Rosalia Ferraro, Jasmin Di Franco, Sergio Caserta, Stefano Guido
Cell spheroids are a widely used model to investigate cell-cell and cell-matrix interactions in a 3D microenvironment in vitro. Most research on cell spheroids has been focused on their response to various stimuli under static conditions. Recently, the effect of flow on cell spheroids has been investigated in the context of tumor invasion in interstitial space. In particular, microfluidic perfusion of cell spheroids embedded in a collagen matrix has been shown to modulate cell-cell adhesion and to represent a possible mechanism promoting tumor invasion by interstitial flow. However, studies on the effects of well-defined flow fields on cell spheroids are lacking in the literature. Here, we apply simple shear flow to cell spheroids in a parallel plate apparatus while observing their morphology by optical microscopy. By using image analysis techniques, we show that cell spheroids rotate under flow as rigid particles. As time goes on, cells from the outer layer detach from the sheared cell spheroids and are carried away by the flow. Hence, the size of cell spheroids declines with time at a rate increasing with the external shear stress, which can be used to estimate cell-cell adhesion. The technique proposed in this work allows one to correlate flow-induced effects with microscopy imaging of cell spheroids in a well-established shear flow field, thus providing a method to obtain quantitative results which are relevant in the general field of mechanobiology.
{"title":"The morphology of cell spheroids in simple shear flow","authors":"Rosalia Ferraro, Jasmin Di Franco, Sergio Caserta, Stefano Guido","doi":"arxiv-2404.07528","DOIUrl":"https://doi.org/arxiv-2404.07528","url":null,"abstract":"Cell spheroids are a widely used model to investigate cell-cell and\u0000cell-matrix interactions in a 3D microenvironment in vitro. Most research on\u0000cell spheroids has been focused on their response to various stimuli under\u0000static conditions. Recently, the effect of flow on cell spheroids has been\u0000investigated in the context of tumor invasion in interstitial space. In\u0000particular, microfluidic perfusion of cell spheroids embedded in a collagen\u0000matrix has been shown to modulate cell-cell adhesion and to represent a\u0000possible mechanism promoting tumor invasion by interstitial flow. However,\u0000studies on the effects of well-defined flow fields on cell spheroids are\u0000lacking in the literature. Here, we apply simple shear flow to cell spheroids\u0000in a parallel plate apparatus while observing their morphology by optical\u0000microscopy. By using image analysis techniques, we show that cell spheroids\u0000rotate under flow as rigid particles. As time goes on, cells from the outer\u0000layer detach from the sheared cell spheroids and are carried away by the flow.\u0000Hence, the size of cell spheroids declines with time at a rate increasing with\u0000the external shear stress, which can be used to estimate cell-cell adhesion.\u0000The technique proposed in this work allows one to correlate flow-induced\u0000effects with microscopy imaging of cell spheroids in a well-established shear\u0000flow field, thus providing a method to obtain quantitative results which are\u0000relevant in the general field of mechanobiology.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140575001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The motility of eukaryotic cells is strongly influenced by their environment, with confined cells often developing qualitatively different motility patterns from those migrating on simple two-dimensional substrates. Recent experiments, coupled with data-driven methods to extract a cell's equation of motion, showed that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed on two-state adhesive micropatterns (two large squares connected by a narrow bridge), while they remain stationary on average in rectangular confinements. In contrast, healthy MCF10A cells migrating on the two-state micropattern are bistable, i.e., they settle into either basin on average with only noise-induced hops between the two states. We can capture all these behaviors with a single computational phase field model of a crawling cell, under the assumption that contact with non-adhesive substrate inhibits the cell front. Our model predicts that larger and softer cells are more likely to persistently hop, while smaller and stiffer cells are more likely to be bistable. Other key factors controlling cell migration are the frequency of protrusions and their magnitude of noise. Our results show that relatively simple assumptions about how cells sense their geometry can explain a wide variety of different cell behaviors, and show the power of data-driven approaches to characterize both experiment and simulation.
