Peer-reviewed Publications

Brown, D., et al. (2015). “Trauma in silico: Individual-specific mathematical models and virtual clinical populations.” Sci Transl Med 7(285): 285ra261.

Mathew, S., et al. (2014). “Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses.” J Theor Biol 358: 132-148.

Namas, R. A., et al. (2013). “Combined in silico, in vivo, and in vitro studies shed insights into the acute inflammatory response in middle-aged mice.” PLoS ONE 8(7): e67419.

Namas, R., et al. (2012). “Sepsis: Something old, something new, and a systems view.” J Crit Care 27(3): 314 e311-311.

Nieman, G., et al. (2012). “A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine.” Crit Care Med 40(4): 1052-1063.

An, G., et al. (2011). “In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation.” Drug Dev Res 72(2): 187-200.

Vodovotz, Y., et al. (2010). “Translational systems approaches to the biology of inflammation and healing.” Immunopharmacol Immunotoxicol 32(2): 181-195.

Torres, A., et al. (2009). “Mathematical modeling of posthemorrhage inflammation in mice: studies using a novel, computer-controlled, closed-loop hemorrhage apparatus.” Shock 32(2): 172-178.

Constantine, G., et al. (2009). “A linear code parameter search algorithm with applications to immunology.” Computational Optimization and Applications 42: 155-171.

Vodovotz, Y., et al. (2008). “Translational systems biology of inflammation.” PLoS Comput Biol 4(4): e1000014.

Kumar, R., et al. (2008). “A mathematical simulation of the inflammatory response to anthrax infection.” Shock 29(1): 104-111.

Vodovotz, Y., et al. (2007). “Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness.” J Crit Care 22(1): 77-84.

Prince, J. M., et al. (2006). “In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice.” Mol Med 12(4-6): 88-96.

Lagoa, C. E., et al. (2006). “The role of initial trauma in the host’s response to injury and hemorrhage: insights from a correlation of mathematical simulations and hepatic transcriptomic analysis.” Shock 26(6): 592-600.

Vodovotz, Y., et al. (2006). “In silico models of acute inflammation in animals.” Shock 26(3): 235-244.

Vodovotz, Y., et al. (2005). “Mathematical Simulations of Sepsis and Trauma.” Shock. Proceedings of the 11th Congress of the European Shock Society 2005

Chow, C. C., et al. (2005). “The acute inflammatory response in diverse shock states.” Shock 24(1): 74-84.

Clermont, G., et al. (2004). “In silico design of clinical trials: a method coming of age.” Crit Care Med 32(10): 2061-2070.

Book Chapter

Vodovotz Y., Bartels J., An G. (2013) In Silico Trials and Personalized Therapy for Sepsis and Trauma. In: Vodovotz Y., An G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, New York, NY

Conference Abstracts/Posters: 

Peng T. et al. (2022). Development of a Quantitative Systems Pharmacology Model for Atopic Dermatitis: from Biological pathways to Virtual Patients and Clinical Endpoints.” ACoP13 (2022) QSP-356.

Cannon et al. (2020). Hemorrhage in silico: modeling inflammation, coagulopathy, and resuscitation in severe traumatic injury. MHSRS 2020.

Vandermann K., Stine A., Chang S. “Predictions of Clinical Outcomes for Checkpoint Inhibitor Combination Therapies in First-Line NSCLC.” AACR Tumor Immunology in 2018. Miami Beach, Florida.

Cannon JW, et al. (2016). Trauma In Silico: A Computational Model of Acute Hemorrhage, Coagulopathy and Resuscitation. Shock. 2016;45:S90. Military Health System Research Symposium (MHSRS) 2016.

Mann-Salinas, E., et al. (2013). “Secondary Validation of Novel Predictors of Sepsis in the Burn Patient.” Critical Care Medicine. Society of Critical Care Medicine 41(12): 251.

Marathe DD, et al. (2010). “Modeling of severe sepsis patients with community acquired pneumonia (CAP). Shock 33 (Suppl 1), 72-73. June 12-15, 2010, Portland, Oregon.

Sarkar, J., et al. (2009). “Mathematical modeling of community-acquired pneumonia patients.” Critical Care 13(4): P49.

Sarkar, J., et al. (2008). “Computational simulation of individual inflammation and outcomes in trauma patients.” Journal of Critical Care – J CRIT CARE 23: 263-263.

Vodovotz Y, et al. (2006). “In silico and in vivo studies modeling the aged acute inflammatory response.” Shock 25. Suppl. 1:39-40 2006.

Daun, S. et al. (2006). “Optimizing a Therapeutic Intervention: Systems Engineering of a Pheresis Intervention for Sepsis.” Crit. Care, Vol. 21, Issue 4, pp. 360-1 (December 2006).

Clermont, G., et al. (2005). “Toward a model-driven design of clinical trials and individualized therapies.” Journal of Critical Care – J CRIT CARE 20: 386-386.

Lagoa, C., et al. (2005). “Mathematical models predict the course of the inflammatory response in rats subjected to trauma-hemorrhagic shock and to anti–tumor necrosis factor α therapy in endotoxemia.” Journal of Critical Care – J CRIT CARE 20: 393-394.

Nieman G, et al. (2005). “Mathematical simulation of inflammation in porcine septic shock and ARDS.” Shock 23 Supplement 3:3.

Clermont G. et al. (2004). “Does the use of a cytokine ‘filter’ during cardiopulmonary bypass make sense?” J. Crit. Care, vol. 8, no. Suppl 1, pp. P149-2.

Clermont G, et al. (2004). “Predicting the response to therapy from a mathematical model.” J. Crit. Care, vol. 8, no. Suppl 1, pp. P206-2.

Invited talks

“What can clinical development learn from QCP?” ACOP13 2022, Aurora, CO.

“Virtual Patients, Real Results: Constructing Populations for QSP.” FDA QSP Seminar Series, August 2020. Virtual.

Prediction of the Dose-Response for a Novel Tyk2 Inhibitor Using a QSP Model of Psoriasis. Scientific Exchange on Quantitative Systems Pharmacology by the FDA. July 1, 2020. Virtual. (presented by client)

“Modelers are from Mars, Biologists are Bothans.” ACOP11 2020, Virtual conference.

“QSP for checkpoint inhibitor combinations.” ACOP10 2019. October 2019. Orlando, Florida.

“Component Model Libraries:  Immunetrics’s evolving approach to QSP model development”. ACOP8 2017. October 2017. Fort Lauderdale, Florida.

“Slaying the Hydras of QSP Model Development.” ACOP6 2015, October 2015. Arlington, Virginia.

“In Silico Clinical Trials.” McGowan Institute’s Computational Modeling Workshop. December 15, 2014. Pittsburgh, PA.

“Fixed points in Detailed Physiological Models.”  SIAM Life Sciences 2010, July 2010, Pittsburgh, PA.

“Identifying Candidate Model Clouds In Large Parameter Spaces.” Bartels J, Constantine G. International Conference on Complexity in Acute Illness, Oct 2005, Cologne, Germany.

“Optimization Issues In Modeling.” Bartels J. International Conference on Complexity in Acute Illness, Nov 2004, Pittsburgh, PA.

“An optimal code algorithm for Nonlinear Optimization.” Dept. of Mathematics colloquium, University of Pittsburgh, April, 2004.

“Calibrating models to data: Automated data fitting strategies.” Complex Systems In Critical Illness Workshop, Nov, 2003, University Of Pittsburgh.