scholarly journals A Stochastic Model For Protrusion Activity

2018 ◽  
Vol 62 ◽  
pp. 56-67
Author(s):  
Christèle Etchegaray ◽  
Nicolas Meunier
Keyword(s):  
Cell Migration ◽  
Large Scale ◽  
Classical Particle ◽  
Mathematical Framework ◽  
Fundamental Properties ◽  
Structured Population ◽  
Cell Velocity ◽  
Study Population ◽  
Cell Switches ◽  
Cell Motion

In this work we approach cell migration under a large-scale assumption, so that the system reduces to a particle in motion. Unlike classical particle models, the cell displacement results from its internal activity: the cell velocity is a function of the (discrete) protrusive forces exerted by filopodia on the substrate. Cell polarisation ability is modeled in the feedback that the cell motion exerts on the protrusion rates: faster cells form preferentially protrusions in the direction of motion. By using the mathematical framework of structured population processes previously developed to study population dynamics [4], we introduce rigorously the mathematical model and we derive some of its fundamental properties. We perform numerical simulations on this model showing that different types of trajectories may be obtained: Brownian-like, persistent, or intermittent when the cell switches between both previous regimes. We find back the trajectories usually described in the literature for cell migration.

scholarly journals Inferring single-cell behaviour from large-scale epithelial sheet migration patterns

2017 ◽  
Vol 14 (130) ◽  
pp. 20170147 ◽  
Author(s):  
Rachel M. Lee ◽  
Haicen Yue ◽  
Wouter-Jan Rappel ◽  
Wolfgang Losert
Keyword(s):  
Cell Migration ◽  
Large Scale ◽  
Collective Motion ◽  
Individual Cell ◽  
Metastatic Cancer ◽  
Emergent Properties ◽  
Collective Migration ◽  
Cell Behaviour ◽  
Cell Motion ◽  
Insight Into

Cell migration plays an important role in a wide variety of biological processes and can incorporate both individual cell motion and collective behaviour. The emergent properties of collective migration are receiving increasing attention as collective motion's role in diseases such as metastatic cancer becomes clear. Yet, how individual cell behaviour influences large-scale, multi-cell collective motion remains unclear. In this study, we provide insight into the mechanisms behind collective migration by studying cell migration in a spreading monolayer of epithelial MCF10A cells. We quantify migration using particle image velocimetry and find that cell groups have features of motion that span multiple length scales. Comparing our experimental results to a model of collective cell migration, we find that cell migration within the monolayer can be affected in qualitatively different ways by cell motion at the boundary, yet it is not necessary to introduce leader cells at the boundary or specify other large-scale features to recapitulate this large-scale phenotype in simulations. Instead, in our model, collective motion can be enhanced by increasing the overall activity of the cells or by giving the cells a stronger coupling between their motion and polarity. This suggests that investigating the activity and polarity persistence of individual cells will add insight into the collective migration phenotypes observed during development and disease.

scholarly journals Controllable and decomposable multidirectional synchronizations

2021 ◽  
Author(s):  
Gábor Bergmann
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Mathematical Framework ◽  
Collaborative Systems ◽  
Consistency Constraints ◽  
Restoration Mechanism ◽  
Common Goals ◽  
Access Privileges ◽  
Novel Concept ◽  
Engineering Projects

AbstractStudying large-scale collaborative systems engineering projects across teams with differing intellectual property clearances, or healthcare solutions where sensitive patient data needs to be partially shared, or similar multi-user information systems over databases, all boils down to a common mathematical framework. Updateable views (lenses) and more generally bidirectional transformations are abstractions to study the challenge of exchanging information between participants with different read access privileges. The view provided to each participant must be different due to access control or other limitations, yet also consistent in a certain sense, to enable collaboration towards common goals. A collaboration system must apply bidirectional synchronization to ensure that after a participant modifies their view, the views of other participants are updated so that they are consistent again. While bidirectional transformations (synchronizations) have been extensively studied, there are new challenges that are unique to the multidirectional case. If complex consistency constraints have to be maintained, synchronizations that work fine in isolation may not compose well. We demonstrate and characterize a failure mode of the emergent behaviour, where a consistency restoration mechanism undoes the work of other participants. On the other end of the spectrum, we study the case where synchronizations work especially well together: we characterize very well-behaved multidirectional transformations, a non-trivial generalization from the bidirectional case. For the former challenge, we introduce a novel concept of controllability, while for the latter one, we propose a novel formal notion of faithful decomposition. Additionally, the paper proposes several novel properties of multidirectional transformations.

scholarly journals European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michela Bottani ◽  
Aasne K. Aarsand ◽  
Giuseppe Banfi ◽  
Massimo Locatelli ◽  
Abdurrahman Coşkun ◽  
...  
Keyword(s):  
Large Scale ◽  
Thyroid Stimulating Hormone ◽  
Biological Variation ◽  
Healthy Individuals ◽  
Serum Samples ◽  
Free Triiodothyronine ◽  
Performance Specifications ◽  
Study Population ◽  
Variance Homogeneity ◽  
Healthy Participants