{"title":"Nonlinear dynamics of confined cell migration -- modeling and inference","authors":"Pedrom Zadeh, Brian A. Camley","doi":"arxiv-2404.07390","DOIUrl":"https://doi.org/arxiv-2404.07390","url":null,"abstract":"The motility of eukaryotic cells is strongly influenced by their environment,\u0000with confined cells often developing qualitatively different motility patterns\u0000from those migrating on simple two-dimensional substrates. Recent experiments,\u0000coupled with data-driven methods to extract a cell's equation of motion, showed\u0000that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed\u0000on two-state adhesive micropatterns (two large squares connected by a narrow\u0000bridge), while they remain stationary on average in rectangular confinements.\u0000In contrast, healthy MCF10A cells migrating on the two-state micropattern are\u0000bistable, i.e., they settle into either basin on average with only\u0000noise-induced hops between the two states. We can capture all these behaviors\u0000with a single computational phase field model of a crawling cell, under the\u0000assumption that contact with non-adhesive substrate inhibits the cell front.\u0000Our model predicts that larger and softer cells are more likely to persistently\u0000hop, while smaller and stiffer cells are more likely to be bistable. Other key\u0000factors controlling cell migration are the frequency of protrusions and their\u0000magnitude of noise. Our results show that relatively simple assumptions about\u0000how cells sense their geometry can explain a wide variety of different cell\u0000behaviors, and show the power of data-driven approaches to characterize both\u0000experiment and simulation.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keith L Chambers, Mary R Myerscough, Michael G Watson, Helen M Byrne
Macrophages in atherosclerotic lesions exhibit a spectrum of behaviours or phenotypes. The phenotypic distribution of monocyte-derived macrophages (MDMs), its correlation with MDM lipid content, and relation to blood lipoprotein densities are not well understood. Of particular interest is the balance between low density lipoproteins (LDL) and high density lipoproteins (HDL), which carry bad and good cholesterol respectively. To address these issues, we have developed a mathematical model for early atherosclerosis in which the MDM population is structured by phenotype and lipid content. The model admits a simpler, closed subsystem whose analysis shows how lesion composition becomes more pathological as the blood density of LDL increases relative to the HDL capacity. We use asymptotic analysis to derive a power-law relationship between MDM phenotype and lipid content at steady-state. This relationship enables us to understand why, for example, lipid-laden MDMs have a more inflammatory phenotype than lipid-poor MDMs when blood LDL lipid density greatly exceeds HDL capacity. We show further that the MDM phenotype distribution always attains a local maximum, while the lipid content distribution may be unimodal, adopt a quasi-uniform profile or decrease monotonically. Pathological lesions exhibit a local maximum in both the phenotype and lipid content MDM distributions, with the maximum at an inflammatory phenotype and near the lipid content capacity respectively. These results illustrate how macrophage heterogeneity arises in early atherosclerosis and provide a framework for future model validation through comparison with single-cell RNA sequencing data.
{"title":"Blood lipoproteins shape the phenotype and lipid content of early atherosclerotic lesion macrophages: a dual-structured mathematical model","authors":"Keith L Chambers, Mary R Myerscough, Michael G Watson, Helen M Byrne","doi":"arxiv-2404.07051","DOIUrl":"https://doi.org/arxiv-2404.07051","url":null,"abstract":"Macrophages in atherosclerotic lesions exhibit a spectrum of behaviours or\u0000phenotypes. The phenotypic distribution of monocyte-derived macrophages (MDMs),\u0000its correlation with MDM lipid content, and relation to blood lipoprotein\u0000densities are not well understood. Of particular interest is the balance\u0000between low density lipoproteins (LDL) and high density lipoproteins (HDL),\u0000which carry bad and good cholesterol respectively. To address these issues, we\u0000have developed a mathematical model for early atherosclerosis in which the MDM\u0000population is structured by phenotype and lipid content. The model admits a\u0000simpler, closed subsystem whose analysis shows how lesion composition becomes\u0000more pathological as the blood density of LDL increases relative to the HDL\u0000capacity. We use