Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 14.860
Filtrar
1.
BMC Bioinformatics ; 21(1): 450, 2020 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-33045987

RESUMO

BACKGROUND: The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. RESULTS: For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein-Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein-Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein-Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. CONCLUSIONS: There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8-3.2 days.


Assuntos
Microbiota , Modelos Biológicos , Voluntários Saudáveis , Humanos , Cinética , Processos Estocásticos
2.
Comput Math Methods Med ; 2020: 9214159, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33082843

RESUMO

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Estatísticos , Pandemias , Pneumonia Viral/epidemiologia , Algoritmos , China/epidemiologia , Biologia Computacional , Simulação por Computador , Fractais , Humanos , Pandemias/estatística & dados numéricos , Processos Estocásticos , Incerteza
3.
PLoS One ; 15(10): e0241163, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33095815

RESUMO

The events of the recent SARS-CoV-2 epidemics have shown the importance of social factors, especially given the large number of asymptomatic cases that effectively spread the virus, which can cause a medical emergency to very susceptible individuals. Besides, the SARS-CoV-2 virus survives for several hours on different surfaces, where a new host can contract it with a delay. These passive modes of infection transmission remain an unexplored area for traditional mean-field epidemic models. Here, we design an agent-based model for simulations of infection transmission in an open system driven by the dynamics of social activity; the model takes into account the personal characteristics of individuals, as well as the survival time of the virus and its potential mutations. A growing bipartite graph embodies this biosocial process, consisting of active carriers (host) nodes that produce viral nodes during their infectious period. With its directed edges passing through viral nodes between two successive hosts, this graph contains complete information about the routes leading to each infected individual. We determine temporal fluctuations of the number of exposed and the number of infected individuals, the number of active carriers and active viruses at hourly resolution. The simulated processes underpin the latent infection transmissions, contributing significantly to the spread of the virus within a large time window. More precisely, being brought by social dynamics and exposed to the currently existing infection, an individual passes through the infectious state until eventually spontaneously recovers or otherwise is moves to a controlled hospital environment. Our results reveal complex feedback mechanisms that shape the dependence of the infection curve on the intensity of social dynamics and other sociobiological factors. In particular, the results show how the lockdown effectively reduces the spread of infection and how it increases again after the lockdown is removed. Furthermore, a reduced level of social activity but prolonged exposure of susceptible individuals have adverse effects. On the other hand, virus mutations that can gradually reduce the transmission rate by hopping to each new host along the infection path can significantly reduce the extent of the infection, but can not stop the spreading without additional social strategies. Our stochastic processes, based on graphs at the interface of biology and social dynamics, provide a new mathematical framework for simulations of various epidemic control strategies with high temporal resolution and virus traceability.


Assuntos
Infecções Assintomáticas , Betacoronavirus/genética , Infecções por Coronavirus/transmissão , Modelos Estatísticos , Pneumonia Viral/transmissão , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/virologia , Suscetibilidade a Doenças , Humanos , Relações Interpessoais , Mutação , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/virologia , Quarentena/métodos , Processos Estocásticos , Fatores de Tempo
4.
PLoS One ; 15(10): e0241170, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33112895

RESUMO

Estimating the percentages of undiagnosed and asymptomatic patients is essential for controlling the outbreak of SARS-CoV-2, and for assessing any strategy for controlling the disease. In this paper, we propose a novel analysis based on the birth-death process with recursive full tracing. We estimated the numbers of undiagnosed symptomatic patients and the lower bound of the number of total infected individuals per diagnosed patient before and after the declaration of the state of emergency in Hokkaido, Japan. The median of the estimated number of undiagnosed symptomatic patients per diagnosed patient decreased from 1.7 to 0.77 after the declaration, and the median of the estimated lower bound of the number of total infected individuals per diagnosed patient decreased from 4.2 to 2.4. We will discuss the limitations and possible expansions of the model.


