title: 3D hybrid modelling of vascular network formation.
abstract: We develop an off-lattice, agent-based model to describe vasculogenesis, the de novo formation of blood vessels from endothelial progenitor cells during development. The endothelial cells that comprise our vessel network are viewed as linearly elastic spheres that move in response to the forces they experience. We distinguish two types of endothelial cells: vessel elements are contained within the network and tip cells are located at the ends of vessels. Tip cells move in response to mechanical forces caused by interactions with neighbouring vessel elements and the local tissue environment, chemotactic forces and a persistence force which accounts for their tendency to continue moving in the same direction. Vessel elements are subject to similar mechanical forces but are insensitive to chemotaxis. An angular persistence force representing interactions with the local tissue is introduced to stabilise buckling instabilities caused by cell proliferation. Only vessel elements proliferate, at rates which depend on their degree of stretch: elongated elements have increased rates of proliferation, and compressed elements have reduced rates. Following division, the fate of the new cell depends on the local mechanical environment: the probability of forming a new sprout is increased if the parent vessel is highly compressed and the probability of being incorporated into the parent vessel increased if the parent is stretched. Simulation results reveal that our hybrid model can reproduce the key qualitative features of vasculogenesis. Extensive parameter sensitivity analyses show that significant changes in network size and morphology are induced by varying the chemotactic sensitivity of tip cells, and the sensitivities of the proliferation rate and the sprouting probability to mechanical stretch. Varying the chemotactic sensitivity directly influences the directionality of the networks. The degree of branching, and thereby the density of the networks, is influenced by the sprouting probability. Glyphs that simultaneously depict several network properties are introduced to show how these and other network quantities change over time and also as model parameters vary. We also show how equivalent glyphs constructed from in vivo data could be used to discriminate between normal and tumour vasculature and, in the longer term, for model validation. We conclude that our biomechanical hybrid model can generate vascular networks that are qualitatively similar to those generated from in vitro and in vivo experiments.
authors.affiliation: Center Systems Biology, University of Stuttgart, Stuttgart, Germany. Electronic address: holger.perfahl@ibvt.uni-stuttgart.de.
authors.forename: Holger
authors.initials: H
authors.lastname: Perfahl
authors.affiliation: School of Mathematics and Statistics, University of Melbourne, Australia.
authors.forename: Barry D
authors.initials: BD
authors.lastname: Hughes
authors.affiliation: ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain; Centre de Recerca Matemàtica, Campus de Bellaterra, Barcelona, Spain; Departament de Matemàtiques, Universitat Autònoma de Barcelona, Barcelona, Spain.
authors.forename: Tomás
authors.initials: T
authors.lastname: Alarcón
authors.affiliation: Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
authors.forename: Philip K
authors.initials: PK
authors.lastname: Maini
authors.affiliation: H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
authors.forename: Mark C
authors.initials: MC
authors.lastname: Lloyd
authors.affiliation: Center Systems Biology, University of Stuttgart, Stuttgart, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Reuss
authors.affiliation: Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
authors.forename: Helen M
authors.initials: HM
authors.lastname: Byrne
mesh_terms: D000818:Animals; D002455:Cell Division; D002633:Chemotaxis; D042783:Endothelial Cells / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008955:Models, Cardiovascular; D009369:Neoplasms / Q000737:chemistry / Q000378:metabolism / Q000473:pathology; D009389:Neovascularization, Pathologic / Q000378:metabolism / Q000473:pathology; D051381:Rats
keywords: Agent-based modelling; Angiogenesis; Mechanical model; Vasculogenesis