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Lisa Tucker-Kellogg

Assistant Professor


The Tucker-Kellogg Lab at Duke-NUS works on wound healing and computational modeling. The wound healing side of the lab studies how muscle tissue is injured and repaired, and our main experimental approaches are microscope imaging of endogenous regeneration, and molecular assays of cells in vitro. The majority of this work is "wetlab" although it does include computational image analysis and computational modeling. The computational modeling side of the lab studies a variety of biological problems provided by experimental collaborators, as well as the wound healing problems provided by our own experiments. Computational modeling allows us to take a series of snapshot measurements, whether from images or molecular concentrations, and infer something indirectly about the underlying dynamic mechanisms that might give rise to the observed time-point measurements. This use of modeling to "connect the dots" of experimental data is particularly relevant to studying the up-and-down dynamics of cell signaling, or the flow of nutrients through metabolic networks.

Lab Overview

We build computational models that are tailored to specific biological questions, and we carry out experimental tests of model-based predictions, in collaboration with experimental and clinical collaborators.

Biological Questions

  • Cell behaviors during injury response & oxidative stress.
  • Extracellular signalling and post-translational signalling.
  • The organization of spatially-specific and temporally-specific signals during injury response.

Computational Methods

  • Quantifying the trends and slopes for temporal and spatiotemporal effects.
  • Ordinary differential equations (ODEs) describing temporal effects, computed repeatedly over space.
  • Bayesian network models and probabilistic networks, for poorly-understood chains of causal influence.
  • Quantifying relationships between cell morphology and biological function, (collab with bioimaging).
  • Custom built models (ball and stick, spring and dashpot, or autonomous agents following rules).

Probabilistic Methods for Modeling

  • SPEDRE: a Web Server for Estimating Rate Parameters of Cell Signaling Dynamics in Data-Rich Environments." By Tri Hieu Nim, Jacob K. White, Lisa Tucker-Kellogg* Nucleic Acids Research, ePub doi: 10.1093/nar/gkt459 (2013). (OxfordJournals)

  • “Systematic Parameter Estimation in Data-Rich Environments for Cell Signaling Dynamics.” By Tri Hieu Nim, Le Luo, Marie-Véronique Clément, Jacob K. White, Lisa Tucker-Kellogg*. Bioinformatics 29(8):1044-1051(2013). (OxfordJournals)

  • “Reactive Oxygen Species (ROS) and Sensitization to TRAIL-Induced Apoptosis, in Bayesian Network Modeling of HeLa Cell Response to LY303511.” By Lisa Tucker-Kellogg, Yuan Shi, Jacob K.White, Shazib Pervaiz. Biochemical Pharmacology, 84 (10): 1307-17 (2012). (ScienceDirect)

  • “An anti-hepatofibrotic drug efficacy predictor that correlates and predicts in vivo drug response based on in vitro high-content analysis” By B Zheng, L Tan, X Mo, W Yu, Y Wang, L Tucker-Kellogg, R Welsch, P So, H Yu*. PLoS One 6(11): e26230(2011). (PLoS)

  • “Composing Globally Consistent Pathway Parameter Estimates Through Belief Propagation.” By G Koh, L Tucker-Kellogg, D Hsu and PS Thiagarajan. Proceedings of the 7th Workshop on Algorithms in Bioinformatics (WABI), Lecture Notes in Bioinformatics 4645: 420-430 (2007). (SpringerLink)

Modeling Synergistic, Additive, or Antagonistic Combination Effects

  • “Computational Modeling of LY303511 and TRAIL-Induced Apoptosis Suggests Dynamic Regulation of cFLIP.” By Yuan Shi, Gregory Mellier, Sinong Huang, Jacob White, Shazib Pervaiz* , Lisa Tucker-Kellogg *. Bioinformatics 29(3): 347-354. (2013). (OxfordJournals)