Assuntos
Infecções Assintomáticas/epidemiologia , Betacoronavirus , Busca de Comunicante/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Pandemias , Pneumonia Viral/epidemiologia , Técnicas de Laboratório Clínico , Análise por Conglomerados , Simulação por Computador , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/transmissão , Conjuntos de Dados como Assunto , Humanos , Ilhas , Japão/epidemiologia , Modelos Teóricos , Pneumonia Viral/diagnóstico , Pneumonia Viral/transmissão , Quarentena , Processos Estocásticos
5.
Nucleic Acids Res ; 48(19): 10867-10876, 2020 11 04.
Artigo em Inglês | MEDLINE | ID: mdl-33051686

RESUMO

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood. Using an innovated, ultra-sensitive technique, we address here enigmatic features underlying the communications between MYC and its enhancers in relation to the transcriptional process. MYC thus interacts with its flanking enhancers in a mutually exclusive manner documenting that enhancer hubs impinging on MYC detected in large cell populations likely do not exist in single cells. Dynamic encounters with pathologically activated enhancers responsive to a range of environmental cues, involved <10% of active MYC alleles at any given time in colon cancer cells. Being the most central node of the chromatin network, MYC itself likely drives its communications with flanking enhancers, rather than vice versa. We submit that these features underlie an acquired ability of MYC to become dynamically activated in response to a diverse range of environmental cues encountered by the cell during the neoplastic process.


Assuntos
Carcinogênese/genética , Montagem e Desmontagem da Cromatina , Regulação Neoplásica da Expressão Gênica , Proteínas Proto-Oncogênicas c-myc/genética , Animais , Drosophila , Redes Reguladoras de Genes , Células HCT116 , Humanos , Proteínas Proto-Oncogênicas c-myc/metabolismo , Processos Estocásticos
6.
BMC Bioinformatics ; 21(1): 476, 2020 Oct 22.
Artigo em Inglês | MEDLINE | ID: mdl-33092528

RESUMO

BACKGROUND: Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes. RESULTS: Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker. CONCLUSIONS: We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.


Assuntos
Redes Reguladoras de Genes , Linguagens de Programação , Software , Algoritmos , Simulação por Computador , Análise de Sequência de RNA , Processos Estocásticos
7.
PLoS One ; 15(10): e0238835, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33052923

RESUMO

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.


Assuntos
Visualização de Dados , Algoritmos , Interpretação Estatística de Dados , Mineração de Dados , Humanos , Método de Monte Carlo , Distribuição Normal , Probabilidade , Software , Processos Estocásticos
8.
PLoS One ; 15(9): e0239149, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32946511

RESUMO

We present an easily calibrated spatial modeling framework for estimating location-specific fertilizer responses, using smallholder maize farming in Tanzania as a case study. By incorporating spatially varying input and output prices, we predict the expected profitability for a location-specific smallholder farmer. A stochastic rainfall component of the model allows us to quantify the uncertainty around expected economic returns. The resulting mapped estimates of expected profitability and uncertainty are good predictors of actual smallholder fertilizer usage in nationally representative household survey data. The integration of agronomic and economic information in our framework makes it a powerful tool for spatially explicit targeting of agricultural technologies and complementary investments, as well as estimating returns to investments at multiple scales.


Assuntos
Produção Agrícola/economia , Fertilizantes/economia , Investimentos em Saúde/economia , Modelos Econômicos , Zea mays/crescimento & desenvolvimento , Produção Agrícola/métodos , Fazendas/economia , Fazendas/estatística & dados numéricos , Previsões/métodos , Chuva , Análise Espacial , Processos Estocásticos , Tanzânia , Incerteza
9.
Epidemiol Infect ; 148: e233, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32988429

RESUMO

In Spain, the epidemic curve caused by COVID-19 has reached its peak in the last days of March. The implementation of the blockade derived from the declaration of the state of alarm on 14th March has raised a discussion on how and when to deal with the unblocking. In this paper, we intend to add information that may help by using epidemic simulation techniques with stochastic individual contact models and several extensions.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Pneumonia Viral/diagnóstico , Simulação por Computador , Infecções por Coronavirus/epidemiologia , Humanos , Pandemias , Isolamento de Pacientes , Pneumonia Viral/epidemiologia , Espanha/epidemiologia , Processos Estocásticos
10.
Phys Rev Lett ; 125(9): 094101, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32915595

RESUMO

Synchronization is a widespread phenomenon observed in physical, biological, and social networks, which persists even under the influence of strong noise. Previous research on oscillators subject to common noise has shown that noise can actually facilitate synchronization, as correlations in the dynamics can be inherited from the noise itself. However, in many spatially distributed networks, such as the mammalian circadian system, the noise that different oscillators experience can be effectively uncorrelated. Here, we show that uncorrelated noise can in fact enhance synchronization when the oscillators are coupled. Strikingly, our analysis also shows that uncorrelated noise can be more effective than common noise in enhancing synchronization. We first establish these results theoretically for phase and phase-amplitude oscillators subject to either or both additive and multiplicative noise. We then confirm the predictions through experiments on coupled electrochemical oscillators. Our findings suggest that uncorrelated noise can promote rather than inhibit coherence in natural systems and that the same effect can be harnessed in engineered systems.


