Sabrina Schönfeld, Laura Scarabosio, Alican Ozkan, Marissa Nichole Rylander, Christina Kuttler
Using a novel modeling approach based on the so-called environmental stress level (ESL), we develop a mathematical model to describe systematically the collective influence of oxygen concentration and stiffness of the extracellular matrix on the response of tumor cells to a combined chemotherapeutic treatment. We perform Bayesian calibrations of the resulting model using particle filters, with in vitro experimental data for different hepatocellular carcinoma cell lines. The calibration results support the validity of our mathematical model. Furthermore, they shed light on individual as well as synergistic effects of hypoxia and tissue stiffness on tumor cell dynamics under chemotherapy.
{"title":"Using systemic modeling and Bayesian calibration to investigate the role of the tumor microenvironment on chemoresistance","authors":"Sabrina Schönfeld, Laura Scarabosio, Alican Ozkan, Marissa Nichole Rylander, Christina Kuttler","doi":"arxiv-2310.19688","DOIUrl":"https://doi.org/arxiv-2310.19688","url":null,"abstract":"Using a novel modeling approach based on the so-called environmental stress\u0000level (ESL), we develop a mathematical model to describe systematically the\u0000collective influence of oxygen concentration and stiffness of the extracellular\u0000matrix on the response of tumor cells to a combined chemotherapeutic treatment.\u0000We perform Bayesian calibrations of the resulting model using particle filters,\u0000with in vitro experimental data for different hepatocellular carcinoma cell\u0000lines. The calibration results support the validity of our mathematical model.\u0000Furthermore, they shed light on individual as well as synergistic effects of\u0000hypoxia and tissue stiffness on tumor cell dynamics under chemotherapy.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522686","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 develop a microfluidic platform for the long-term cultivation and observation of both THP-1 cells under different physiological conditions. First, we determine optimal seeding conditions and microwell geometry. Next, we observe changes in cell size and circularity. Results show that gradient magnetic forces on the order of 102 T/m results in stunted growth and irregular cell shapes. Finally, we observe the temporal change in ROS signals under control, static and gradient magnetic fields. For exposure to static and gradient magnetic fields, the peak in ROS signals occurs after 24 hours and 36 hours, respectively.
{"title":"A Microwell-Based Microfluidic Device for Single-Cell Trapping and Magnetic Field Gradient Stimulation","authors":"Richard Lee Lai","doi":"arxiv-2310.12829","DOIUrl":"https://doi.org/arxiv-2310.12829","url":null,"abstract":"We develop a microfluidic platform for the long-term cultivation and\u0000observation of both THP-1 cells under different physiological conditions.\u0000First, we determine optimal seeding conditions and microwell geometry. Next, we\u0000observe changes in cell size and circularity. Results show that gradient\u0000magnetic forces on the order of 102 T/m results in stunted growth and irregular\u0000cell shapes. Finally, we observe the temporal change in ROS signals under\u0000control, static and gradient magnetic fields. For exposure to static and\u0000gradient magnetic fields, the peak in ROS signals occurs after 24 hours and 36\u0000hours, respectively.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"106 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522690","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}
Luis A. Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A. Makse
Symmetry principles have proven important in physics, deep learning and geometry, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. Biological systems often show a high level of complexity and consist of a high number of interacting parts. Using symmetry fibrations, the relevant symmetries for biological 'message-passing' networks, we reduced the gene regulatory networks of E. coli and B. subtilis bacteria in a way that preserves information flow and highlights the computational capabilities of the network. Nodes that share isomorphic input trees are grouped into equivalence classes called fibers, whereby genes that receive signals with the same 'history' belong to one fiber and synchronize. We further reduce the networks to its computational core by removing 'dangling ends' via k-core decomposition. The computational core of the network consists of a few strongly connected components in which signals can cycle while signals are transmitted between these 'information vortices' in a linear feed-forward manner. These components are in charge of decision making in the bacterial cell by employing a series of genetic toggle-switch circuits that store memory, and oscillator circuits. These circuits act as the central computation machine of the network, whose output signals then spread to the rest of the network.
{"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}
Siddhartha G Jena, Archit Verma, Barbara E Engelhardt
Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shape, relative locations of, movement of, and interactions between cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each that may yield specific insight, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological insight.
{"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}
Benjamin Winkler, Mohammad Abu Hamed, Alexander A. Nepomnyashchy, Falko Ziebert
We propose and study a simple, physical model for phagocytosis, i.e. the active, actin-mediated uptake of micron-sized particles by biological cells. The cell is described by the phase field method and the driving mechanisms of uptake are actin ratcheting, modeled by a dynamic vector field, as well as cell-particle adhesion due to receptor-ligand binding. We first test the modeling framework for the symmetric situation of a spherical cell engulfing a fixed spherical particle. We then exemplify its versatility by studying various asymmetric situations like different particle shapes and orientations, as well as the simultaneous uptake of two particles. In addition, we perform a perturbation theory of a slightly modified model version in the symmetric setting, allowing to derive a reduced model, shedding light on the effective driving forces and being easier to solve. This work is meant as a first step in describing phagocytosis and we discuss several effects that are amenable to future modeling within the same framework.
