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Items: 1 to 50 of 106

1.

Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4.

Spinosa PC, Humphries BA, Lewin Mejia D, Buschhaus JM, Linderman JJ, Luker GD, Luker KE.

Sci Signal. 2019 Jul 9;12(589). pii: eaaw4204. doi: 10.1126/scisignal.aaw4204.

PMID:
31289212
2.

The Role of Dimensionality in Understanding Granuloma Formation.

Marino S, Hult C, Wolberg P, Linderman JJ, Kirschner DE.

Computation (Basel). 2018 Dec;6(4). pii: 58. doi: 10.3390/computation6040058. Epub 2018 Nov 14.

3.

A Two-Pulse Cellular Stimulation Test Elucidates Variability and Mechanisms in Signaling Pathways.

Sumit M, Jovic A, Neubig RR, Takayama S, Linderman JJ.

Biophys J. 2019 Mar 5;116(5):962-973. doi: 10.1016/j.bpj.2019.01.022. Epub 2019 Jan 30.

PMID:
30782397
4.

Integrating Non-human Primate, Human, and Mathematical Studies to Determine the Influence of BCG Timing on H56 Vaccine Outcomes.

Joslyn LR, Pienaar E, DiFazio RM, Suliman S, Kagina BM, Flynn JL, Scriba TJ, Linderman JJ, Kirschner DE.

Front Microbiol. 2018 Aug 17;9:1734. doi: 10.3389/fmicb.2018.01734. eCollection 2018.

5.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.

Cicchese JM, Evans S, Hult C, Joslyn LR, Wessler T, Millar JA, Marino S, Cilfone NA, Mattila JT, Linderman JJ, Kirschner DE.

Immunol Rev. 2018 Sep;285(1):147-167. doi: 10.1111/imr.12671. Review.

6.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas.

Pienaar E, Linderman JJ, Kirschner DE.

PLoS One. 2018 May 10;13(5):e0196322. doi: 10.1371/journal.pone.0196322. eCollection 2018.

7.

Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model.

Warsinske HC, Pienaar E, Linderman JJ, Mattila JT, Kirschner DE.

Front Immunol. 2017 Dec 20;8:1843. doi: 10.3389/fimmu.2017.01843. eCollection 2017.

8.

Applying optimization algorithms to tuberculosis antibiotic treatment regimens.

Cicchese JM, Pienaar E, Kirschner DE, Linderman JJ.

Cell Mol Bioeng. 2017 Dec;10(6):523-535. doi: 10.1007/s12195-017-0507-6. Epub 2017 Aug 30.

9.

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 Nov 8;12(11):e0187357. doi: 10.1371/journal.pone.0187357. eCollection 2017.

10.

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 DE, Linderman JJ.

PLoS Comput Biol. 2017 Aug 17;13(8):e1005650. doi: 10.1371/journal.pcbi.1005650. eCollection 2017 Aug.

11.

Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection.

Warsinske HC, DiFazio RM, Linderman JJ, Flynn JL, Kirschner DE.

J Theor Biol. 2017 Sep 21;429:1-17. doi: 10.1016/j.jtbi.2017.06.017. Epub 2017 Jun 20.

12.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.

Kirschner D, Pienaar E, Marino S, Linderman JJ.

Curr Opin Syst Biol. 2017 Jun;3:170-185. doi: 10.1016/j.coisb.2017.05.014. Epub 2017 May 22.

13.

Correction: Cell, Isoform, and Environment Factors Shape Gradients and Modulate Chemotaxis.

Chang SL, Cavnar SP, Luker KE, Takayama S, Luker GD, Linderman JJ.

PLoS One. 2017 Mar 14;12(3):e0174189. doi: 10.1371/journal.pone.0174189. eCollection 2017.

14.

New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling.

Sumit M, Takayama S, Linderman JJ.

Integr Biol (Camb). 2017 Jan 23;9(1):6-21. doi: 10.1039/c6ib00178e. Review.

15.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis.

Warsinske HC, Wheaton AK, Kim KK, Linderman JJ, Moore BB, Kirschner DE.

Front Pharmacol. 2016 Jun 23;7:183. doi: 10.3389/fphar.2016.00183. eCollection 2016.

16.

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.