Abstract Objectives Thyroid biomarkers are fundamental for the diagnosis of thyroid disorders and for the monitoring and treatment of patients with these diseases. The knowledge of biological variation (BV) is important to define analytical performance specifications (APS) and reference change values (RCV). The aim of this study was to deliver BV estimates for thyroid stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), thyroglobulin (TG), and calcitonin (CT). Methods Analyses were performed on serum samples obtained from the European Biological Variation Study population (91 healthy individuals from six European laboratories; 21–69 years) on the Roche Cobas e801 at the San Raffaele Hospital (Milan, Italy). All samples from each individual were evaluated in duplicate within a single run. The BV estimates with 95% CIs were obtained by CV-ANOVA, after analysis of variance homogeneity and outliers. Results The within-subject (CV I ) BV estimates were for TSH 17.7%, FT3 5.0%, FT4 4.8%, TG 10.3, and CT 13.0%, all significantly lower than those reported in the literature. No significant differences were observed for BV estimates between men and women. Conclusions The availability of updated, in the case of CT not previously published, BV estimates for thyroid markers based on the large scale EuBIVAS study allows for refined APS and associated RCV applicable in the diagnosis and management of thyroid and related diseases.

scholarly journals Hierarchical modeling of mechano-chemical dynamics of epithelial sheets across cells and tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoshifumi Asakura ◽  
Yohei Kondo ◽  
Kazuhiro Aoki ◽  
Honda Naoki
Keyword(s):  
Wound Healing ◽  
Cell Migration ◽  
Continuum Model ◽  
Tissue Level ◽  
Chemical Signals ◽  
Cellular Level ◽  
Mathematical Framework ◽  
Collective Cell Migration ◽  
The Continuum ◽  
Cell Cell

AbstractCollective cell migration is a fundamental process in embryonic development and tissue homeostasis. This is a macroscopic population-level phenomenon that emerges across hierarchy from microscopic cell-cell interactions; however, the underlying mechanism remains unclear. Here, we addressed this issue by focusing on epithelial collective cell migration, driven by the mechanical force regulated by chemical signals of traveling ERK activation waves, observed in wound healing. We propose a hierarchical mathematical framework for understanding how cells are orchestrated through mechanochemical cell-cell interaction. In this framework, we mathematically transformed a particle-based model at the cellular level into a continuum model at the tissue level. The continuum model described relationships between cell migration and mechanochemical variables, namely, ERK activity gradients, cell density, and velocity field, which could be compared with live-cell imaging data. Through numerical simulations, the continuum model recapitulated the ERK wave-induced collective cell migration in wound healing. We also numerically confirmed a consistency between these two models. Thus, our hierarchical approach offers a new theoretical platform to reveal a causality between macroscopic tissue-level and microscopic cellular-level phenomena. Furthermore, our model is also capable of deriving a theoretical insight on both of mechanical and chemical signals, in the causality of tissue and cellular dynamics.

scholarly journals Cell migration guided by long-lived spatial memory

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph d’Alessandro ◽  
Alex Barbier--Chebbah ◽  
Victor Cellerin ◽  
Olivier Benichou ◽  
René Marc Mège ◽  
...  
Keyword(s):  
Cell Migration ◽  
Spatial Memory ◽  
Large Scale ◽  
Single Cells ◽  
Environmental Perturbations ◽  
Motile Cells ◽  
Unicellular Organisms ◽  
Biochemical Signals ◽  
The Impact ◽  
Cell Trajectories

AbstractLiving cells actively migrate in their environment to perform key biological functions—from unicellular organisms looking for food to single cells such as fibroblasts, leukocytes or cancer cells that can shape, patrol or invade tissues. Cell migration results from complex intracellular processes that enable cell self-propulsion, and has been shown to also integrate various chemical or physical extracellular signals. While it is established that cells can modify their environment by depositing biochemical signals or mechanically remodelling the extracellular matrix, the impact of such self-induced environmental perturbations on cell trajectories at various scales remains unexplored. Here, we show that cells can retrieve their path: by confining motile cells on 1D and 2D micropatterned surfaces, we demonstrate that they leave long-lived physicochemical footprints along their way, which determine their future path. On this basis, we argue that cell trajectories belong to the general class of self-interacting random walks, and show that self-interactions can rule large scale exploration by inducing long-lived ageing, subdiffusion and anomalous first-passage statistics. Altogether, our joint experimental and theoretical approach points to a generic coupling between motile cells and their environment, which endows cells with a spatial memory of their path and can dramatically change their space exploration.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

scholarly journals Cell migration driven by long-lived spatial memory

2021 ◽  
Author(s):  
Joseph d’Alessandro ◽  
Alex Barbier-Chebbah ◽  
Victor Cellerin ◽  
Olivier Bénichou ◽  
René-Marc Mège ◽  
...  
Keyword(s):  
Cell Migration ◽  
Spatial Memory ◽  
Large Scale ◽  
Single Cells ◽  
Environmental Perturbations ◽  
Motile Cells ◽  
Unicellular Organisms ◽  
Biochemical Signals ◽  
The Impact ◽  
Cell Trajectories