Assuntos
Relógios Biológicos , Modelos Teóricos , Humanos , Oscilometria/métodos , Processos Estocásticos
11.
J Chem Phys ; 153(11): 114119, 2020 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-32962383

RESUMO

The complexity associated with an epidemic defies any quantitatively reliable predictive theoretical scheme. Here, we pursue a generalized mathematical model and cellular automata simulations to study the dynamics of infectious diseases and apply it in the context of the COVID-19 spread. Our model is inspired by the theory of coupled chemical reactions to treat multiple parallel reaction pathways. We essentially ask the question: how hard could the time evolution toward the desired herd immunity (HI) be on the lives of people? We demonstrate that the answer to this question requires the study of two implicit functions, which are determined by several rate constants, which are time-dependent themselves. Implementation of different strategies to counter the spread of the disease requires a certain degree of a quantitative understanding of the time-dependence of the outcome. Here, we compartmentalize the susceptible population into two categories, (i) vulnerables and (ii) resilients (including asymptomatic carriers), and study the dynamical evolution of the disease progression. We obtain the relative fatality of these two sub-categories as a function of the percentages of the vulnerable and resilient population and the complex dependence on the rate of attainment of herd immunity. We attempt to study and quantify possible adverse effects of the progression rate of the epidemic on the recovery rates of vulnerables, in the course of attaining HI. We find the important result that slower attainment of the HI is relatively less fatal. However, slower progress toward HI could be complicated by many intervening factors.


Assuntos
Doenças Transmissíveis/imunologia , Doenças Transmissíveis/patologia , Imunidade Coletiva , Modelos Teóricos , Controle de Doenças Transmissíveis , Humanos , Modelos Biológicos , Probabilidade , Processos Estocásticos
12.
Math Biosci ; 329: 108466, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32920095

RESUMO

In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Biológicos , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , Teorema de Bayes , Infecções por Coronavirus/transmissão , Progressão da Doença , Previsões , Hospitalização/estatística & dados numéricos , Humanos , Conceitos Matemáticos , Pneumonia Viral/transmissão , Eslovênia/epidemiologia , Processos Estocásticos
13.
Math Biosci Eng ; 17(4): 2792-2804, 2020 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-32987496

RESUMO

The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. This has led to a significant impact on the lives and economy in China and other countries. Here we develop a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease. Model parameters are estimated on the basis of fitting to newly reported data from January 11 to February 13, 2020 in China. The estimates of the contact rate and the effective reproductive number support the efficiency of the control measures that have been implemented so far. Simulations show the newly confirmed cases will continue to decline and the total confirmed cases will reach the peak around the end of February of 2020 under the current control measures. The impact of the timing of returning to work is also evaluated on the disease transmission given different strength of protection and control measures.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Biológicos , Pandemias , Pneumonia Viral/epidemiologia , Número Básico de Reprodução/estatística & dados numéricos , China/epidemiologia , Simulação por Computador , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Humanos , Conceitos Matemáticos , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Processos Estocásticos
14.
Proc Natl Acad Sci U S A ; 117(39): 24575-24580, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32887803

RESUMO

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.


Assuntos
Controle de Doenças Transmissíveis/organização & administração , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Betacoronavirus , Cidades/epidemiologia , Controle de Doenças Transmissíveis/métodos , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Humanos , Modelos Estatísticos , Ontário/epidemiologia , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Prevalência , Processos Estocásticos , Viagem
15.
Oecologia ; 194(1-2): 123-134, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32865688

