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The Voronoi cell-based model (VCBM) is an off-lattice ABM that represents individual cancer cell shapes and simulates proliferation and movement. This work extends the VCBM to capture biphasic growth with two distinct phases and uses approximate Bayesian computation to calibrate parameters from in vivo data on breast, ovarian, and pancreatic cancers. The method estimates parameter distributions and yields uncertainty, including the switching time between phases and maturation timing of daughter cells, enabling refined biological comparisons.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/calibration-of-agent-based-models-for-monophasic-and-biphasic-tumour-growth-using-approximate-bayesian-computation/331848/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/calibration-of-agent-based-models-for-monophasic-and-biphasic-tumour-growth-using-approximate-bayesian-computation/331848.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why are agent-based models difficult to validate with experimental measurements?","Question",{"text":112,"@type":113},"ABMs capture tumour stochasticity but are often challenging to validate because their complexity makes it harder to relate model outputs directly to experimental data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the Voronoi cell-based model (VCBM) used for in this study?",{"text":117,"@type":113},"The VCBM is an off-lattice agent-based model that uses Voronoi tessellation to represent individual cell shapes and to simulate cancer cell proliferation and movement.",{"name":119,"@type":110,"acceptedAnswer":120},"How does approximate Bayesian computation help in calibrating the model?",{"text":121,"@type":113},"Approximate Bayesian computation is used to estimate the distribution of model parameters governing cell proliferation, and to produce outputs that match in vivo experimental measurements while quantifying uncertainty.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},331848,1790709477,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation  \nXiaoyu Wang1,2 · Adrianne L. Jenner1,2 · Robert Salomone2,3 · David J. Warne1,2 · Christopher Drovandi1,2  \nReceived: 28 June 2023 / Revised: 25 October 2023 / Accepted: 27 December 2023 /  \nPublished online: 15 February 2024 © The Author(s) 2024  \nAbstract  \nAgent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence oftwo distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases ofthe biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.  \nKeywords Agent-based model · Cancer · Likelihood-free inference · Parameter estimation · Tumour growth  \nB Xiaoyu Wang [x311.wang@hdr.qut.edu.au](x311.wang@hdr.qut.edu.au)  \n1 School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia  \n2 Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia  \n3 School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia  \nMathematics Subject Classification 92-08 · 92-10 · 62-F15  \n1 Introduction  \nCancer is a disease that arises through the progressive alteration of normal cells. Solid cancers form what is commonly referred to as tumours, which are populations of cancerous cells joined together with connective tissue (Weinberg and Weinberg 2006) . Over time, these tumours expand and take up residency in healthy tissue. To elucidate the mechanisms of the cancer pathophysiology, researchers have mainly focused on the analysis of aberrant cell dynamics within tumour structures. The pivotal role these abnormal cells play in oncogenesis constitutes a salient focus of these investigations (Noble 2002; Markowetz 2017) .  \nMathematical modelling of cancer growth and development has been used to study the dynamical process of cancer cells for many years (Altrock et al. 2015 ; Beerenwinkel et al. 2015; Barbolosi et al. 2016; Tabassum et al. 2019) . Deterministic approaches, such as systems of ordinary differential equations (ODEs) and systems of partial differential equations (PDEs), have been used successfully to model cancer growth (Villasana and Radunskaya 2003; Yafia 2011; Tao et al. 2014; Jenner et al. 2020b; Dehingia et al. 2021; Klowss et al. 2022; VandenHeuvel et al. 2022) . While insightful, generally these models do not capture the phenotypical and spatial heterogeneity that arises through stochastic processes of tumour growth, or consider the behaviour at an individual cancer cell level (Irurzun-Arana et al. 2020) . In turn, such models often do not capture dynamical processes such as individual cell proliferation (Iyer et al. 2011; Sahoo et al. 2011) and cell movement (Groh and ","cbCaio4IdCPEk8om","https://ap.wps.com/l/cbCaio4IdCPEk8om","pdf",1374982,25,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why are agent-based models difficult to validate with experimental measurements?\",\"answer\":\"ABMs capture tumour stochasticity but are often challenging to validate because their complexity makes it harder to relate model outputs directly to experimental data.\"},{\"question\":\"What is the Voronoi cell-based model (VCBM) used for in this study?\",\"answer\":\"The VCBM is an off-lattice agent-based model that uses Voronoi tessellation to represent individual cell shapes and to simulate cancer cell proliferation and movement.\"},{\"question\":\"How does approximate Bayesian computation help in calibrating the model?\",\"answer\":\"Approximate Bayesian computation is used to estimate the distribution of model parameters governing cell proliferation, and to produce outputs that match in vivo experimental measurements while quantifying uncertainty.\"}]","Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation | PDF",1789995480,63]