arXiv - QuanBio - Cell Behavior最新文献_第9页

Using systemic modeling and Bayesian calibration to investigate the role of the tumor microenvironment on chemoresistance 利用系统建模和贝叶斯校准研究肿瘤微环境在化疗耐药中的作用

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-30 DOI: arxiv-2310.19688

Sabrina Schönfeld, Laura Scarabosio, Alican Ozkan, Marissa Nichole Rylander, Christina Kuttler

引用次数: 0

A Microwell-Based Microfluidic Device for Single-Cell Trapping and Magnetic Field Gradient Stimulation 基于微孔的单细胞捕获和磁场梯度刺激微流控装置

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-19 DOI: arxiv-2310.12829

Richard Lee Lai

引用次数: 0

Fibration symmetry uncovers minimal regulatory networks for logical computation in bacteria 纤颤对称揭示了细菌中逻辑计算的最小调节网络

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-17 DOI: arxiv-2310.10895

Luis A. Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A. Makse

{"title":"Fibration symmetry uncovers minimal regulatory networks for logical computation in bacteria","authors":"Luis A. Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A. Makse","doi":"arxiv-2310.10895","DOIUrl":"https://doi.org/arxiv-2310.10895","url":null,"abstract":"Symmetry principles have proven important in physics, deep learning and\u0000geometry, allowing for the reduction of complicated systems to simpler, more\u0000comprehensible models that preserve the system's features of interest.\u0000Biological systems often show a high level of complexity and consist of a high\u0000number of interacting parts. Using symmetry fibrations, the relevant symmetries\u0000for biological 'message-passing' networks, we reduced the gene regulatory\u0000networks of E. coli and B. subtilis bacteria in a way that preserves\u0000information flow and highlights the computational capabilities of the network.\u0000Nodes that share isomorphic input trees are grouped into equivalence classes\u0000called fibers, whereby genes that receive signals with the same 'history'\u0000belong to one fiber and synchronize. We further reduce the networks to its\u0000computational core by removing 'dangling ends' via k-core decomposition. The\u0000computational core of the network consists of a few strongly connected\u0000components in which signals can cycle while signals are transmitted between\u0000these 'information vortices' in a linear feed-forward manner. These components\u0000are in charge of decision making in the bacterial cell by employing a series of\u0000genetic toggle-switch circuits that store memory, and oscillator circuits.\u0000These circuits act as the central computation machine of the network, whose\u0000output signals then spread to the rest of the network.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522681","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}

引用次数: 0

Answering open questions in biology using spatial genomics and structured methods 利用空间基因组学和结构化方法回答生物学中的开放性问题

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-14 DOI: arxiv-2310.09482

Siddhartha G Jena, Archit Verma, Barbara E Engelhardt

{"title":"Answering open questions in biology using spatial genomics and structured methods","authors":"Siddhartha G Jena, Archit Verma, Barbara E Engelhardt","doi":"arxiv-2310.09482","DOIUrl":"https://doi.org/arxiv-2310.09482","url":null,"abstract":"Genomics methods have uncovered patterns in a range of biological systems,\u0000but obscure important aspects of cell behavior: the shape, relative locations\u0000of, movement of, and interactions between cells in space. Spatial technologies\u0000that collect genomic or epigenomic data while preserving spatial information\u0000have begun to overcome these limitations. These new data promise a deeper\u0000understanding of the factors that affect cellular behavior, and in particular\u0000the ability to directly test existing theories about cell state and variation\u0000in the context of morphology, location, motility, and signaling that could not\u0000be tested before. Rapid advancements in resolution, ease-of-use, and scale of\u0000spatial genomics technologies to address these questions also require an\u0000updated toolkit of statistical methods with which to interrogate these data. We\u0000present four open biological questions that can now be answered using spatial\u0000genomics data paired with methods for analysis. We outline spatial data\u0000modalities for each that may yield specific insight, discuss how conflicting\u0000theories may be tested by comparing the data to conceptual models of biological\u0000behavior, and highlight statistical and machine learning-based tools that may\u0000prove particularly helpful to recover biological insight.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523149","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}

