Wichuk, S Brynjolfsson, S Palsson, S Gudmundsson, BO Palsson (2017). Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. Journal of Biological Chemistry 292 (48), 19556-19564
• K Chen, Y Gao, N Mih, EJ O’Brien, L Yang, BO Palsson (2017). Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc Natl Acad Sci USA 114 (43), 11548-11553
• JT Yurkovich, L Yang, BO Palsson (2017). Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells. PLoS Computational Biology 13 (3), e1005424
• E Brunk, KW George, J Alonso-Gutierrez, M Thompson, E Baidoo, G Wang, CJ Petzold, D McCloskey, J Monk, L Yang, EJ O'Brien, TS Batth, HG Martin, A Feist, PD Adams, JD Keasling, BO Palsson, TS Lee (2016). Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. Cell Systems 2 (5), 335-346
• L Yang, D Ma, A Ebrahim, CJ Lloyd, MA Saunders, BO Palsson (2016). solveME: fast and reliable solution of nonlinear ME models. BMC Bioinformatics 17 (1), 391
• L Yang, JT Yurkovich, CJ Lloyd, A Ebrahim, MA Saunders, BO Palsson (2016). Principles of proteome allocation are revealed using proteomic data and genome-scale models. Scientific Reports 6, 36734
• L Yang, J Tan, EJ O’Brien, JM Monk, D Kim, HJ Li, P Charusanti, ... (2015). Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc Natl Acad Sci USA 112 (34), 10810-10815
• L Yang, S Srinivasan, R Mahadevan, WR Cluett (2015). Characterizing metabolic pathway diversification in the context of perturbation size. Metabolic Engineering 28, 114-122
• K Zhuang, L Yang, WR Cluett, R Mahadevan (2013). Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnology 13 (1), 8
• P Gawand, L Yang, WR Cluett, R Mahadevan (2013). Metabolic model refinement using phenotypic microarray data. Systems Metabolic Engineering 47-59
• L Yang, WR Cluett, R Mahadevan (2011). EMILiO: a fast algorithm for genome-scale strain design. Metabolic Engineering 13 (3), 272-281
• S Garg, L Yang, R Mahadevan (2010). Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling. BMC Research Notes 3 (1), 125
• L Yang, R Mahadevan, WR Cluett (2010). Designing experiments from noisy metabolomics data to refine constraint-based models. Proceedings of the 2010 American Control Conference, 5143-5148
• L Yang, WR Cluett, R Mahadevan (2010). Rapid design of system-wide metabolic network modifications using iterative linear programming. IFAC Proceedings Volumes 43 (5), 391-396
• L Yang, R Mahadevan, WR Cluett (2008). A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks. Computers & Chemical Engineering 32 (9), 2072-2085