000127561 001__ 127561
000127561 005__ 20240731103343.0
000127561 0247_ $$2doi$$a10.1016/j.isci.2023.107164
000127561 0248_ $$2sideral$$a134573
000127561 037__ $$aART-2023-134573
000127561 041__ $$aeng
000127561 100__ $$aCamacho-Gomez, Daniel$$uUniversidad de Zaragoza
000127561 245__ $$aA hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids
000127561 260__ $$c2023
000127561 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127561 5203_ $$aHow cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.
000127561 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-124271OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21
000127561 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000127561 590__ $$a4.6$$b2023
000127561 592__ $$a1.497$$b2023
000127561 591__ $$aMULTIDISCIPLINARY SCIENCES$$b19 / 134 = 0.142$$c2023$$dQ1$$eT1
000127561 593__ $$aMultidisciplinary$$c2023$$dQ1
000127561 594__ $$a7.2$$b2023
000127561 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127561 700__ $$aSorzabal-Bellido, Ioritz
000127561 700__ $$aOrtiz-de-Solorzano, Carlos
000127561 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000127561 700__ $$0(orcid)0000-0002-1878-8997$$aGómez-Benito, María José$$uUniversidad de Zaragoza
000127561 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000127561 773__ $$g26, 7 (2023), 107164 [24 pp.]$$piScience$$tISCIENCE$$x2589-0042
000127561 8564_ $$s4837410$$uhttps://zaguan.unizar.es/record/127561/files/texto_completo.pdf$$yVersión publicada
000127561 8564_ $$s1319346$$uhttps://zaguan.unizar.es/record/127561/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127561 909CO $$ooai:zaguan.unizar.es:127561$$particulos$$pdriver
000127561 951__ $$a2024-07-31-09:51:05
000127561 980__ $$aARTICLE