Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.

Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic und...

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Autores principales:	Zhenzhen Shi, Yang Li, Majid Jaberi-Douraki
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/04fcaf35a1c242a8ba03b34f748257c7
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spelling	oai:doaj.org-article:04fcaf35a1c242a8ba03b34f748257c72021-12-02T19:58:13ZHybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.1553-734X1553-735810.1371/journal.pcbi.1009413https://doaj.org/article/04fcaf35a1c242a8ba03b34f748257c72021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009413https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic β-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.Zhenzhen ShiYang LiMajid Jaberi-DourakiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009413 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Zhenzhen Shi Yang Li Majid Jaberi-Douraki Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
description	Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic β-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.
format	article
author	Zhenzhen Shi Yang Li Majid Jaberi-Douraki
author_facet	Zhenzhen Shi Yang Li Majid Jaberi-Douraki
author_sort	Zhenzhen Shi
title	Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
title_short	Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
title_full	Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
title_fullStr	Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
title_full_unstemmed	Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
title_sort	hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/04fcaf35a1c242a8ba03b34f748257c7
work_keys_str_mv	AT zhenzhenshi hybridcomputationalmodelingdemonstratestheutilityofsimulatingcomplexcellularnetworksintype1diabetes AT yangli hybridcomputationalmodelingdemonstratestheutilityofsimulatingcomplexcellularnetworksintype1diabetes AT majidjaberidouraki hybridcomputationalmodelingdemonstratestheutilityofsimulatingcomplexcellularnetworksintype1diabetes
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Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.

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