Home
Resource
A multiscale model for the assessment of autonomic dysfunction in human endotoxemia
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(2.20 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Foteinou, Panagiota. A multiscale model for the assessment of autonomic dysfunction in human endotoxemia. Retrieved from https://doi.org/doi:10.7282/T34749ZJ

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleA multiscale model for the assessment of autonomic dysfunction in human endotoxemia
NameFoteinou, Panagiota (author); Androulakis, Ioannis P (chair); Yarmush, Martin l (internal member); Moghe, Prabhas V (internal member); Calvano, Steven E (outside member); Lowry, Stephen F (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2010
Other Date2010 (degree)
SubjectBiomedical Engineering, Endotoxemia, Inflammation--Mathematical models
Extentxxiii, 235 p. : ill.
DescriptionThe overall goal of this dissertation is the development of a semi-mechanistic, physiology-based, multiscale model of human endotoxemia, as a prototype model of acute inflammation in humans that integrates essential regulatory processes across the host from the cellular to the systemic level. The complex and multiplex characteristics of the acute inflammatory response and its complications have been thought to be a leading potential reason for the inability to propose effective clinical intervention strategies. The nature of the response has led researchers to the realization that mathematical models of inflammation offer the opportunity to study the dynamics of interacting components and establish a causal inference relationship through the manipulation of the corresponding dynamic elements. Driven by the premise to develop in silico methodologies that will enable translational research to elucidate mechanisms by which macroscopic responses, at the physiome level, emerge as the result of propagating information across an intricate web of interacting modules, a systems-level modeling approach is developed that addresses the following unique aspects: (i) identification of the essential components characterizing cellular transcriptional dynamics in response to an external perturbation; (ii) reverse engineering of quantifiable representations of these elements exploring the concept of physicochemical and indirect response modeling that connect extracellular signals and intracellular signaling cascades leading to the emergent transcriptional dynamics; and finally (iii) multiscale, physiology-based modeling that quantifies critical aspects of the complex neuro-immune crosstalk while clinical observables are explicitly incorporated to assess systemic abnormalities indicative of the severity of the host. It is therefore the purpose of this research to demonstrate the feasibility of a relevant human inflammation model that bridges the initiating signal and phenotypic expressions through semi-mechanistic based host response models that include transcriptional dynamics, signaling cascades and physiological components; and to define a notional template for multiscale modeling extendable to a wide range of clinically important conditions. The ultimate deliverable of the proposed research is the multiscale human inflammation model that would allow us to clarify the clinical contexts in which inflammatory dysregulation contributes to morbidity and mortality in acutely stressed patients; thereby advancing the translational potential of systems modeling in clinical research.
NotePh.D.
NoteIncludes abstract
NoteVita
NoteIncludes bibliographical references
Noteby Panagiota Theodoros Foteinou
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T34749ZJ
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024