asymptotic analysis to derive a power-law relationship between\u0000MDM phenotype and lipid content at steady-state. This relationship enables us\u0000to understand why, for example, lipid-laden MDMs have a more inflammatory\u0000phenotype than lipid-poor MDMs when blood LDL lipid density greatly exceeds HDL\u0000capacity. We show further that the MDM phenotype distribution always attains a\u0000local maximum, while the lipid content distribution may be unimodal, adopt a\u0000quasi-uniform profile or decrease monotonically. Pathological lesions exhibit a\u0000local maximum in both the phenotype and lipid content MDM distributions, with\u0000the maximum at an inflammatory phenotype and near the lipid content capacity\u0000respectively. These results illustrate how macrophage heterogeneity arises in\u0000early atherosclerosis and provide a framework for future model validation\u0000through comparison with single-cell RNA sequencing data.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140575002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collective response to external directional cues like electric fields plays a pivotal role in processes such as tissue development, regeneration, and wound healing. In this study we focus on the impact of anisotropy in cell shape and local cell alignment on the collective response to electric fields. We model elongated cells that have a different accuracy sensing the field depending on their orientation with respect to the field. Elongated cells often line up with their long axes in the same direction - "nematic order" - does this help the group of cells sense the field more accurately? We use simulations of a simple model to show that if cells orient themselves perpendicular to their average velocity, alignment of a cell's long axis to its nearest neighbors' orientation can enhance the directional response to electric fields. However, for cells to benefit from aligning, their accuracy of sensing must be strongly dependent on cell orientation. We also show that cell-cell adhesion modulates the accuracy of cells in the group.
{"title":"Does nematic order allow groups of elongated cells to sense electric fields better?","authors":"Kurmanbek Kaiyrbekov, Brian A. Camley","doi":"arxiv-2404.04723","DOIUrl":"https://doi.org/arxiv-2404.04723","url":null,"abstract":"Collective response to external directional cues like electric fields plays a\u0000pivotal role in processes such as tissue development, regeneration, and wound\u0000healing. In this study we focus on the impact of anisotropy in cell shape and\u0000local cell alignment on the collective response to electric fields. We model\u0000elongated cells that have a different accuracy sensing the field depending on\u0000their orientation with respect to the field. Elongated cells often line up with\u0000their long axes in the same direction - \"nematic order\" - does this help the\u0000group of cells sense the field more accurately? We use simulations of a simple\u0000model to show that if cells orient themselves perpendicular to their average\u0000velocity, alignment of a cell's long axis to its nearest neighbors' orientation\u0000can enhance the directional response to electric fields. However, for cells to\u0000benefit from aligning, their accuracy of sensing must be strongly dependent on\u0000cell orientation. We also show that cell-cell adhesion modulates the accuracy\u0000of cells in the group.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles S. Wright, Kunaal Joshi, Rudro R. Biswas, Srividya Iyer-Biswas
Organisms maintain the status quo, holding key physiological variables constant to within an acceptable tolerance, and yet adapt with precision and plasticity to dynamic changes in externalities. What organizational principles ensure such exquisite yet robust control of systems-level "state variables" in complex systems with an extraordinary number of moving parts and fluctuating variables? Here we focus on these issues in the specific context of intra- and intergenerational life histories of individual bacterial cells, whose biographies are precisely charted via high-precision dynamic experiments using the SChemostat technology. We highlight intra- and intergenerational scaling laws and other "emergent simplicities" revealed by these high-precision data. In turn, these facilitate a principled route to dimensional reduction of the problem, and serve as essential building blocks for phenomenological and mechanistic theory. Parameter-free data-theory matches for multiple organisms validate theory frameworks, and explicate the systems physics of stochastic homeostasis and adaptation.
