department of chemical engineering @ the university of michigan












recent publications   -   books and book chapters






Publications since 2010

For a complete list of publications in PubMed, click here.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas
Pienaar E, Linderman JJ, Kirschner DE. PLoS ONE. 2018.

The CXCL12/CXCR7 signaling axis, isoforms, circadian rhythms, and tumor cellular composition dictate gradients in tissue
Spinosa PC, Luker KE, Luker GD, Linderman JJ. PLoS ONE. 2017.

Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach
Pienaar E, Sarathy J, Prideaux B, Dietzold J, Dartois V, Kirschner D, Linderman JJ. PLoS Computational Biology. 2017.

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatement Regimens
Cicchese J, Pienaar E, Kirschner D, Linderman J. Cellular and Molecular Bioengineering. 2017.

New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling
Sumit M, Takayama S, Linderman JJ. Integrative Biology. 2017.

Multi-scale model of Mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions
Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE. Infection and Immunity. 2016.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis
Warsinke HC, Wheaton AK, Kim KK, Linderman JJ, Moore BB, Kirschner DE. Frontiers in Pharmacology. 2016.

Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study
Ziraldo C, Gong C, Kirschner DE, Linderman JJ. Frontiers in Microbiology. 6: 1477. 2016.

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome
Marino S, Gideo HP, Gong C, Mankad S, McCrone JT, Lin PL, Linderman JJ, Flynn JL, Kirschner DE. PLoS Comp Bio. 2016.

In silico evaluation and exploration of antibiotic tuberculosis treatment regimens
Pienaar E, Dartois V, Linderman JJ, Kirschner DE. BMC Systems Biology. 9: 79. 2015.

Cell, isoform, and environment factors shape gradients and modulate chemotaxis
Chang SL, Cavnar SP, Takayama S, Luker GD, Linderman JJ. PLoS One. 2015.

Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation
Sumit M, Neubig RR, Takayama S, Linderman JJ. Integrative Biology. 7: 1378-1386. 2015.

In silico models of M. tuberculosis provide a route to new therapies.
Linderman JJ, Kirschner DE. Drug Discov Today Dis Models. 2015.

Identifying Mechanisms of Homeostatic Signaling in Fibroblast Differentiation
Warsinke HC, Ashley SL, Linderman JJ, Moore BB, Kirschner DE. Bull Math Biol. 2015.

Computational Modeling Predicts IL-10 Control of Lesion Sterilization by Balancing Early Host Immunity-Mediated Antimicrobial Responses with Caseation during Mycobacterium tuberculosis Infection
Cilfone NA, Ford CB, Marino S, Mattila JT, Gideon HP, Flynn JL, Kirschner DE, Linderman JJ. Journal of Immunology. 194(2): 664-77. 2015.

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment
Pienaar E, Cilfone NA, Lin PL, Dartois V, Mattila JT, Butler JR, Flynn JL, Kirschner DE, Linderman JJ. Journal of Theoretical Biology. 367: 166-79. 2015.

A comprehensive analysis of CXCL12 isoforms in breast cancer
Zhao S, Chang SL, Linderman JJ, Feng F, Luker GD. Translational Oncology. 2014.

Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection.
Marino S, Cilfone NA, Mattila JT, Linderman JJ, Flynn JL, Kirschner DE. Infection and Immunity. 83(1):324-38. 2014.

Harnessing the heterogeneity of T cell differentiation fate to fine-tune generation of effector and memory T cells
Gong C, Linderman JJ, Kirschner D. Frontiers in Immunology. 5: 57. 2014.

Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.
Cilfone NA, Kirschner DE, Linderman JJ. Cellular and Molecular Bioengineering. 2014.

CXCR7 Controls Competition for Recruitment of B-Arrestin 2 in Cells Expressing Both CXCR4 and CXCR7
Coggins NL, Trakimas D, Chang SL, Ehrlich A, Ray P, Luker KE, Linderman JJ, Luker GD. PLoS ONE. 9(6): e98328. 2014.