{"title":"Physical phase field model for phagocytosis","authors":"Benjamin Winkler, Mohammad Abu Hamed, Alexander A. Nepomnyashchy, Falko Ziebert","doi":"arxiv-2310.08321","DOIUrl":"https://doi.org/arxiv-2310.08321","url":null,"abstract":"We propose and study a simple, physical model for phagocytosis, i.e. the\u0000active, actin-mediated uptake of micron-sized particles by biological cells.\u0000The cell is described by the phase field method and the driving mechanisms of\u0000uptake are actin ratcheting, modeled by a dynamic vector field, as well as\u0000cell-particle adhesion due to receptor-ligand binding. We first test the\u0000modeling framework for the symmetric situation of a spherical cell engulfing a\u0000fixed spherical particle. We then exemplify its versatility by studying various\u0000asymmetric situations like different particle shapes and orientations, as well\u0000as the simultaneous uptake of two particles. In addition, we perform a\u0000perturbation theory of a slightly modified model version in the symmetric\u0000setting, allowing to derive a reduced model, shedding light on the effective\u0000driving forces and being easier to solve. This work is meant as a first step in\u0000describing phagocytosis and we discuss several effects that are amenable to\u0000future modeling within the same framework.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522685","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}
Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli
Mathematical models describing the spatial spreading and invasion of populations of biological cells are often developed in a continuum modelling framework using reaction-diffusion equations. While continuum models based on linear diffusion are routinely employed and known to capture key experimental observations, linear diffusion fails to predict well-defined sharp fronts that are often observed experimentally. This observation has motivated the use of nonlinear degenerate diffusion, however these nonlinear models and the associated parameters lack a clear biological motivation and interpretation. Here we take a different approach by developing a stochastic discrete lattice-based model incorporating biologically-inspired mechanisms and then deriving the reaction-diffusion continuum limit. Inspired by experimental observations, agents in the simulation deposit extracellular material, that we call a substrate, locally onto the lattice, and the motility of agents is taken to be proportional to the substrate density. Discrete simulations that mimic a two--dimensional circular barrier assay illustrate how the discrete model supports both smooth and sharp-fronted density profiles depending on the rate of substrate deposition. Coarse-graining the discrete model leads to a novel partial differential equation (PDE) model whose solution accurately approximates averaged data from the discrete model. The new discrete model and PDE approximation provides a simple, biologically motivated framework for modelling the spreading, growth and invasion of cell populations with well-defined sharp fronts
{"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}
Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. We conducted experiments using the GDSCv2 and CellMiner datasets. The results demonstrate that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery.
{"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}
Viktoria Freingruber, Kevin J. Painter, Mariya Ptashnyk, Linus Schumacher
Collective cell migration is a multicellular phenomenon that arises in various biological contexts, including cancer and embryo development. "Collectiveness" can be promoted by cell-cell interactions such as co-attraction and contact inhibition of locomotion. These mechanisms act on cell polarity, pivotal for directed cell motility, through influencing the intracellular dynamics of small GTPases such as Rac1. To model these dynamics we introduce a biased random walk model, where the bias depends on the internal state of Rac1, and the Rac1 state is influenced by cell-cell interactions and chemoattractive cues. In an extensive simulation study we demonstrate and explain the scope and applicability of the introduced model in various scenarios. The use of a biased random walk model allows for the derivation of a corresponding partial differential equation for the cell density while still maintaining a certain level of intracellular detail from the individual based setting.
{"title":"A biased random walk approach for modeling the collective chemotaxis of neural crest cells","authors":"Viktoria Freingruber, Kevin J. Painter, Mariya Ptashnyk, Linus Schumacher","doi":"arxiv-2310.01294","DOIUrl":"https://doi.org/arxiv-2310.01294","url":null,"abstract":"Collective cell migration is a multicellular phenomenon that arises in\u0000various biological contexts, including cancer and embryo development.\u0000\"Collectiveness\" can be promoted by cell-cell interactions such as\u0000co-attraction and contact inhibition of locomotion. These mechanisms act on\u0000cell polarity, pivotal for directed cell motility, through influencing the\u0000intracellular dynamics of small GTPases such as Rac1. To model these dynamics\u0000we introduce a biased random walk model, where the bias depends on the internal\u0000state of Rac1, and the Rac1 state is influenced by cell-cell interactions and\u0000chemoattractive cues. In an extensive simulation study we demonstrate and\u0000explain the scope and applicability of the introduced model in various\u0000scenarios. The use of a biased random walk model allows for the derivation of a\u0000corresponding partial differential equation for the cell density while still\u0000maintaining a certain level of intracellular detail from the individual based\u0000setting.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"112 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522688","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}
When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in a environment that changes over time, past measurements may not reflect current conditions - creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell's environment control this tradeoff? What properties of the environment make it variable enough that this tradeoff is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g. folic acid). In this case, the environment changes because the bacteria swim - leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies, ranging from not time averaging at all, to averaging over an arbitrarily long time, or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation, and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while the environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.
{"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}
Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior.
{"title":"Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images","authors":"Xiuxiu He, Kuangcai Chen, Ning Fang, Yi Jiang","doi":"arxiv-2309.16498","DOIUrl":"https://doi.org/arxiv-2309.16498","url":null,"abstract":"Cell shape has long been used to discern cell phenotypes and states, but the\u0000underlying premise has not been quantitatively tested. Here, we show that a\u0000single cell image can be used to discriminate its migration behavior by\u0000analyzing a large number of cell migration data in vitro. We analyzed a large\u0000number of two-dimensional cell migration images over time and found that the\u0000cell shape variation space has only six dimensions, and migration behavior can\u0000be determined by the coordinates of a single cell image in this 6-dimensional\u0000shape-space. We further show that this is possible because persistent cell\u0000migration is characterized by spatial-temporally coordinated protrusion and\u0000contraction, and a distribution signature in the shape-space. Our findings\u0000provide a quantitative underpinning for using cell morphology to differentiate\u0000cell dynamical behavior.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"96 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523138","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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