Abstract
| Original language | English |
|---|---|
| Title of host publication | Computational Science – ICCS 2023 - 23rd International Conference, Proceedings |
| Editors | Jiří Mikyška, Clélia de Mulatier, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Maciej Paszynski, Jack J. Dongarra |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 330-338 |
| Number of pages | 9 |
| Volume | 14074 LNCS |
| ISBN (Print) | 9783031360206 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 23rd International Conference on Computational Science, ICCS 2023 - Prague, Czech Republic Duration: 3 Jul 2023 → 5 Jul 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14074 LNCS |
Conference
| Conference | 23rd International Conference on Computational Science, ICCS 2023 |
|---|---|
| Country/Territory | Czech Republic |
| City | Prague |
| Period | 3/07/2023 → 5/07/2023 |
Keywords
- Allostatic Load
- Chronic stress
- Computational modelling
- Diabetes
- Disease progress
- in-silico tool
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Computational Science – ICCS 2023 - 23rd International Conference, Proceedings. ed. / Jiří Mikyška; Clélia de Mulatier; Valeria V. Krzhizhanovskaya; Peter M.A. Sloot; Maciej Paszynski; Jack J. Dongarra. Vol. 14074 LNCS Springer Science and Business Media Deutschland GmbH, 2023. p. 330-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14074 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Modelling the Interplay Between Chronic Stress and Type 2 Diabetes On-Set
AU - Bumbuc, Roland V.
AU - Yildirim, Vehpi
AU - Sheraton, M. Vivek
N1 - Funding Information: Acknowledgments. This research is financially supported by the Russian Science Foundation, Agreement 17-71-30029 (https://rscf.ru/en/project/17-71-30029/), with co-financing of Bank Saint Petersburg. Funding Information: Acknowledgements. The work of Klaudia Bałazy and Łukasz Struski was supported by the National Centre of Science (Poland) Grant No. 2020/39/D/ST6/01332. The research of Jacek Tabor was carried out within the research project “Bio-inspired artificial neural network” (grant no. POIR.04.04.00-00-14DE/18-00) within the Team-Net program of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund. The work of Marek Śmieja was supported by the National Centre of Science (Poland) Grant No. 2022/45/B/ST6/01117. Klau-dia Balazy is affiliated with Doctoral School of Exact and Natural Sciences at the Jagiellonian University. Funding Information: Acknowledgement. One of the authors Dr. Sanjay K. Sahay is thankful to Data Security Council of India for financial support to work on the Android malware detection system. Funding Information: The publication has been supported by a grant from the Faculty of Management and Social Communication under the Strategic Programme Excellence Initiative at Jagiellonian University. Funding Information: Acknowledgements. This work was supported by NVIDIA (project “Patient-specific models of the heart for precision medicine”, NVIDIA Academic Hardware Grant Program), the Federal University of Juiz de Fora, Brazil, through the scholarship “Coor-denação de Aperfeiçoamento de Pessoal de Nível Superior” (CAPES) -Brazil-Finance Code 001; by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-Brazil Grant numbers 423278/2021-5, 308745/2021-3 and 310722/2021-7; by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) TEC APQ 01340/18. Funding Information: Acknowledgements. Research project supported by the program “Excellence initiative - research university” for the AGH University of Science and Technology. Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. 2042155. Funding Information: This research was partially supported by funds from the Polish Ministry of Education and Science assigned to AGH University of Science and Technology (AB, WT, MKD) and Cracow University of Science and Technology (LB). This research was supported by PLGrid Infrastructure. Funding Information: Acknowledgements. This work has been supported by UFJF, by CAPES - Finance Code 001; by CNPq - Grant number 308745/2021-3; and by FAPEMIG Grant number APQ 02830/17 and APQ-02513-22; by FINEP SOS Equipamentos 2021 AV02 0062/22. Funding Information: Patrick Vega has received funding from: the Chilean National Research and Development Agency (ANID) though the grant ANID FONDECYT No. 3220858. Funding Information: Acknowledgements. The research was conducted under the Implementation Doctorate programme of Polish Ministry of Science and Higher Education and also partially funded by Department of Artificial Intelligence, Wroclaw Tech and by the European Union under the Horizon Europe grant OMINO (grant number 101086321). It was also partially co-funded by the European Regional Development Fund within Measure 1.1. “Enterprise R&D Projects”, Sub-measure 1.1.1. “Industrial research and development by companies” as part of The Operational Programme Smart Growth 2014-2020, support contract no. POIR.01.01.01-00-0876/20-00. Funding Information: Acknowledgments. David Pardo has received funding from: