Cell-Based Computational Modeling of Vascular Morphogenesis Using Tissue Simulation Toolkit

Daub, Joséphine T.; Merks, Roeland

doi:10.1007/978-1-4939-1462-3_6

Cited by 25 publications

(32 citation statements)

References 48 publications

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“…We consider a population of cells on a grid that search for resources to be able to replicate. We explicitly account for cell shape and interactions by implementing a 2D hybrid Cellular Potts Model on a square lattice [34–36]. Cells can adhere to each other if they express matching ligands and receptors on their surface.…”

Section: Resultsmentioning

confidence: 99%

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Evolution of multicellularity by collective integration of spatial information

Colizzi

Vroomans

Merks

2020

Preprint

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10At the origin of multicellularity, cells may have evolved aggregation in 11 response to predation, for functional specialisation or to allow large-scale 12 integration of environmental cues. These group-level properties emerged 13 from the interactions between cells in a group, and determined the selection 14 pressures experienced by these cells. 15 We investigate the evolution of multicellularity with an evolutionary 16 model where cells search for resources by chemotaxis in a shallow, noisy 17 gradient. Cells can evolve their adhesion to others in a periodically chang-18 ing environment, where a cell's fitness solely depends on its distance from 19 the gradient source. 20 We show that multicellular aggregates evolve because they perform chemo-21 taxis more efficiently than single cells. Only when the environment changes 22 too frequently, a unicellular state evolves which relies on cell dispersal. Both 23 strategies prevent the invasion of the other through interference competition, 24 creating evolutionary bi-stability. Therefore, collective behaviour can be an 25 emergent selective driver for undifferentiated multicellularity. 26 1 1 Introduction 27The evolution of multicellularity is a major transition in individuality, from au-28 tonomously replicating cells to groups of interdependent cells forming a higher-29 level of organisation [1, 2]. It has evolved independently several times across the 30 tree of life [3, 4]. Comparative genomics suggests [5], and experimental evolution 31 confirms [6, 7] that the increase of cell-cell adhesion drives the early evolution 32 of (undifferentiated) multicellularity. Increased cell adhesion may be temporally 33 limited and/or may be triggered by environmental changes (e.g. in Dictyostelids 34 and Myxobacteria [8, 9]). Moreover, multicellular organisation may come about 35 either by aggregation of genetically distinct cells or by incomplete separation after 36 cell division [8, 10]. 37The genetic toolkit and the cellular components that allow for multicellularity 38 -including adhesion proteins -pre-date multicellular species and are found in 39 their unicellular relatives [8,[11][12][13]. Aggregates of cells can organise themselves 40 by exploiting these old components in the new multicellular context, allowing 41 them to perform novel functions (or to perform old functions in novel ways) that 42 may confer some competitive advantage over single cells. Greater complexity can 43 later evolve by coordinating the division of tasks between different cell lineages 44 of the same organism (e.g. in the soma-germline division of labour), giving rise 45 to embryonic development. Nevertheless, the properties of early multicellular 46 organisms are defined by self-organised aggregate cell dynamics, and the space of 47 possible multicellular outcomes and emergent functions resulting from such self-48 organisation seems large -even with limited differential adhesion and signalling 49 between cells. However, the evolution of emergent functions as a consequence of 50 ad...

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Section: Resultsmentioning

confidence: 99%

“…The model is a hybrid Cellular Potts Model implemented with the Tissue Simulation Toolkit [36]. A population of N cells exists on a regular square lattice Λ 1 ⊂ ℤ 2 .…”

Section: Modelmentioning

confidence: 99%

Evolution of multicellularity by collective integration of spatial information

Colizzi

Vroomans

Merks

2020

Preprint

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“…The authors showed that differences in the matrix binding affinity of vascular endothelial growth factor (VEGF) isoforms resulted in vastly different capillary morphologies. Merks and co-workers [94,95] …”

Section: Mathematical Models Of Vascularized Tumours and Angiogenesismentioning

confidence: 99%

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Karolak

Markov

McCawley

et al. 2018

J. R. Soc. Interface.

114

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A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multicomponent tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

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“…It is the first work to study systemic modeling of tumor growth and immune response within an integrated 3D model (Figure 5). In addition, tumor progression is related to angiogenesis; therefore, it is necessary to integrate vascularization into ABM model to reflect the endothelial cells interaction with cancer cells through some key factors, such as VEGF [98]. Wang et al proposed an ABM model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment [99].…”

Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

Mathematical and Computational Modeling in Complex Biological Systems

Yan

et al. 2017

BioMed Research International

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The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

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Cell-Based Computational Modeling of Vascular Morphogenesis Using Tissue Simulation Toolkit

Cited by 25 publications

References 48 publications

Evolution of multicellularity by collective integration of spatial information

Evolution of multicellularity by collective integration of spatial information

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Mathematical and Computational Modeling in Complex Biological Systems

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