{"title":"Emergent Simplicities in the Living Histories of Individual Cells","authors":"Charles S. Wright, Kunaal Joshi, Rudro R. Biswas, Srividya Iyer-Biswas","doi":"arxiv-2404.01682","DOIUrl":"https://doi.org/arxiv-2404.01682","url":null,"abstract":"Organisms maintain the status quo, holding key physiological variables\u0000constant to within an acceptable tolerance, and yet adapt with precision and\u0000plasticity to dynamic changes in externalities. What organizational principles\u0000ensure such exquisite yet robust control of systems-level \"state variables\" in\u0000complex systems with an extraordinary number of moving parts and fluctuating\u0000variables? Here we focus on these issues in the specific context of intra- and\u0000intergenerational life histories of individual bacterial cells, whose\u0000biographies are precisely charted via high-precision dynamic experiments using\u0000the SChemostat technology. We highlight intra- and intergenerational scaling\u0000laws and other \"emergent simplicities\" revealed by these high-precision data.\u0000In turn, these facilitate a principled route to dimensional reduction of the\u0000problem, and serve as essential building blocks for phenomenological and\u0000mechanistic theory. Parameter-free data-theory matches for multiple organisms\u0000validate theory frameworks, and explicate the systems physics of stochastic\u0000homeostasis and adaptation.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140575007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wounds in epithelial tissues compromise their vital role in homeostasis. A rapid and efficient wound healing encompasses different mechanisms, which includes the formation of a contractile actin-myosin cable around its edge, known as the purse-string mechanism. We combine mean-field calculations and numerical simulations of the Vertex model to study the interplay between tissue properties and the purse-string mechanism and its impact on the healing process. We find different regimes, where the wound opens, closes partially or completely. We also derive an analytic expression for the closure time which is validated by numerical simulations. This study establishes under which conditions the purse-string mechanism suffices for closure, providing an analytical mean-field expression for the respective thresholds.
{"title":"Healing Regimes for Microscopic Wounds in the Vertex Model of Cell Tissues","authors":"R. F. Almada, N. A. M. Araujo, P. Patricio","doi":"arxiv-2403.14501","DOIUrl":"https://doi.org/arxiv-2403.14501","url":null,"abstract":"Wounds in epithelial tissues compromise their vital role in homeostasis. A\u0000rapid and efficient wound healing encompasses different mechanisms, which\u0000includes the formation of a contractile actin-myosin cable around its edge,\u0000known as the purse-string mechanism. We combine mean-field calculations and\u0000numerical simulations of the Vertex model to study the interplay between tissue\u0000properties and the purse-string mechanism and its impact on the healing\u0000process. We find different regimes, where the wound opens, closes partially or\u0000completely. We also derive an analytic expression for the closure time which is\u0000validated by numerical simulations. This study establishes under which\u0000conditions the purse-string mechanism suffices for closure, providing an\u0000analytical mean-field expression for the respective thresholds.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Josua StadelmaierUniversity of Tübingen, Brandon MaloneNEC OncoImmunity, Ralf EggelingUniversity of Tübingen
We study the prediction of T-cell response for specific given peptides, which could, among other applications, be a crucial step towards the development of personalized cancer vaccines. It is a challenging task due to limited, heterogeneous training data featuring a multi-domain structure; such data entail the danger of shortcut learning, where models learn general characteristics of peptide sources, such as the source organism, rather than specific peptide characteristics associated with T-cell response. Using a transformer model for T-cell response prediction, we show that the danger of inflated predictive performance is not merely theoretical but occurs in practice. Consequently, we propose a domain-aware evaluation scheme. We then study different transfer learning techniques to deal with the multi-domain structure and shortcut learning. We demonstrate a per-source fine tuning approach to be effective across a wide range of peptide sources and further show that our final model outperforms existing state-of-the-art approaches for predicting T-cell responses for human peptides.
