scholarly journals Origins of cell-to-cell variability, kinetic proof-reading and the robustness of MAPK signal transduction

2015 ◽  
Author(s):  
Sarah Filippi ◽  
Chris Barnes ◽  
Paul Kirk ◽  
Takamasa Kudo ◽  
Siobhan McMahon ◽  
...  
Keyword(s):  
Image Cytometry ◽  
Population Level ◽  
Statistical Modelling ◽  
Population Heterogeneity ◽  
Dominant Factor ◽  
Cellular Signalling ◽  
Modelling Framework ◽  
Erk Phosphorylation ◽  
Proof Reading ◽  
Cell To Cell Variability

Cellular signalling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signalling systems remain poorly understood. Here we measure the temporal evolution of phosphorylated MEK and ERK dynamics across populations of cells and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modelling framework to show that upstream noise is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. In particular, the cell-to-cell variability during sustained phosphorylation stems from random fluctuations in the background upstream signalling processes, while during transient phosphorylation, the heterogeneity is primarily due to noise in the intensity of the upstream signal(s). We show that the core MEK/ERK system uses kinetic proof-reading to faithfully and robustly transmits these variable inputs. The MAPK cascade thus propagates cell-to-cell variability at the population level, rather than attenuating or increasing it.

scholarly journals The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Oecologia ◽  
2021 ◽  
Author(s):  
Peng He ◽  
Pierre-Olivier Montiglio ◽  
Marius Somveille ◽  
Mauricio Cantor ◽  
Damien R. Farine
Keyword(s):  
Population Level ◽  
R Package ◽  
Habitat Configuration ◽  
Modelling Framework ◽  
Alternative Habitat ◽  
Animal Populations ◽  
Research Questions ◽  
Habitat Networks ◽  
Animal Habitat

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

scholarly journals Digital signaling decouples activation probability and population heterogeneity

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Ryan A Kellogg ◽  
Chengzhe Tian ◽  
Tomasz Lipniacki ◽  
Stephen R Quake ◽  
Savaş Tay
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Activation ◽  
Population Heterogeneity ◽  
Cellular Phenotype ◽  
Noisy Environments ◽  
Gene Expression Phenotype ◽  
Weak Stimulation ◽  
Cell To Cell Variability ◽  
Activation Probability

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

scholarly journals Disentangling the behavioural variability of confined cell migration

2020 ◽  
Vol 17 (163) ◽  
pp. 20190689 ◽  
Author(s):  
David B. Brückner ◽  
Alexandra Fink ◽  
Joachim O. Rädler ◽  
Chase P. Broedersz
Keyword(s):  
Cell Migration ◽  
Dynamical Systems ◽  
Stochastic Dynamics ◽  
Systems Approach ◽  
Time Windows ◽  
Population Heterogeneity ◽  
Biological Processes ◽  
Long Time ◽  
Cell To Cell Variability ◽  
Additional Variance

Cell-to-cell variability is inherent to numerous biological processes, including cell migration. Quantifying and characterizing the variability of migrating cells is challenging, as it requires monitoring many cells for long time windows under identical conditions. Here, we observe the migration of single human breast cancer cells (MDA-MB-231) in confining two-state micropatterns. To describe the stochastic dynamics of this confined migration, we employ a dynamical systems approach. We identify statistics to measure the behavioural variance of the migration, which significantly exceeds that predicted by a population-averaged stochastic model. This additional variance can be explained by the combination of an ‘ageing’ process and population heterogeneity. To quantify population heterogeneity, we decompose the cells into subpopulations of slow and fast cells, revealing the presence of distinct classes of dynamical systems describing the migration, ranging from bistable to limit cycle behaviour. Our findings highlight the breadth of migration behaviours present in cell populations.

scholarly journals Social media captures demographic and regional physical activity

2019 ◽  
Vol 5 (1) ◽  
pp. e000567 ◽  
Author(s):  
Nina Cesare ◽  
Quynh C Nguyen ◽  
Christan Grant ◽  
Elaine O Nsoesie
Keyword(s):  
Physical Activity ◽  
Social Media ◽  
Physical Inactivity ◽  
Specific Activity ◽  
Activity Patterns ◽  
Health Behaviours ◽  
Population Level ◽  
Statistical Modelling ◽  
Men And Women ◽  
The Usa

ObjectivesWe examined the use of data from social media for surveillance of physical activity prevalence in the USA.MethodsWe obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention.ResultsThe association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes.ConclusionsThe regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

scholarly journals Data-driven modelling of mutational hotspots and in silico predictors in hypertrophic cardiomyopathy

2020 ◽  
pp. jmedgenet-2020-106922
Author(s):  
Adam Waring ◽  
Andrew Harper ◽  
Silvia Salatino ◽  
Christopher Kramer ◽  
Stefan Neubauer ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
In Silico ◽  
Synthetic Data ◽  
Missense Variant ◽  
Statistical Modelling ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Modelling Framework ◽  
Mutational Hotspots ◽  
Anderson Darling