  • “The synergy in cytokine production through MyD88-TRIF pathways is co-ordinated with ERK phosphorylation in macrophages.” By RST Tan, B Lin, Q Liu, L Tucker-Kellogg, B Ho, BPL Leung, JL Ding*. Immunology and Cell Biology (in press, 2013). (Nature)

  • “FLIP: a Flop for Execution Signals.” By K Subramaniam, JL Hirpara, L Tucker-Kellogg, G Tucker-Kellogg, S Pervaiz* Cancer Letters, ePub Jul 7 (2012). (ScienceDirect)

  • "Simulating EGFR-ERK Signaling Control by Scaffold Proteins KSR and MP1 Reveal Differential Ligand-Sensitivity co-regulated by Cbl-CIN85 and Endophilin." By Lu Huang, Catherine Q Pan, B Li, Lisa Tucker-Kellogg, Bruce Tidor, YZ Chen*, BC Low*. PLoS One. 2011;6(8):e22933; (2011). (PLoS)

  • “Computational Modeling of Pathway Dynamics For Detecting Drug Effects: Paradoxical Effects of LY303511 on TRAIL-Induced Apoptosis.” By Y Shi, SM Varghese, S Huang, J White, S Pervaiz*, L Tucker-Kellogg * . Computational Systems Bioinformatics “CSB09”, 8: 213-224 (2009). (Life Sciences Society)

Extracellular Signaling in Injury and Fibrosis

  • "The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation." By Wang J, Tucker-Kellogg L, Ng IC, Jia R, Thiagarajan PS, White JK, Yu H. PLoS Computational Biology 10(6):e1003573 (2014). (PLoS)
  • "Extracellular Hemoglobin Upregulates and Binds to Tissue Factor on Macrophages: Implications for Coagulation and Oxidative Stress." By Neha Bahl, Imelda Winarsih, Lisa Tucker-Kellogg *, Ding Jeak Ling *. Thrombosis and Haemostasis 111(1):67-78(2014). (Schattauer)
  • "The CD47-binding Peptide of Thrombospondin-1 Induces Defenestration of Liver Sinusoidal Endothelial Cells." By L. Venkatraman and L. Tucker-Kellogg*. Liver International 33(9):1386-97 (2013). (Wiley)
  • “Hepatic stellate cell-targeted delivery of hepatocyte growth factor transgene via bile duct infusion enhances its expression at fibrotic foci to regress dimethylnitrosamine-induced liver fibrosis.” By BC Narmada, Y Kang, L Venkatraman, Q Peng, RB Sakban, BNX Jiang, RM Bunte, PTC So, L Tucker-Kellogg, HQ Mao* & H Yu*. Human Gene Therapy 24(5):508-19 (2013). (Liebert
  • “HGF regulates the activation of TGF-ß1 in rat hepatocytes and hepatic stellate cells.” By BC Narmada, S-M Chia, L Tucker-Kellogg*, and H Yu.* Journal of Cellular Physiology, 228 (2): 393-401. (2013). (ePub June 20, 2012) (Wiley)
  • “Plasmin Triggers a Switch-like Decrease in Thrombospondin-Dependent Activation of TGF-β1” By Lakshmi Venkatraman, Ser-Mien Chia, Balakrishnan Chakrapani Narmada, Liang Siang Poh, Huipeng Li, Rashidah Sakban, Rui Rui Jia, Shali Shen, Jacob K. White, Sourav Saha Bhowmick, C. Forbes Dewey, Jr., Peter T. So, Lisa Tucker-Kellogg * , Hanry Yu.* Biophysical Journal, 103: 1060–1068.(2012). (Cell Press)
  • "Systems Biology in Biomaterials and Tissue Engineering.” A. Ananthanarayanan, L. Tucker-Kellogg, B.C. Narmada, L. Venkatraman, N.A. Abdul Rahim, Y. Wang, C.H. Kang, H. Yu. Comprehensive Biomaterials, Vol. 5, p.177-188 (2011). (ScienceDirect
  • “The Steady States and Dynamics of Urokinase-mediated Plasmin Activation, in Silico and in Vitro” By L Venkatraman, H Li, CF Dewey Jr, JK White, SS Bhowmick, H Yu *, L Tucker-Kellogg* Biophysical Journal 101:1825–1834 (2011). (Cell Press)
  • “Predicting in vivo anti-hepatofibrotic drug efficacy based on in vitro high-content analysis” By B Zheng, L Tan, X Mo, W Yu, Y Wang, L Tucker-Kellogg, R Welsch, P So, H Yu*. PLoS One 6(11): e26230(2011). (PLoS)
  • “Cell-Delivery Therapeutics for Liver Regeneration” By W-X Zhang, L Tucker-Kellogg (joint first author), BC Narmada, L Venkatraman, S Chang, Y Lu, H Yu*. Advanced Drug Delivery Reviews 62(7-8):814-26 (2010). (ScienceDirect)
  • “The Steady States and Dynamics of Urokinase-Mediated Plasmin Activation” By L Venkatraman, H Yu, SS Bhowmick, CF Dewey Jr., L Tucker-Kellogg . Pacific Symposium on Biocomputing 15:190-199 (2010). (Pubmed)