我们研究了针对特定多肽的 T 细胞反应预测，除其他应用外，这可能是开发个性化癌症疫苗的关键一步。由于具有多域结构的异构训练数据有限，这是一项具有挑战性的任务；此类数据存在捷径学习的危险，即模型学习的是肽源的一般特征，如源生物，而不是与 T 细胞反应相关的特定肽特征。通过使用 T 细胞反应预测的转换器模型，我们发现预测性能膨胀的危险不仅存在于理论上，而且在实践中也时有发生。因此，我们提出了一种领域感知评估方案。然后，我们研究了不同的迁移学习技术，以处理多领域结构和捷径学习。我们证明了按来源进行微调的方法在广泛的肽来源中是有效的，并进一步证明了我们的最终模型在预测人类肽的 T 细胞反应方面优于现有的最先进方法。
{"title":"Transfer Learning for T-Cell Response Prediction","authors":"Josua StadelmaierUniversity of Tübingen, Brandon MaloneNEC OncoImmunity, Ralf EggelingUniversity of Tübingen","doi":"arxiv-2403.12117","DOIUrl":"https://doi.org/arxiv-2403.12117","url":null,"abstract":"We study the prediction of T-cell response for specific given peptides, which\u0000could, among other applications, be a crucial step towards the development of\u0000personalized cancer vaccines. It is a challenging task due to limited,\u0000heterogeneous training data featuring a multi-domain structure; such data\u0000entail the danger of shortcut learning, where models learn general\u0000characteristics of peptide sources, such as the source organism, rather than\u0000specific peptide characteristics associated with T-cell response. Using a transformer model for T-cell response prediction, we show that the\u0000danger of inflated predictive performance is not merely theoretical but occurs\u0000in practice. Consequently, we propose a domain-aware evaluation scheme. We then\u0000study different transfer learning techniques to deal with the multi-domain\u0000structure and shortcut learning. We demonstrate a per-source fine tuning\u0000approach to be effective across a wide range of peptide sources and further\u0000show that our final model outperforms existing state-of-the-art approaches for\u0000predicting T-cell responses for human peptides.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adéla Šterberová, Andreea Dincu, Stijn Oudshoorn, Vincent van Duinen, Lu Cao
Tumor angiogenesis concerns the development of new blood vessels supplying the necessary nutrients for the further development of existing tumor cells. The entire process is complex, involving the production and consumption of chemicals, endothelial cell transitions as well as cell interactions, divisions, and migrations. Microfluidic cell culture platform has been used to study angiogenesis of endothelial cells derived from human induced pluripotent stem cells (iPSC-ECs) for a physiological relevant micro-environment. In this paper, we elaborate on how to define a pipeline for simulating the transformation and process that an iPSC-derived endothelial cell goes through in this biological scenario. We leverage the robustness and simplicity of Petri nets for modeling the cell transformation and associated constraints. The environmental and spacial factors are added using custom 2-dimensional grids. Although the pipeline does not capture the entire complexity of tumor angiogenesis, we are able to capture the essence of endothelial cell transitions in tumor angiogenesis using this conceptually simplified solution.
{"title":"Modeling iPSC-derived Endothelial Cell Transition in Tumor Angiogenesis using Petri Nets","authors":"Adéla Šterberová, Andreea Dincu, Stijn Oudshoorn, Vincent van Duinen, Lu Cao","doi":"arxiv-2403.06555","DOIUrl":"https://doi.org/arxiv-2403.06555","url":null,"abstract":"Tumor angiogenesis concerns the development of new blood vessels supplying\u0000the necessary nutrients for the further development of existing tumor cells.\u0000The entire process is complex, involving the production and consumption of\u0000chemicals, endothelial cell transitions as well as cell interactions,\u0000divisions, and migrations. Microfluidic cell culture platform has been used to\u0000study angiogenesis of endothelial cells derived from human induced pluripotent\u0000stem cells (iPSC-ECs) for a physiological relevant micro-environment. In this\u0000paper, we elaborate on how to define a pipeline for simulating the\u0000transformation and process that an iPSC-derived endothelial cell goes through\u0000in this biological scenario. We leverage the robustness and simplicity of Petri\u0000nets for modeling the cell transformation and associated constraints. The\u0000environmental and spacial factors are added using custom 2-dimensional grids.\u0000Although the pipeline does not capture the entire complexity of tumor\u0000angiogenesis, we are able to capture the essence of endothelial cell\u0000transitions in tumor angiogenesis using this conceptually simplified solution.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We derive the main properties of adaptive Hagen-Poiseuille flows in elastic microchannel networks akin to biological veins in organisms. We show that adaptive Hagen-Poiseuille flows successfully simulate key features of textit{Physarum polycephalum} networks, replicating physiological out-of-equilibrium phenomena like peristalsis and shuttle streaming, associated with the mechanism of nutrient transport in textit{Physarum}. A new topological steady state has been identified for asynchronous adaptation, supporting out-of-equilibrium laminar fluxes. Adaptive Hagen-Poiseuille flows show saturation effects on the fluxes in contractile veins, as observed in animal and artificial contractile veins.