the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 777778 (MATHROCKS); the Spanish Ministry of Science and Innovation projects with references TED2021-132783B-I00, PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00 (MCIN/AEI/10.13039/501100011033/Next Generation EU), the “BCAM Severo Ochoa” accreditation of excellence CEX2021-001142-S/MICIN/AEI/10.13039/501100011033; and the Basque Government through the BERC 2022–2025 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1456-22) given by the Department of Education. Funding Information: Acknowledgements. This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN-Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19 and by the project co-financed by the Minister of Education and Science under the agreement 2022/WK/09. Funding Information: Acknowledgements. Work of Marek Bolanowski and Andrzej Paszkiewicz is financed by the Minister of Education and Science of the Republic of Poland within the “Regional Initiative of Excellence” program for years 2019-2023. Project number 027/RID/2018/19, amount granted 11 999 900 PLN. Work of Maria Ganzha and Marcin Paprzycki was funded in part by the European Commission, under the Horizon Europe project ASSIST-IoT, grant number 957258. Funding Information: Acknowledgments. The research presented in this paper was supported by funds allocated to the AGH University of Krakow by the Polish Ministry of Science and Higher Education. The authors utilized the PL-Grid Infrastructure and computing resources provided by ACK Cyfronet. Funding Information: Acknowledgements. The project financed under the program of the Polish Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019 - 2023 project number 020/RID/2018/19 the amount of financing PLN 12,000,000. Funding Information: This publication is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement Sano No 857533. This publication is supported by the Sano project carried out within the International Research Agendas program of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund. Funding Information: This work is supported by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT.IP. Funding Information: Acknowledgments. The authors acknowledge the support of NSF grant CMMI-1953323, a PITA (Pennsylvania Infrastructure Technology Alliance) grant, and a PMFI (Pennsylvania Manufacturing Fellows Initiative) grant. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges-2 system, which is supported by NSF award number ACI-1928147, at the Pittsburgh Supercomputing Center (PSC). The authors would like to thank Faqia Shahid and Sara Begane for running the simulation in the neuron geometry of NMO_134036 and NMO_06846, respectively. Funding Information: Acknowledgements. This work was supported by the Carl Zeiss Foundation within the project Interactive Inference and from the Ministry for Economics, Sciences and Digital Society of Thuringia (TMWWDG), under the framework of the Landespro-gramm ProDigital (DigLeben-5575/10-9). Funding Information: Acknowledgements. The project is financed under the 2014–2020 Smart Development Operational Programme, Priority IV: Increasing the scientific and research potential, Measure 4.2: Development of modern research infrastructure of the science sector, No. POIR.04.02.00-00C002/19, "CLARIN - Common Language Resources and Technology Infrastructure" and by the project co-financed by the Minister of Education and Science under the agreement 2022/WK/09. Funding Information: Acknowledgements. ATARCA received funding from the EU Horizon 2020 agreement No 964678. The authors thank Prof Pekka Nikander for constitutional entrepreneurship, and Prof Juuso Töyli, Prof Len Malczynski, Dr. Sampsa Ruutu, Prof Heikki Hämmäinen, Prof Raimo Kantola, and Prof Petri Mähönen. Funding Information: Acknowledgements. We acknowledge Michael Brown and Los Alamos National Laboratory for the possibility to use the Quick Urban & Industrial Complex Dispersion Modeling System. This work was partially supported by Ministry of Education and Science, project number: DNK/SP/549572/2022. Funding Information: Acknowledgments. This work was supported by the Department of Computer Graphics, Vision, and Digital Systems, under the statutory research project (Rau6, 2023), Silesian University of Technology (Gliwice, Poland). Funding Information: This work has been supported by the project PRA 2020 61 of the University of Pisa and by the Spoke 1 “FutureHPC & BigData” of the Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (ICSC) funded by MUR Missione 4 Componente 2 Investimento 1.4: Potenziamento strutture di ricerca e creazione di “campioni nazionali di R&S (M4C2-19 )” - Next Generation EU (NGEU). Funding Information: (ABCI) provided by National Institute of Advanced Industrial Science and Technology (AIST) was used. This work was supported by MEXT as “Program for Promoting Researches on the Supercomputer Fugaku” (Large-scale numerical simulation of earthquake generation, wave propagation and soil amplification, JPMXP1020200203). This work was