Structural Modeling Methods

  • “Delineation of Lipopolysaccharide (LPS)-binding Sites on Hemoglobin: FROM IN SILICO PREDICTIONS TO BIOPHYSICAL CHARACTERIZATION.” By N Bahl, R Du, I Winarsih, B Ho, L Tucker-Kellogg, B Tidor, and JL Ding*. J. Biol. Chem. 286(43):37793-803 (2011). (Pubmed)
  • pFlexAna: Detecting Conformational Changes in Remotely Related Proteins.” By A Nigham, L Tucker-Kellogg, I Mihalek, C Verma, and D Hsu *.Nucleic Acids Research, 36: W246–W251 (2008). (OxfordJournals)
  • “De novo determination of peptide structure with solid-state magic-angle spinning NMR spectroscopy.” by Rienstra, Tucker-Kellogg, Jaroniec, Hohwy, Reif, McMahon, Tidor, Lozano-Pérez, Griffin*. Proc Natl Acad Sci U S A. 2002 Aug 6;99(16):10260-5. (PDF)
  • “Engrailed (Gln50–>Lys) homeodomain-DNA complex at 1.9 A resolution: structural basis for enhanced affinity and altered specificity.” Tucker-Kellogg, Rould, Chambers, Ades, Sauer, Pabo*. Structure. 1997;5(8):1047-54. (Pubmed)
  • “Local rule-based theory of virus shell assembly.” By Berger, Shor, Tucker-Kellogg, King*. Proc Natl Acad Sci U S A. 1994;91(16):7732-6. (PDF)

Heuristic Network Methods

  • “STEROID: In Silico Heuristic Target Combination Identification for Disease-Related Signaling Networks.” By Huey Eng Chua, Sourav S. Bhowmick, Lisa Tucker-Kellogg, C. Forbes Dewey, Jr. Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine – BCB2012, p.4-11 (2012). (ACM Press)
  • “In Silico Identification of End16 Regulators in the Sea Urchin Endomesoderm Gene Regulatory Network” By H-E Chua, SS Bhowmick, L Tucker-Kellogg, Q Zhao, CF Dewey Jr, H Yu. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium.- IHI2012, p.131-140 (2012). (ACM Press)
  • “PANI: an interactive data-driven tool for target prioritization in signaling networks.” By Chua, Huey-Eng, Sourav S. Bhowmick, Lisa Tucker-Kellogg, Qing Zhao, C. Forbes Dewey, Hanry Yu. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI'12). ACM Press. p.851-854 (2012). (ACM Press)
  • “PANI: A Novel Algorithm for Fast Discovery of Putative Target Nodes in Signaling Networks” By HE Chua, SS Bhowmick*, CF Dewey, Jr., L Tucker-Kellogg, and H Yu. Published at the ACM Conference on Bioinformatics, Computational Biology and Biomedicine “ACM BCB” p. 284-288 (2011). (ACM Press)