{"title":"Properties of Hagen-Poiseuille flows in channel networks","authors":"A. F. Valente, R. Almeida, R. Dilão","doi":"arxiv-2402.19185","DOIUrl":"https://doi.org/arxiv-2402.19185","url":null,"abstract":"We derive the main properties of adaptive Hagen-Poiseuille flows in elastic\u0000microchannel networks akin to biological veins in organisms. We show that\u0000adaptive Hagen-Poiseuille flows successfully simulate key features of\u0000textit{Physarum polycephalum} networks, replicating physiological\u0000out-of-equilibrium phenomena like peristalsis and shuttle streaming, associated\u0000with the mechanism of nutrient transport in textit{Physarum}. A new\u0000topological steady state has been identified for asynchronous adaptation,\u0000supporting out-of-equilibrium laminar fluxes. Adaptive Hagen-Poiseuille flows\u0000show saturation effects on the fluxes in contractile veins, as observed in\u0000animal and artificial contractile veins.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, presents profound challenges. This study presents LeukoGraph, a recently developed framework designed explicitly for this purpose employing graph attention networks (GATs) to navigate hierarchical classification (HC) complexities. Notably, LeukoGraph stands as a pioneering effort, marking the application of graph neural networks (GNNs) for hierarchical inference on graphs, accommodating up to one million nodes and millions of edges, all derived from flow cytometry data. LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets. The technique is more general than this example. A hallmark achievement of LeukoGraph is its F-score of 98%, significantly outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's prowess extends beyond theoretical innovation, showcasing remarkable precision in predicting both flat and hierarchical cell types across flow cytometry datasets from 30 distinct patients. This precision is further underscored by LeukoGraph's ability to maintain a correct label ratio, despite the inherent challenges posed by hierarchical classifications.
在外周血或骨髓等血液样本的复杂环境中，细胞分类、将不同种群划分为层次结构等工作面临着巨大挑战。本研究介绍了LeukoGraph，它是最近开发的一个框架，专门为此目的而设计，采用图注意网络（GAT）来驾驭分层分类（HC）的复杂性。值得注意的是，LeukoGraph 是一项开创性的工作，它标志着图神经网络（GNN）在图层次推断中的应用，可容纳多达一百万个节点和数百万条边，所有这些都来自流式细胞仪数据。LeukoGraph复杂地处理了一个分类范例，例如，四个不同的细胞群进行平面分类，而第五个细胞群则分化为两个不同的子分支，体现了复杂数据集中固有的细微层次结构。LeukoGraph 的一个标志性成就是它的 F 分数高达 98%，大大超过了目前最先进的方法。最重要的是，LeukoGraph 的优势不仅限于理论创新，它在预测来自 30 位不同患者的流式细胞仪数据集中的扁平和分层细胞类型方面都表现出了非凡的精确性。LeukoGraph 还能保持正确的标记比例，这进一步突出了它的精确性，尽管分层分类本身就存在挑战。
{"title":"Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph","authors":"Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet","doi":"arxiv-2402.18610","DOIUrl":"https://doi.org/arxiv-2402.18610","url":null,"abstract":"In the complex landscape of hematologic samples such as peripheral blood or\u0000bone marrow, cell classification, delineating diverse populations into a\u0000hierarchical structure, presents profound challenges. This study presents\u0000LeukoGraph, a recently developed framework designed explicitly for this purpose\u0000employing graph attention networks (GATs) to navigate hierarchical\u0000classification (HC) complexities. Notably, LeukoGraph stands as a pioneering\u0000effort, marking the application of graph neural networks (GNNs) for\u0000hierarchical inference on graphs, accommodating up to one million nodes and\u0000millions of edges, all derived from flow cytometry data. LeukoGraph intricately\u0000addresses a classification paradigm where for example four different cell\u0000populations undergo flat categorization, while a fifth diverges into two\u0000distinct child branches, exemplifying the nuanced hierarchical structure\u0000inherent in complex datasets. The technique is more general than this example.\u0000A hallmark achievement of LeukoGraph is its F-score of 98%, significantly\u0000outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's\u0000prowess extends beyond theoretical innovation, showcasing remarkable precision\u0000in predicting both flat and hierarchical cell types across flow cytometry\u0000datasets from 30 distinct patients. This precision is further underscored by\u0000LeukoGraph's ability to maintain a correct label ratio, despite the inherent\u0000challenges posed by hierarchical classifications.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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