000125854 001__ 125854
000125854 005__ 20241125101154.0
000125854 0247_ $$2doi$$a10.3390/math11081824
000125854 0248_ $$2sideral$$a133329
000125854 037__ $$aART-2023-133329
000125854 041__ $$aeng
000125854 100__ $$0(orcid)0000-0001-6727-563X$$aUrdeitx, Pau$$uUniversidad de Zaragoza
000125854 245__ $$aMultiple myeloma cell simulation using an agent-based framework coupled with a continuous fluid model
000125854 260__ $$c2023
000125854 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125854 5203_ $$aBone marrow mechanical conditions play a key role in multiple myeloma cancer. The complex mechanical and chemical conditions, as well as the interactions with other resident cells, hinder the development of effective treatments. Agent-based computational models, capable of defining the specific conditions for every single cell, can be a useful tool to identify the specific tumor microenvironment. In this sense, we have developed a novel hybrid 3D agent-based model with coupled fluid and particle dynamics to study multiple myeloma cells’ growth. The model, which considers cell–cell interactions, cell maturation, and cell proliferation, has been implemented by employing user-defined functions in the commercial software Fluent. To validate and calibrate the model, cell sedimentation velocity and cell proliferation rates have been compared with in vitro results, as well as with another previously in-house developed model. The results show that cell proliferation increased as cell–cell, and cell–extracellular matrix interactions increased, as a result of the reduction n maturation time. Cells in contact form cell aggregates, increasing cell–cell interactions and thus cell proliferation. Saturation in cell proliferation was observed when cell aggregates increased in size and the lack of space inhibited internal cells’ proliferation. Compared with the previous model, a huge reduction in computational costs was obtained, allowing for an increase in the number of simulated cells.
000125854 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C41-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R
000125854 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125854 590__ $$a2.3$$b2023
000125854 592__ $$a0.475$$b2023
000125854 591__ $$aMATHEMATICS$$b21 / 490 = 0.043$$c2023$$dQ1$$eT1
000125854 593__ $$aEngineering (miscellaneous)$$c2023$$dQ2
000125854 593__ $$aMathematics (miscellaneous)$$c2023$$dQ2
000125854 593__ $$aComputer Science (miscellaneous)$$c2023$$dQ2
000125854 594__ $$a4.0$$b2023
000125854 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125854 700__ $$aClara-Trujillo, Sandra
000125854 700__ $$aGomez Ribelles, Jose Luis
000125854 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, Mohamed H.$$uUniversidad de Zaragoza
000125854 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000125854 773__ $$g11, 8 (2023), 1824 [13 pp.]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000125854 8564_ $$s2080899$$uhttps://zaguan.unizar.es/record/125854/files/texto_completo.pdf$$yVersión publicada
000125854 8564_ $$s2563658$$uhttps://zaguan.unizar.es/record/125854/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125854 909CO $$ooai:zaguan.unizar.es:125854$$particulos$$pdriver
000125854 951__ $$a2024-11-22-12:08:24
000125854 980__ $$aARTICLE