supported by JSPS KAKENHI Grant Numbers 18H05239, 22K12057, 22K18823. This work was supported by MEXT, under its Earthquake and Volcano Hazards Observation and Research Program. This work was supported by JST SPRING, Grant Number JPMJSP2108. Funding Information: Acknowledgements. This work was supported in part by the Joint Usage and Research Center for Interdisciplinary Large-Scale Information Infrastructure and Innovative High Performance Computing Infrastructure (project numbers: jh210047-NAH, jh220017, jh230042, hp220040, and hp230046), as well as the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP22K12049. The numerical Funding Information: Acknowledgments. The authors would like to express their thanks to CAPES (Finance Code 001 and Projeto CAPES - Processo 88881.708850/2022-01), CNPq (308745/2021–3), FAPEMIG (APQ-02830/17 and APQ-01226-21), FINEP (SOS Equipamentos 2021 AV02 0062/22) and UFJF for funding this work. Funding Information: Acknowledgments. This research has been supported by the Agencia Estatal de Investigacion (AEI), Spain and the Fondo Europeo de Desarrollo Regional (FEDER) UE, under contract PID2020-112496GB-I00 and partially funded by the Fundacion Escuelas Universitarias Gimbernat (EUG). Funding Information: Acknowledgement. The study is supported by the project “Big Data in Atmospheric Physics (BINARY)”, funded by the Carl Zeiss Foundation (grant P2018-02-003). We acknowledge the ECMWF for providing access to the ERA5 reanalysis data and the ZDV of JGU for providing access to Mogon II. We further acknowledge Daniel Kunkel for supporting us with data management and thank Michael Wand for fruitful discussions. Funding Information: Acknowledgements. This paper is funded from the XPM (Explainable Predictive Maintenance) project funded by the National Science Center, Poland under CHIST-ERA programme Grant Agreement No. 857925 (NCN UMO-2020/02/Y/ST6/00070). Funding Information: Supported by Lodz University of Technology, Institute of Electronics. Funding Information: Acknowledgments. This research is supported by Russian Scientific Foundation and Saint Petersburg Scientific Foundation, grant No. 23-28-10069 “Forecasting social well-being in order to optimize the functioning of the urban digital services ecosystem in St. Petersburg” (https://rscf.ru/project/23-28-10069/). Funding Information: Acknowledgement. This work has been granted by the Spanish Ministry of Science and Innovation MCIN AEI/10.13039/501100011033 under contract PID2020-113614RB-C21 and by the Catalan government under grant 2021 SGR-00574. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Stress has become part of the day-to-day life in the modern world. A major pathological repercussion of chronic stress (CS) is Type 2 Diabetes (T2D). Modelling T2D as a complex biological system involves combining under-the-skin and outside-the-skin parameters to properly define the dynamics involved. In this study, a compartmental model is built based on the various inter-players that constitute the hallmarks involved in the progression of this disease. Various compartments that constitute this model are tested in a glucose-disease progression setting with the help of an adjacent minimal model. Temporal dynamics of the glucose-disease progression was simulated to explore the contribution of different model parameters to T2D onset. The model simulations reveal CS as a critical modulator of T2D disease progression.
AB - Stress has become part of the day-to-day life in the modern world. A major pathological repercussion of chronic stress (CS) is Type 2 Diabetes (T2D). Modelling T2D as a complex biological system involves combining under-the-skin and outside-the-skin parameters to properly define the dynamics involved. In this study, a compartmental model is built based on the various inter-players that constitute the hallmarks involved in the progression of this disease. Various compartments that constitute this model are tested in a glucose-disease progression setting with the help of an adjacent minimal model. Temporal dynamics of the glucose-disease progression was simulated to explore the contribution of different model parameters to T2D onset. The model simulations reveal CS as a critical modulator of T2D disease progression.
KW - Allostatic Load
KW - Chronic stress
KW - Computational modelling
KW - Diabetes
KW - Disease progress
KW - in-silico tool
UR - http://www.scopus.com/inward/record.url?scp=85169674730&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-36021-3_34
DO - https://doi.org/10.1007/978-3-031-36021-3_34
M3 - Conference contribution
SN - 9783031360206
VL - 14074 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 330
EP - 338
BT - Computational Science – ICCS 2023 - 23rd International Conference, Proceedings
A2 - Mikyška, Jiří
A2 - de Mulatier, Clélia
A2 - Krzhizhanovskaya, Valeria V.
A2 - Sloot, Peter M.A.
A2 - Paszynski, Maciej
A2 - Dongarra, Jack J.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Computational Science, ICCS 2023
Y2 - 3 July 2023 through 5 July 2023
ER -