Jennifer Linderman

University of Pennsylvania
PhD Chemical and Biochemical Engineering ’87
MSE Chemical and Biochemical Engineering ’84

University of Rochester
BS ChE ’82

Research in the Linderman group uses approaches from computational and systems biology to understand and manipulate cell responses. Current work includes:

• Multi-scale and multi-system approaches to understanding the immune response to Mycobacterium tuberculosis, focusing especially on cytokines, immunomodulation, antibiotic treatment, and generation of appropriate T cells developing mathematical models of signal transduction pathways, particularly for G-protein coupled receptors
• Identifying potential therapeutic targets and optimizing drug delivery for breast cancer treatment

• Women’s Initiatives Committee’s Mentorship Excellence Award, 2019
  AIChE
• Rackham Distinguished Graduate Mentor Award, 2017
  The University of Michigan
• Harold R. Johnson Diversity Service Award, 2016
  The University of Michigan
• Phi Kappa Phi, 2015
• CIC – Academic Leadership Program Fellow, 2012-2013
• Fellow, 2000
  American Institute for Medical and Biological Engineering (AIMBE)
• College of Engineering Teaching Excellence Award, 1997
  University of Michigan
• Faculty Recognition Award, 1996
  University of Michigan
• Chemical Engineering Department Excellence in Teaching Award, 1994
  University of Michigan
• NSF Presidential Young Investigator, 1989-1994
  National Science Foundation
• G.G. Brown Assistant Professor Chair, 1987-1990
  University of Michigan

See complete list of publications

Recent Journal Publications

• Marino S, Cilfone N, Mattila JT, Linderman JJ, Flynn JL, Kirschner DE. “Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection, Infection and Immunity 83: 324-338, 2015.
• Cilfone NA, Ford CB, Marino S, Mattila JT, Gideon HP, Flynn JL, Kirschner DE, Linderman JJ. Computational Modeling Predicts Interleukin-10 Control of Lesion Sterilization By Balancing Early Host-Immunity-Mediated Antimicrobial Responses With Caseation During Mycobacterium tuberculosis Infection, J. Immunology 194: 664-677, 2015.
• Pienaar E, Cilfone NA, Lin PL, Dartois V, Mattila JT, Butler R, Flynn JL, Kirschner D, Linderman JJ. A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. J. Theoretical Biology 367: 166-179, 2015.
• Gong C, Linderman JJ, Kirschner D. A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes. J. Mathematical Biosciences and Engineering 12: 625-642, 2015.
• Cilfone NA, Pienaar E, Thurber GM, Kirschner DE, Linderman JJ. A systems pharmacology approach towards the design of inhaled formulations of rifampicin and isoniazid for treatment of tuberculosis. CTP: Pharmacometrics and Systems Pharmacology 4: e22, 2015. DOI: 10.1002/psp4.22.
• Linderman JJ, Cilfone NA, Pienaar E, Gong C, Kirschner DE. A multi-scale approach to designing therapeutics for tuberculosis. Integrative Biology 7: 591-609, 2015.
• Chang SL, Cavnar SP, Luker KE, Takayama S, Luker GD, Linderman JJ. Cell, Isoform, and Environment Factors Shape Gradients and Modulate Chemotaxis. PLoS ONE 10(4): e0123450. doi:10.1371/journal/pone.0123450. 2015.
• Sumit M, Neubig RR, Takayama S, Linderman JJ. Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation. Integrative Biology 7: 1378-1386. DOI: 10.1039/c5ib00181a. 2015.
• Warsinske H, Ashley S, Linderman JJ, Moore B, Kirschner DE. Identifying mechanisms of homeostatic signaling in fibroblast differentiation. Bull Math Biol. DOI 10.1007/s11538-015-0096-2. 2015.
• Pienaar E, Dartois V, Linderman JJ, Kirschner DE. In silico evaluation and exploration of antibiotic tuberculosis treatment regimens. BMC Systems Biology. DOI: 10.1186/s12918-015-0221-8. 2015.
• Ziraldo C, Gong C, Kirschner DE, Linderman JJ. Strategic priming with multiple antigens can yield memory cell phenotypes optimized for infection with Mycobacterium tuberculosis: A computational study. Frontiers in Microbiology 6: 1477. doi: 10.3389/fmicb.2015.01477. 2016.
• Marino S, Gideon HP, Gong C, Mankad S, McCrone JT, Lin PL, Linderman JJ, Flynn JL, Kirschner DE. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome. PLoS Computational Biology 12(4): e1004804. doi:10.1371/journal.pcbi.1004804. 2016.
• Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE. Multi-scale model of Mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions. Infection and Immunity 84: 1650-1669, 2016.
• Warsinske HC, Wheaton A, Kim KK, Linderman JJ, Moore BB, Kirschner DE. Computational modeling predicts simultaneous targeting of fibroblasts and epithelial cells is necessary for treatment of pulmonary fibrosis. Frontiers in Pharmacology 7:183. doi: 10.3389/fphar.2016.00183. 2016.
• Sumit M, Takayama S, Linderman JJ. New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling. Integrative Biology, DOI: 10.1039/c6ib00178e, 2017.

Books and Chapters

• Fallahi-Sichani M, Marino S, Flynn JL, Linderman JJ, Kirschner DE. “A systems biology approach for understanding granuloma formation and function in tuberculosis”, in Systems Biology of Tuberculosis, J McFadden, DJV Beste, AM Kierzek, eds. Springer, 2012.
• Marino S, Fallahi-Sichani M, Linderman JJ, Kirschner DE. “Mathematical models of anti-TNF therapies and their correlation with tuberculosis”, in Antibody-mediated Drug Delivery Systems: Concepts, Technology and Applications, Y. Pathak and S. Benita, eds., John Wiley and Sons, 2012.
• Jovic, A, Takayama S, Linderman JJ. “Using microfluidics, real-time imaging and mathematical modeling to study GPCR signaling”, in G protein-coupled receptors: From Structure to Function (J. Giraldo and J-P Pin, eds.), The Royal Society of Chemistry, 2011.
• Woolf, P.J., and Linderman, J.J., “From the Static to the Dynamic: Three Models of Signal Transduction in G-protein Coupled Receptors”, in Biomedical Applications of Computer Modeling, A. Christopoulos, ed., pp. 87-108, 2000.
• Linderman, J.J., “Kinetic Modeling Approaches to Understanding Ligand Efficacy”, The Pharmacology of Functional, Biochemical, and Recombinant Receptor Systems, Handbook of Experimental Pharmacology Volume 148, T. Kenakin and J. A. Angus, eds., Springer-Verlag, pp. 119-146, 2000.
• Lauffenburger, D.A. and J.J. Linderman, Receptors: Models for Binding, Trafficking, and Signaling, Oxford University Press, 365 pp., 1993. Reprinted in paperback, 1996.
• Linderman, J.J. and D.A. Lauffenburger, “Receptor/Ligand Sorting Along the Endocytic Pathway”, Lecture Notes in Biomathematics, Vol. 78. Springer-Verlag, 165 pp., 1989.