引用次数: 0

Physical phase field model for phagocytosis 吞噬作用的物理相场模型

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-12 DOI: arxiv-2310.08321

Benjamin Winkler, Mohammad Abu Hamed, Alexander A. Nepomnyashchy, Falko Ziebert

引用次数: 0

Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion 尖锋集体细胞迁移和入侵的离散和连续数学模型

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-11 DOI: arxiv-2310.07938

Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli

{"title":"Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion","authors":"Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli","doi":"arxiv-2310.07938","DOIUrl":"https://doi.org/arxiv-2310.07938","url":null,"abstract":"Mathematical models describing the spatial spreading and invasion of\u0000populations of biological cells are often developed in a continuum modelling\u0000framework using reaction-diffusion equations. While continuum models based on\u0000linear diffusion are routinely employed and known to capture key experimental\u0000observations, linear diffusion fails to predict well-defined sharp fronts that\u0000are often observed experimentally. This observation has motivated the use of\u0000nonlinear degenerate diffusion, however these nonlinear models and the\u0000associated parameters lack a clear biological motivation and interpretation.\u0000Here we take a different approach by developing a stochastic discrete\u0000lattice-based model incorporating biologically-inspired mechanisms and then\u0000deriving the reaction-diffusion continuum limit. Inspired by experimental\u0000observations, agents in the simulation deposit extracellular material, that we\u0000call a substrate, locally onto the lattice, and the motility of agents is taken\u0000to be proportional to the substrate density. Discrete simulations that mimic a\u0000two--dimensional circular barrier assay illustrate how the discrete model\u0000supports both smooth and sharp-fronted density profiles depending on the rate\u0000of substrate deposition. Coarse-graining the discrete model leads to a novel\u0000partial differential equation (PDE) model whose solution accurately\u0000approximates averaged data from the discrete model. The new discrete model and\u0000PDE approximation provides a simple, biologically motivated framework for\u0000modelling the spreading, growth and invasion of cell populations with\u0000well-defined sharp fronts","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"10 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522687","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}

引用次数: 0

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening 临床前药物筛选中药物反应预测的零学习

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-05 DOI: arxiv-2310.12996

Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu, Bo Du

{"title":"Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening","authors":"Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu, Bo Du","doi":"arxiv-2310.12996","DOIUrl":"https://doi.org/arxiv-2310.12996","url":null,"abstract":"Conventional deep learning methods typically employ supervised learning for\u0000drug response prediction (DRP). This entails dependence on labeled response\u0000data from drugs for model training. However, practical applications in the\u0000preclinical drug screening phase demand that DRP models predict responses for\u0000novel compounds, often with unknown drug responses. This presents a challenge,\u0000rendering supervised deep learning methods unsuitable for such scenarios. In\u0000this paper, we propose a zero-shot learning solution for the DRP task in\u0000preclinical drug screening. Specifically, we propose a Multi-branch\u0000Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can\u0000be seamlessly integrated with conventional DRP methods, learning invariant\u0000features from the prior response data of similar drugs to enhance real-time\u0000predictions of unlabeled compounds. We conducted experiments using the GDSCv2\u0000and CellMiner datasets. The results demonstrate that MSDA efficiently predicts\u0000drug responses for novel compounds, leading to a general performance\u0000improvement of 5-10% in the preclinical drug screening phase. The significance\u0000of this solution resides in its potential to accelerate the drug discovery\u0000process, improve drug candidate assessment, and facilitate the success of drug\u0000discovery.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523150","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}

引用次数: 0

A biased random walk approach for modeling the collective chemotaxis of neural crest cells 一种模拟神经嵴细胞集体趋化的有偏随机漫步方法

arXiv - QuanBio - Cell Behavior Pub Date : 2023-10-02 DOI: arxiv-2310.01294

Viktoria Freingruber, Kevin J. Painter, Mariya Ptashnyk, Linus Schumacher

引用次数: 0

Tradeoffs in concentration sensing in dynamic environments 动态环境中浓度传感的权衡

arXiv - QuanBio - Cell Behavior Pub Date : 2023-09-29 DOI: arxiv-2310.00062

Aparajita Kashyap, Wei Wang, Brian A. Camley

{"title":"Tradeoffs in concentration sensing in dynamic environments","authors":"Aparajita Kashyap, Wei Wang, Brian A. Camley","doi":"arxiv-2310.00062","DOIUrl":"https://doi.org/arxiv-2310.00062","url":null,"abstract":"When cells measure concentrations of chemical signals, they may average\u0000multiple measurements over time in order to reduce noise in their measurements.\u0000However, when cells are in a environment that changes over time, past\u0000measurements may not reflect current conditions - creating a new source of\u0000error that trades off against noise in chemical sensing. What statistics in the\u0000cell's environment control this tradeoff? What properties of the environment\u0000make it variable enough that this tradeoff is relevant? We model a single\u0000eukaryotic cell sensing a chemical secreted from bacteria (e.g. folic acid). In\u0000this case, the environment changes because the bacteria swim - leading to\u0000changes in the true concentration at the cell. We develop analytical\u0000calculations and stochastic simulations of sensing in this environment. We find\u0000that cells can have a huge variety of optimal sensing strategies, ranging from\u0000not time averaging at all, to averaging over an arbitrarily long time, or\u0000having a finite optimal averaging time. The factors that primarily control the\u0000ideal averaging are the ratio of sensing noise to environmental variation, and\u0000the ratio of timescales of sensing to the timescale of environmental variation.\u0000Sensing noise depends on the receptor-ligand kinetics, while the environmental\u0000variation depends on the density of bacteria and the degradation and diffusion\u0000properties of the secreted chemoattractant. Our results suggest that\u0000fluctuating environmental concentrations may be a relevant source of noise even\u0000in a relatively static environment.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522689","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}

引用次数: 0

Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images 低维细胞形状空间的坐标区分了单个静态细胞图像的动态迁移

arXiv - QuanBio - Cell Behavior Pub Date : 2023-09-28 DOI: arxiv-2309.16498

Xiuxiu He, Kuangcai Chen, Ning Fang, Yi Jiang

引用次数: 0