BackgroundAlthough rare missense variants in Mendelian disease genes often cluster in specific regions of proteins, it is unclear how to consider this when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene association and variant interpretation that use this powerful signal.MethodsWe present statistical methods to detect missense variant clustering (BIN-test) combined with burden information (ClusterBurden). We introduce a flexible generalised additive modelling (GAM) framework to identify mutational hotspots using burden and clustering information (hotspot model) and supplemented by in silico predictors (hotspot+ model). The methods were applied to synthetic data and a case–control dataset, comprising 5338 hypertrophic cardiomyopathy patients and 125 748 population reference samples over 34 putative cardiomyopathy genes.ResultsIn simulations, the BIN-test was almost twice as powerful as the Anderson-Darling or Kolmogorov-Smirnov tests; ClusterBurden was computationally faster and more powerful than alternative position-informed methods. For 6/8 sarcomeric genes with strong clustering, Clusterburden showed enhanced power over burden-alone, equivalent to increasing the sample size by 50%. Hotspot+ models that combine burden, clustering and in silico predictors outperform generic pathogenicity predictors and effectively integrate ACMG criteria PM1 and PP3 to yield strong or moderate evidence of pathogenicity for 31.8% of examined variants of uncertain significance.ConclusionGAMs represent a unified statistical modelling framework to combine burden, clustering and functional information. Hotspot models can refine maps of regional burden and hotspot+ models can be powerful predictors of variant pathogenicity. The BIN-test is a fast powerful approach to detect missense variant clustering that when combined with burden information (ClusterBurden) may enhance disease-gene discovery.

scholarly journals Simultaneous tracking of division and differentiation from individual hematopoietic stem and progenitor cells reveals within-family homogeneity despite population heterogeneity

2019 ◽  
Author(s):  
Tamar Tak ◽  
Giulio Prevedello ◽  
Gaël Simon ◽  
Noémie Paillon ◽  
Ken R. Duffy ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
High Throughput ◽  
Common Ancestor ◽  
Single Cells ◽  
Population Level ◽  
Cell Types ◽  
Culture Conditions ◽  
Population Heterogeneity ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells

AbstractThe advent of high throughput single cell methods such as scRNA-seq has uncovered substantial heterogeneity in the pool of hematopoietic stem and progenitor cells (HSPCs). A significant issue is how to reconcile those findings with the standard model of hematopoietic development, and a fundamental question is how much instruction is inherited by offspring from their ancestors. To address this, we further developed a high-throughput method that enables simultaneously determination of common ancestor, generation, and differentiation status of a large collection of single cells. Data from it revealed that while there is substantial population-level heterogeneity, cells that derived from a common ancestor were highly concordant in their division progression and share similar differentiation outcomes, revealing significant familial effects on both division and differentiation. Although each family diversifies to some extent, the overall collection of cell types observed in a population is largely composed of homogeneous families from heterogeneous ancestors. Heterogeneity between families could be explained, in part, by differences in ancestral expression of cell-surface markers that are used for phenotypic HSPC identification: CD48, SCA-1, c-kit and Flt3. These data call for a revision of the fundamental model of haematopoiesis from a single tree to an ensemble of trees from distinct ancestors where common ancestor effect must be considered. As HSPCs are cultured in the clinic before bone marrow transplantation, our results suggest that the broad range of engraftment and proliferation capacities of HSPCs could be consequences of the heterogeneity in their engrafted families, and altered culture conditions might reduce heterogeneity between families, possibly improving transplantation outcomes.

scholarly journals Topology and habitat assortativity drive neutral and adaptive diversification in spatial graphs

2021 ◽  
Author(s):  
Victor Boussange ◽  
Loic Pellissier
Keyword(s):  
Habitat Heterogeneity ◽  
Landscape Connectivity ◽  
Spatial Dynamics ◽  
Evolutionary Model ◽  
Population Level ◽  
Landscape Features ◽  
Adaptive Diversification ◽  
Modelling Framework ◽  
Spatial Graphs ◽  
Connectivity Patterns

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Notably, landscape connectivity and habitat heterogeneity constrain the movement and survival of individuals, thereby promoting differentiation through drift and local adaptation. Nevertheless, the complexity of landscape features can blur our understanding of how they drive differentiation. Here, we formulate a stochastic, eco-evolutionary model where individuals are structured over a graph that captures complex connectivity patterns and accounts for habitat heterogeneity. Individuals possess neutral and adaptive traits, whose divergence results in differentiation at the population level. The modelling framework enables an analytical underpinning of emerging macroscopic properties, which we complement with numerical simulations to investigate how the graph topology and the spatial habitat distribution affect differentiation. We show that in the absence of selection, graphs with high characteristic length and high heterogeneity in degree promote neutral differentiation. Habitat assortativity, a metric that captures habitat spatial auto-correlation in graphs, additionally drives differentiation patterns under habitat-dependent selection. While assortativity systematically amplifies adaptive differentiation, it can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations in complex landscapes, our study establishes the link between landscape features and the emergence of diversification, contributing to a fundamental understanding of the origin of biodiversity gradients.

scholarly journals Identifying cell-to-cell variability in internalisation using flow cytometry

2021 ◽  
Author(s):  
Alexander P Browning ◽  
Niloufar Ansari ◽  
Christopher Drovandi ◽  
Angus Johnston ◽  
Matthew J Simpson ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Signal To Noise Ratio ◽  
Dynamical Behaviour ◽  
Dynamical Processes ◽  
Extrinsic Noise ◽  
Flow Cytometry Data ◽  
Modelling Framework ◽  
Distribution Matching ◽  
Primary Means ◽  
Cell To Cell Variability

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is the primary means by which cells absorb drugs, viruses and other material, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data collected from an experiment that probes the internalisation of antibody by transferrin receptors in C1R cells. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from experiments that probe a range of dynamical processes.

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