Many living cells actively migrate in their environment to perform key biological functions – from unicellular organisms looking for food to single cells such as fibroblasts, leukocytes or cancer cells that can shape, patrol or invade tissues. Cell migration results from complex intracellular processes that enable cell self-propulsion 1,2, and has been shown to also integrate various chemical or physical extracellular signals 3,4,5. While it is established that cells can modify their environment by depositing biochemical signals or mechanically remodeling the extracellular matrix, the impact of such self-induced environmental perturbations on cell trajectories at various scales remains unexplored. Here, we show that cells remember their path: by confining cells on 1D and 2D micropatterned surfaces, we demonstrate that motile cells leave long-lived physicochemical footprints along their way, which determine their future path. On this basis, we argue that cell trajectories belong to the general class of self-interacting random walks, and show that self-interactions can rule large scale exploration by inducing long-lived ageing, subdiffusion and anomalous first-passage statistics. Altogether, our joint experimental and theoretical approach points to a generic coupling between motile cells and their environment, which endows cells with a spatial memory of their path and can dramatically change their space exploration.

scholarly journals Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways

2020 ◽  
Vol 295 (50) ◽  
pp. 16906-16919
Author(s):  
Jae-Hong Kim ◽  
Yeojin Seo ◽  
Myungjin Jo ◽  
Hyejin Jeon ◽  
Young-Seop Kim ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Cell Migration ◽  
Intracellular Signaling ◽  
Mammalian Cells ◽  
Drug Targets ◽  
Large Scale ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Signal Transduction Pathways ◽  
Global View

Kinases are critical components of intracellular signaling pathways and have been extensively investigated with regard to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid Saccharomyces cerevisiae yeast deletion mutants identified ∼400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4, KIF11, mTOR, PKM2, SGPP1, TTK, and YWHAE regulate PAK1-induced cell migration and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1-TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.

scholarly journals Technology in the advancement of glaciology

1987 ◽  
Vol 33 (S1) ◽  
pp. 66-77 ◽  
Author(s):  
H. Jay Zwally
Keyword(s):  
Large Scale ◽  
New Technology ◽  
Ice Cores ◽  
Data Logging ◽  
Mass Spectrometers ◽  
Automatic Data ◽  
Fundamental Properties ◽  
Large Memory ◽  
Satellite Sensors ◽  
Evolution Of Science

AbstractMany of the major advances in glaciology during the past 50 years have followed the development and application of new technology for viewing and measuring various characteristics of ice. Microscopes to study ice crystals, radars to probe the internal structure of large ice masses, mass spectrometers to analyze the atomic composition of ice cores, and satellite sensors to measure the global distribution of ice are some of the tools readily adapted by glaciologists. Today, new tools include microcomputers for automatic data logging, large-memory computers for data processing and numerical modeling, sensitive instruments for ice analysis, and satellite sensors for large-scale ice observations. In the future, continued advances in key technologies will help guide the evolution of science questions considered by glaciologists, expanding our view of ice, its fundamental properties, its interactions within the ice–ocean–land–atmosphere system, and its role in the evolution of our global environment.

scholarly journals A dynamical systems view of network centrality

2014 ◽  
Vol 470 (2165) ◽  
pp. 20130835 ◽  
Author(s):  
Peter Grindrod ◽  
Desmond J. Higham
Keyword(s):  
Dynamical Systems ◽  
Large Scale ◽  
Dynamic Networks ◽  
Network Evolution ◽  
Mathematical Framework ◽  
The Novel ◽  
Matrix Logarithm ◽  
The Matrix ◽  
Systems View ◽  
Logarithm Function

To gain insights about dynamic networks, the dominant paradigm is to study discrete snapshots , or timeslices , as the interactions evolve. Here, we develop and test a new mathematical framework where network evolution is handled over continuous time, giving an elegant dynamical systems representation for the important concept of node centrality. The resulting system allows us to track the relative influence of each individual. This new setting is natural in many digital applications, offering both conceptual and computational advantages. The novel differential equations approach is convenient for modelling and analysis of network evolution and gives rise to an interesting application of the matrix logarithm function. From a computational perspective, it avoids the awkward up-front compromises between accuracy, efficiency and redundancy required in the prevalent discrete-time setting. Instead, we can rely on state-of-the-art ODE software, where discretization takes place adaptively in response to the prevailing system dynamics. The new centrality system generalizes the widely used Katz measure, and allows us to identify and track, at any resolution, the most influential nodes in terms of broadcasting and receiving information through time-dependent links. In addition to the classical static network notion of attenuation across edges, the new ODE also allows for attenuation over time, as information becomes stale. This allows ‘running measures’ to be computed, so that networks can be monitored in real time over arbitrarily long intervals. With regard to computational efficiency, we explain why it is cheaper to track good receivers of information than good broadcasters. An important consequence is that the overall broadcast activity in the network can also be monitored efficiently. We use two synthetic examples to validate the relevance of the new measures. We then illustrate the ideas on a large-scale voice call network, where key features are discovered that are not evident from snapshots or aggregates.

Sign in / Sign up

Export Citation Format

Share Document