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: 

  • 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-

Invited talks

  • 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.
  • 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