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Multi-Scale Mathematical Models of Airway Epithelium to Facilitate Cystic Fibrosis Treatment

Serrano Castillo, Florencio (2019) Multi-Scale Mathematical Models of Airway Epithelium to Facilitate Cystic Fibrosis Treatment. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Cystic Fibrosis is a life-shortening, autosomal recessive disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. Cystic Fibrosis is the most common lethal genetic disorder among Caucasians and occurs at a rate of 1 in every 3,400 births in the United States. The CFTR gene codes an anion channel expressed on the mucosal side of the epithelium of multiple organ systems, including the GI tract, reproductive organs, and airways. Loss of CFTR expression or function results in an osmotic imbalance due to defective ion and water transport. In the respiratory tract, this leads to airway surface liquid hyperabsorption and mucus dehydration causing decreased mucociliary clearance rates. Failure to clear mucus and other inhaled debris favor the development of mucus plugs and the prolonged colonization of harmful pathogens. These conditions lead to a sustained, unregulated inflammatory response that results in severe tissue damage and, ultimately, respiratory failure, the leading cause of Cystic Fibrosis mortality.

Cystic Fibrosis therapeutic development is largely dependent on the availability of model systems that can be used to test and optimize therapies ahead of clinical use. These systems include networks of interactive elements that can be overly complex to exhaustively explore experimentally. The work presented here focuses on the development of cell-scale, mechanistic, and biologically relevant mathematical models that provide information about the contribution of individual mechanisms to experimental outcomes.

The proposed models were trained and validated with data obtained from human bronchial and nasal epithelial cell cultures from donors with Cystic Fibrosis and non-Cystic Fibrosis controls, as well as from carriers of a single disease-causing allele in the case of nasal cells. Within this context, we have used these models as tools to explore the underlying mechanisms behind Cystic Fibrosis pathophysiology not easily accessible experimentally. These predictions will be further enhanced through the inclusion of similar estimates generated from separately developed models of in vivo lung-scale function, as well as standard clinical measurements of airway function. Model predictions provide us with unique and novel parametric descriptions of mechanistic and physiological differences between the three populations and across multiple spatiotemporal scales. We expect to implement these models as means to facilitate the deployment of personalized treatment protocols, where cells sampled and cultured from individuals could be used to generate patient-specific in silico predictors of lung-scale disease state, and therapeutic response.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Serrano Castillo, Florenciofls13@pitt.edufls13
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairParker, Robert S.rparker@pitt.edurparker
Committee CoChairCorcoran, Timothy E.corcorante@upmc.edutec23
Committee MemberBertrand, Carol A.cbertra@pitt.educbertra
Committee MemberMyerburg, Michael M.myerburgm@upmc.edummmst40
Committee MemberBanerjee, Ipsitaipb1@pitt.eduipb1
Date: 10 September 2019
Date Type: Publication
Defense Date: 9 July 2019
Approval Date: 10 September 2019
Submission Date: 22 June 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 215
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Cystic Fibrosis, System medicine, Mathematical model, Epithelial electrophysiology, Airway surface liquid layer, Mucociliary clearance
Date Deposited: 10 Sep 2019 17:57
Last Modified: 10 Sep 2019 17:57


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