Microfluidic source-sink model reveals effects of biophysically distinct CXCL12 isoforms in breast cancer chemotaxis
Cavnar SP, Ray P, Moudgil P, Chang SL, Luker KE, Linderman JJ, Takayama S Luker GD. Integrative Biology. 6: 564-576. 2014.

Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models
Kirschner DE, Hunt CA, Marino S, Fallahi-Sichani M, Linderman JJ. WIREs Systems Biology and Medicine. 6: 289-309. 2014.

In silico models of M. tuberculosis infection provide a route to new therapies
Linderman JJ, Kirschner D. Drug Discovery Today: Disease Models. (Article in press)

A comprehensive analysis of CXCL12 Isoforms in Breast Cancer
Zhao S, Chang SL, Linderman JJ, Feng FY, Luker GD. Translational Oncology. 7(3): 429-438. 2014.

Predicting lymph node output efficiency using systems biology
Gong C, Mattila JT, Miller M, Flynn JL, Linderman JJ, Kirschner D. Journal of Theoretical Biology. 335: 169-184. 2013.

Microfluidic interrogation and mathematical modeling of multi-regime calcium signaling dynamics
Jovic A, Wade SM, Neubig RR, Linderman JJ, Takayama S. Integrative Biology. 5: 932-939. 2013.

Multi-Scale Modeling Predicts a Balance of Tumor Necrosis Factor-alpha and Interleukin-10
Controls the Granuloma Environment during Mycobacterium tuberculosis Infection,

Cilfone NA, Perry CR, Kirschner DE, Linderman JJ. PLoS ONE. 8(7): e68680. 2013.

NF-kB signaling dynamics play a key role in infection control in tuberculosis,
Fallahi-Sichani M, Kirschner DE, Linderman JJ. Frontiers in Physiology. 3(170) 2012.

Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability.
Fallahi-Sichani M, Flynn JL, Linderman JJ, Kirschner DE. Journal of Immunology. 188(7) : 3169-3178, 2012.

Ruffles limit diffusion in the plasma membrane during macropinosome formation.
Welliver TP, Chang SL, Linderman JJ and Swanson JA. Journal of Cell Science. 124 : 4106-4114, 2011.

Systems biology approaches for understanding cellular mechanisms of immunity in lymph nodes during infection.
Mirsky HP, Miller MJ, Linderman JJ, Kirschner DE. Journal of Theoretical Biology. 287 : 160-170, 2011.

Hi-Fi transmission of periodic signals amid cell-to-cell variability.
Jovic A, Wade SM, Miyawaki A, Neubig RR, Linderman JJ, Takayama S. Molecular Biosystems. 7 : 2238-2244, 2011.

Multiscale computational modeling reveals a critical role for TNFR1 dynamics in tuberculosis granuloma formation.
Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DE, Linderman JJ. The Journal of Immunology. 186(6) : 3472-3483, 2011.

Integrin organization: Linking adhesion ligand nanopatterns with altered cell responses.
Comisar WA, Mooney DJ, Linderman JJ. Journal of Theoretical Biology. 274(1) : 120-130, 2011.

Phase-locked signals elucidate circuit architecture of an oscillatory pathway.
Jovic A, Howell B, Cote M, Wade SM, Mehta K, Miyawaki A, Neubig RR, Linderman JJ, Takayama S. PLoS Computational Biology. 6(12) : e1001040, 2010.

A multifaceted approach to modeling the immune response in tuberculosis.
Marino S, Linderman JJ, Kirschner DE. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 3(4) : 479-489, 2010.

Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma.
Fallahi-Sichani M, Schaller MA, Kirschner DE, Kunkel SL, Linderman JJ. PLoS Computational Biology. 6(5) : e1000778, 2010.

Characterizing the dynamics of CD4+ T cell priming within a lymph node.
Linderman JJ, Riggs T, Pande M, Miller M, Marino S, Kirschner DE. The Journal of Immunology. 184(6) : 2873-2885, 2010.