1,720,997 research outputs found
Mathematical modelling of malignant growth and invasion
No full textThe work presented in this thesis is concerned with the growth and development of malignancy. Such development can be thought of in terms of cell proliferation and associated morphological developments as well as in terms of active migration of malignant cells. Consequently this thesis can broadly be divided into two parts, one concerning growth dynamics in tumours, and the other active invasion of tissue by the malignancy. The first part of this thesis is concerned with the development of growth induced stresses within a multi-cell tumour spheroid (MCS), and associated structural changes. In particular, the growth and development of necrotic regions within a MCS is studied. Traditionally necrotic regions are considered to arise from the accumulation of necrotic cell debris, and assuch form under chemically adverse conditions e.g. in hypoxic or nutrient deficient regions. However, it has been observed that the connection between such conditions and necrosis formation is not so simple. In particular, necrosis formation can precede or follow hypoxia. Therefore, in this thesis we examine a novel mechanism for necrosis formation, by allowing necrotic regions to arise under conditions of adverse mechanical stress. We consequently develop a model for spheroid growth in which necrosis forms in areas of mechanical tension but does not assume this formation a priori, and show that under the right conditions such a spheroid will support necrosis formation pre-hypoxia. Models in which the MCS is composed of a viscous, an elastic, and a viscoelastic material are all considered, and it is concluded that both biologically and mathematically a tumour spheroid is best modelled as a viscoelastic medium. The second part of this thesis is concerned with active migration of cells across a substratevia haptotaxis, and the application of this motility mechanism to glioma invasion of the central nervous system. A novel model for receptor mediated haptotaxis is developed which allows adhesion, proteolysis of extra-cellular matrix (ECM) components and subsequent migration of a cell to be modelled in a biochemically accurate manner. This single cell framework is then used to derive an average cell continuum velocity and flux, and these in turn are used to examine cell population migration via receptor mediated haptotaxis. Under appropriate limits the model presented is shown to reduce to a well known class of models, and as such provides a sound biochemical basis for these previous modelling attempts. Invasion of glioma cells into the central nervous system is studied with particular attention being paid to the effects of glioma-host interactions in modulation of migration velocity and interface shape. It is concluded that, under certain circumstances, an up-regulation ofpro-migratory ECM components by the brain can inhibit glioma migration by slowing cell migration speed, and by sharpening the glioma-host interface. The phenomena of interface sharpening is seen as important, since gliomas often show diffuse boarders which present problems for their surgical resection within reasonable limits. The model outlined therefore suggests potential avenues for pre-surgical treatment which may prove very fruitful
Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes
No full textThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the “average” pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells—corresponding to naïve and formative pluripotent states and an early primitive endoderm state—and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities
Truth and beauty in physics and biology
No full textPhysicists and biologists have different conceptions of beauty. A better appreciation of these differences may bring the disciplines closer and help develop a more integrated view of life
Self-renewal without niche instruction, feedback or fine-tuning
No full textTo self-renew, stem cells must precisely balance proliferation and differentiation. Typically, this is achieved under feedback from the niche; yet many stem cells also possess an intrinsic self-renewal program that allows them to do so autonomously, as required. However, because self-renewal implies a stable equilibrium -- in which the expected stem cell number neither increases nor decreases over time -- this seems to require fine-tuning to a critical point. Here, we show that this is not the case: self-renewal can, in principle, be easily achieved without the need for extrinsic instruction, feedback or fine-tuning, by a simple 'dimerization cycle' that uses partitioning errors at cell division to reliably establish asymmetric divisions and perfectly balance symmetric divisions
Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE
No full textThe potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively
Heterogeneity and 'memory' in stem cell populations
No full textModern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories – changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli – are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines
Information-theoretic approaches to understanding stem cell variability
No full textPurpose of Review: the purpose of this study is to outline how ideas from information theory may be used to analyze single-cell data and better understand stem cell behavior.Recent Findings: recent technological breakthroughs in single-cell profiling have made it possible to interrogate cell–cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional, and noisy datasets that arise from single-cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability, and dynamics.Summary: we provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single-cell level. We also discuss how work in this area might develop in the near future
Entropy, ergodicity, and stem cell multipotency
No full textPopulations of mammalian stem cells commonly exhibit considerable cell-cell variability. However, the functional role of this diversity is unclear. Here, we analyze expression fluctuations of the stem cell surface marker Sca1 in mouse hematopoietic progenitor cells using a simple stochastic model and find that the observed dynamics naturally lie close to a critical state, thereby producing a diverse population that is able to respond rapidly to environmental changes. We propose an information- theoretic interpretation of these results that views cellular multipotency as an instance of maximum entropy statistical inferenc
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