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      Mathematical Models of Organoid Cultures

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          Abstract

          Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while still being amenable to in vitro experimental study. Compared with two-dimensional cultures, the three-dimensional structure of organoids provides a more realistic environment and structural organization of in vivo organs. Similarly, organoids are better suited to reproduce signaling pathway dynamics in vitro, due to a more realistic physiological environment. As such, organoids are a valuable tool to explore the dynamics of organogenesis and offer routes to personalized preclinical trials of cancer progression, invasion, and drug response. Complementary to experiments, mathematical and computational models are valuable instruments in the description of spatiotemporal dynamics of organoids. Simulations of mathematical models allow the study of multiscale dynamics of organoids, at both the intracellular and intercellular levels. Mathematical models also enable us to understand the underlying mechanisms responsible for phenotypic variation and the response to external stimulation in a cost- and time-effective manner. Many recent studies have developed laboratory protocols to grow organoids resembling different organs such as the intestine, brain, liver, pancreas, and mammary glands. However, the development of mathematical models specific to organoids remains comparatively underdeveloped. Here, we review the mathematical and computational approaches proposed so far to describe and predict organoid dynamics, reporting the simulation frameworks used and the models’ strengths and limitations.

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          Most cited references41

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          Organoid cultures derived from patients with advanced prostate cancer.

          The lack of in vitro prostate cancer models that recapitulate the diversity of human prostate cancer has hampered progress in understanding disease pathogenesis and therapy response. Using a 3D organoid system, we report success in long-term culture of prostate cancer from biopsy specimens and circulating tumor cells. The first seven fully characterized organoid lines recapitulate the molecular diversity of prostate cancer subtypes, including TMPRSS2-ERG fusion, SPOP mutation, SPINK1 overexpression, and CHD1 loss. Whole-exome sequencing shows a low mutational burden, consistent with genomics studies, but with mutations in FOXA1 and PIK3R1, as well as in DNA repair and chromatin modifier pathways that have been reported in advanced disease. Loss of p53 and RB tumor suppressor pathway function are the most common feature shared across the organoid lines. The methodology described here should enable the generation of a large repertoire of patient-derived prostate cancer lines amenable to genetic and pharmacologic studies. Copyright © 2014 Elsevier Inc. All rights reserved.
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            Human Primary Liver Cancer -derived Organoid Cultures for disease modelling and drug screening

            Human liver cancer research currently lacks in vitro models that faithfully recapitulate the pathophysiology of the original tumour. We recently described a novel, near-physiological organoid culture system, where primary human healthy liver cells form long-term expanding organoids that retain liver tissue function and genetic stability. Here, we extend this culture system to the propagation of primary liver cancer (PLC) organoids from three of the most common PLC subtypes: hepatocellular carcinoma (HCC), cholangiocarcinoma (CC) and combined HCC/CC (CHC) tumours. PLC-derived organoid cultures preserve the histological architecture, gene expression and genomic landscape of the original tumour, allowing discrimination between different tumour tissues and subtypes, even after long term expansion in culture in the same medium conditions. Xenograft studies demonstrate that the tumourogenic potential, histological features and metastatic properties of PLC-derived organoids are preserved in vivo. PLC-derived organoids are amenable for biomarker identification and drug screening testing and lead to the identification of the ERK inhibitor SCH772984 as a potential therapeutic agent for primary liver cancer. We thus demonstrate the wide-ranging biomedical utilities of PLC-derived organoid models in furthering the understanding of liver cancer biology and in developing personalized medicine approaches for the disease.
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              Reaction-diffusion model as a framework for understanding biological pattern formation.

              The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                19 September 2019
                2019
                : 10
                : 873
                Affiliations
                [1] 1Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom
                [2] 2School of Cellular and Molecular Medicine, University of Bristol , Bristol, United Kingdom
                [3] 3Bristol Centre for Synthetic Biology, University of Bristol , Bristol, United Kingdom
                Author notes

                Edited by: Thimios Mitsiadis, University of Zurich, Switzerland

                Reviewed by: Nenad Filipovic, University of Kragujevac, Serbia; Eumorphia Remboutsika, National and Kapodistrian University of Athens, Greece; Claudio Cantù, Linköping, University, Sweden

                *Correspondence: Martin Homer, martin.homer@ 123456bristol.ac.uk

                This article was submitted to Stem Cell Research, a section of the journal Frontiers in Genetics

                †These authors share last authorship

                Article
                10.3389/fgene.2019.00873
                6761251
                31592020
                931e9537-1de5-491a-83af-fe3ee5d2922c
                Copyright © 2019 Montes-Olivas, Marucci and Homer

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 May 2019
                : 20 August 2019
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 72, Pages: 10, Words: 5147
                Categories
                Genetics
                Mini Review

                Genetics
                organoids,mathematical modeling,agent-based models,3d tissue,differential equations,computational modeling

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