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Mathematical modeling of energy consumption in the acute inflammatory response during sepsis

Ramirez Zuniga, Ivan (2020) Mathematical modeling of energy consumption in the acute inflammatory response during sepsis. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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When a pathogen invades the body, an acute inflammatory response is activated to eliminate the intruder. In some patients, runaway activation of the immune system may lead to collateral tissue damage and, in the extreme, organ failure and death.
Experimental studies have found an association between severe infections and depletion in levels of adenosine triphosphate (ATP), increase in nitric oxide production, and accumulation of lactate, suggesting that tissue energetics is compromised.

We present a computational model consisting of ordinary differential equations to explore the dynamics of the acute inflammatory response against infections caused when a pathogen makes its way into a host. This model incorporates energy production along with the energy requirements that arise when fighting such an infection. In particular, we investigate the role of energetics during infection and explore the relation between overproduction of nitric oxide (NO), lactate, altered adenosine triphosphate (ATP) levels, and sepsis.

Finally, a data-driven approach is used to extend our model as an effort to better understand the role of energy in sepsis. This extended model is calibrated by fitting animal data from a study done in thirty-two baboons that were induced into sepsis after infusing E. coli intravenously. Using Bayesian analysis, we quantify uncertainty in model parameters and with them we investigate differences across different populations, including survivors and non-survivors.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Ramirez Zuniga, Ivanivanrazu@gmail.com0000-0002-1052-8306
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairRubin,
Committee ChairSwigon,
Committee MemberClermont,
Committee MemberErmentrout,
Date: 16 September 2020
Date Type: Publication
Defense Date: 9 July 2020
Approval Date: 16 September 2020
Submission Date: 21 August 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 176
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Immunology, sepsis, bioenergetics, bifurcation analysis, basins of attraction, parameter estimation, data-driven model, virtual patients, logistic regression.
Date Deposited: 16 Sep 2020 14:55
Last Modified: 16 Sep 2021 05:15


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