Marino S, Gideon HP, Gong C, Mankad S, McCrone JT, Lin PL, Linderman JJ, Flynn JL, Kirschner DE.

PLoS Comput Biol. 2016 Apr 11;12(4):e1004804. doi: 10.1371/journal.pcbi.1004804. eCollection 2016 Apr.

17.

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions.

Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE.

Infect Immun. 2016 Apr 22;84(5):1650-1669. doi: 10.1128/IAI.01438-15. Print 2016 May.

18.

In silico models of M. tuberculosis infection provide a route to new therapies.

Linderman JJ, Kirschner DE.

Drug Discov Today Dis Models. 2015 Spring;15:37-41. Epub 2014 May 9.

19.

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.

Front Microbiol. 2016 Jan 6;6:1477. doi: 10.3389/fmicb.2015.01477. eCollection 2015.

20.

In silico evaluation and exploration of antibiotic tuberculosis treatment regimens.

Pienaar E, Dartois V, Linderman JJ, Kirschner DE.

BMC Syst Biol. 2015 Nov 14;9:79. doi: 10.1186/s12918-015-0221-8.

21.

Identifying Mechanisms of Homeostatic Signaling in Fibroblast Differentiation.

Warsinske HC, Ashley SL, Linderman JJ, Moore BB, Kirschner DE.

Bull Math Biol. 2015 Aug;77(8):1556-82. doi: 10.1007/s11538-015-0096-2. Epub 2015 Sep 18.

22.

Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation.

Sumit M, Neubig RR, Takayama S, Linderman JJ.

Integr Biol (Camb). 2015 Nov;7(11):1378-86. doi: 10.1039/c5ib00181a.

23.
24.

Systems Pharmacology Approach Toward the Design of Inhaled Formulations of Rifampicin and Isoniazid for Treatment of Tuberculosis.

Cilfone NA, Pienaar E, Thurber GM, Kirschner DE, Linderman JJ.

CPT Pharmacometrics Syst Pharmacol. 2015 Mar;4(3):e00022. doi: 10.1002/psp4.22. Epub 2015 Mar 11.

25.

A multi-scale approach to designing therapeutics for tuberculosis.

Linderman JJ, Cilfone NA, Pienaar E, Gong C, Kirschner DE.

Integr Biol (Camb). 2015 May;7(5):591-609. doi: 10.1039/c4ib00295d. Epub 2015 Apr 30.

26.

Cell, isoform, and environment factors shape gradients and modulate chemotaxis.

Chang SL, Cavnar SP, Takayama S, Luker GD, Linderman JJ.

PLoS One. 2015 Apr 24;10(4):e0123450. doi: 10.1371/journal.pone.0123450. eCollection 2015. Erratum in: PLoS One. 2017 Mar 14;12 (3):e0174189.

27.

A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes.

Gong C, Linderman JJ, Kirschner D.

Math Biosci Eng. 2015 Jun;12(3):625-42. doi: 10.3934/mbe.2015.12.625.

28.

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.

J Immunol. 2015 Jan 15;194(2):664-77. doi: 10.4049/jimmunol.1400734. Epub 2014 Dec 15.

29.

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.

J Theor Biol. 2015 Feb 21;367:166-179. doi: 10.1016/j.jtbi.2014.11.021. Epub 2014 Dec 9.

30.

Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection.

Marino S, Cilfone NA, Mattila JT, Linderman JJ, Flynn JL, Kirschner DE.

Infect Immun. 2015 Jan;83(1):324-38. doi: 10.1128/IAI.02494-14. Epub 2014 Nov 3.

31.

CXCR7 controls competition for recruitment of β-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. 2014 Jun 4;9(6):e98328. doi: 10.1371/journal.pone.0098328. eCollection 2014.

32.

A Comprehensive Analysis of CXCL12 Isoforms in Breast Cancer1,2.

Zhao S, Chang SL, Linderman JJ, Feng FY, Luker GD.

Transl Oncol. 2014 May 13. pii: S1936-5233(14)00021-7. doi: 10.1016/j.tranon.2014.04.001. [Epub ahead of print]

33.

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.

Wiley Interdiscip Rev Syst Biol Med. 2014 Jul-Aug;6(4):289-309. doi: 10.1002/wsbm.1270. Epub 2014 May 9. Review.

34.

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.

Integr Biol (Camb). 2014 May;6(5):564-76. doi: 10.1039/c4ib00015c.

35.

Harnessing the heterogeneity of T cell differentiation fate to fine-tune generation of effector and memory T cells.

Gong C, Linderman JJ, Kirschner D.

Front Immunol. 2014 Feb 19;5:57. doi: 10.3389/fimmu.2014.00057. eCollection 2014.

36.

Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection.

Cilfone NA, Perry CR, Kirschner DE, Linderman JJ.

PLoS One. 2013 Jul 15;8(7):e68680. doi: 10.1371/journal.pone.0068680. Print 2013.

37.

Predicting lymph node output efficiency using systems biology.

Gong C, Mattila JT, Miller M, Flynn JL, Linderman JJ, Kirschner D.

J Theor Biol. 2013 Oct 21;335:169-84. doi: 10.1016/j.jtbi.2013.06.016. Epub 2013 Jun 29.

38.

Microfluidic interrogation and mathematical modeling of multi-regime calcium signaling dynamics.

Jovic A, Wade SM, Neubig RR, Linderman JJ, Takayama S.

Integr Biol (Camb). 2013 Jul 24;5(7):932-9. doi: 10.1039/c3ib40032h. Epub 2013 Jun 4.

39.

NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis.

Fallahi-Sichani M, Kirschner DE, Linderman JJ.

Front Physiol. 2012 Jun 6;3:170. doi: 10.3389/fphys.2012.00170. eCollection 2012.

40.

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.

J Immunol. 2012 Apr 1;188(7):3169-78. doi: 10.4049/jimmunol.1103298. Epub 2012 Feb 29.

41.

Ruffles limit diffusion in the plasma membrane during macropinosome formation.

Welliver TP, Chang SL, Linderman JJ, Swanson JA.

J Cell Sci. 2011 Dec 1;124(Pt 23):4106-14. doi: 10.1242/jcs.091538.

42.

Systems biology approaches for understanding cellular mechanisms of immunity in lymph nodes during infection.

Mirsky HP, Miller MJ, Linderman JJ, Kirschner DE.

J Theor Biol. 2011 Oct 21;287:160-70. doi: 10.1016/j.jtbi.2011.06.037. Epub 2011 Jul 23. Review.

43.

Hi-Fi transmission of periodic signals amid cell-to-cell variability.

Jovic A, Wade SM, Miyawaki A, Neubig RR, Linderman JJ, Takayama S.

Mol Biosyst. 2011 Jul;7(7):2238-44. doi: 10.1039/c1mb05031a. Epub 2011 May 11.

44.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation.

Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DE, Linderman JJ.

J Immunol. 2011 Mar 15;186(6):3472-83. doi: 10.4049/jimmunol.1003299. Epub 2011 Feb 14.

45.

Integrin organization: linking adhesion ligand nanopatterns with altered cell responses.

Comisar WA, Mooney DJ, Linderman JJ.

J Theor Biol. 2011 Apr 7;274(1):120-30. doi: 10.1016/j.jtbi.2011.01.007. Epub 2011 Jan 19.

46.

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 Comput Biol. 2010 Dec 23;6(12):e1001040. doi: 10.1371/journal.pcbi.1001040.

47.

A multifaceted approach to modeling the immune response in tuberculosis.

Marino S, Linderman JJ, Kirschner DE.

Wiley Interdiscip Rev Syst Biol Med. 2011 Jul-Aug;3(4):479-89. doi: 10.1002/wsbm.131. Epub 2010 Dec 31. Review.

48.

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 Comput Biol. 2010 May 6;6(5):e1000778. doi: 10.1371/journal.pcbi.1000778.

49.

Characterizing the dynamics of CD4+ T cell priming within a lymph node.

Linderman JJ, Riggs T, Pande M, Miller M, Marino S, Kirschner DE.

J Immunol. 2010 Mar 15;184(6):2873-85. doi: 10.4049/jimmunol.0903117. Epub 2010 Feb 12.

50.

A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging.

Mehta K, Hoppe AD, Kainkaryam R, Woolf PJ, Linderman JJ.

Proteomics. 2009 Dec;9(23):5371-83. doi: 10.1002/pmic.200800494.

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