RESUMO

Recent studies suggest that the assembly of trophic interaction networks is the result of both niche (deterministic and selective) and neutral (stochastic) processes, but we know little about their relative importance. Succession following disturbance offers a good opportunity to address this question. Studies of single-trophic guilds suggest that, shortly after a disturbance, such as a fire, neutral assembly processes (e.g. colonisation events) dominate; whereas, niche processes (selection) become more and more important as succession proceeds. Building on these observations, we predict similar changes in interaction networks during succession, with a shift from stochastic toward selective interactions. To test this, we studied succession of plant-herbivorous insect networks in South Africa after a fire. We sampled a total of 385 herbivorous arthropod species and 92 plant species. For different successional stages and spatial grain sizes, we used network descriptors to estimate plant-herbivore specificity and partner fidelity of plant and herbivore species across networks (i.e. localities). We compared the observed network descriptors to neutral models, and then differentiated selective species (associated with similar partner species in different networks) from neutral species (associated at random with their partners). Our results suggest that specialisation, partner fidelity and the proportion of selective species of plants and herbivores increased with succession, which is consistent with the hypothesis that niche-based processes prevail over neutral processes as succession proceeds. However, in all the successional stages, the majority of species were neutral species, which pinpoints the importance of opportunistic interactions in the assembly of trophic networks.


Assuntos
Fogo , Herbivoria , Animais , Ecossistema , Plantas , África do Sul , Processos Estocásticos
16.
PLoS One ; 15(8): e0238000, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32866182

RESUMO

The standard GLM and GAM frequency-severity models assume independence between the claim frequency and severity. To overcome restrictions of linear or additive forms and to relax the independence assumption, we develop a data-driven dependent frequency-severity model, where we combine a stochastic gradient boosting algorithm and a profile likelihood approach to estimate parameters for both of the claim frequency and average claim severity distributions, and where we introduce the dependence between the claim frequency and severity by treating the claim frequency as a predictor in the regression model for the average claim severity. The model can flexibly capture the nonlinear relation between the claim frequency (severity) and predictors and complex interactions among predictors and can fully capture the nonlinear dependence between the claim frequency and severity. A simulation study shows excellent prediction performance of our model. Then, we demonstrate the application of our model with a French auto insurance claim data. The results show that our model is superior to other state-of-the-art models.


Assuntos
Revisão da Utilização de Seguros/estatística & dados numéricos , Modelos Estatísticos , Processos Estocásticos
17.
Mol Cell ; 80(2): 359-373.e8, 2020 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-32991830

RESUMO

Eukaryotic gene expression regulation involves thousands of distal regulatory elements. Understanding the quantitative contribution of individual enhancers to gene expression is critical for assessing the role of disease-associated genetic risk variants. Yet, we lack the ability to accurately link genes with their distal regulatory elements. To address this, we used 3D enhancer-promoter (E-P) associations identified using split-pool recognition of interactions by tag extension (SPRITE) to build a predictive model of gene expression. Our model dramatically outperforms models using genomic proximity and can be used to determine the quantitative impact of enhancer loss on gene expression in different genetic backgrounds. We show that genes that form stable E-P hubs have less cell-to-cell variability in gene expression. Finally, we identified transcription factors that regulate stimulation-dependent E-P interactions. Together, our results provide a framework for understanding quantitative contributions of E-P interactions and associated genetic variants to gene expression.


Assuntos
Bactérias/isolamento & purificação , Elementos Facilitadores Genéticos , Regiões Promotoras Genéticas , Animais , Células Dendríticas/metabolismo , Feminino , Regulação da Expressão Gênica , Modelos Lineares , Camundongos Endogâmicos C57BL , Modelos Biológicos , Processos Estocásticos , Fatores de Transcrição/metabolismo
18.
Nat Commun ; 11(1): 4743, 2020 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-32958773

RESUMO

How the coexistence of many species is maintained is a fundamental and unresolved question in ecology. Coexistence is a puzzle because we lack a mechanistic understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. Here, I study the variation of species presence and abundance in microbial communities from a macroecological standpoint. I identify three macroecological laws that quantitatively characterize the fluctuation of species abundance across communities and over time. Using these three laws, one can predict species' presence and absence, diversity, and commonly studied macroecological patterns. I show that a mathematical model based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.


Assuntos
Biodiversidade , Microbiota , Modelos Teóricos , Ecossistema , Processos Estocásticos
19.
PLoS Comput Biol ; 16(9): e1007728, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32970668

RESUMO

Calcium oscillations and waves induce depolarization in cardiac cells which are believed to cause life-threathening arrhythimas. In this work, we study the conditions for the appearance of calcium oscillations in both a detailed subcellular model of calcium dynamics and a minimal model that takes into account just the minimal ingredients of the calcium toolkit. To avoid the effects of homeostatic changes and the interaction with the action potential we consider the somewhat artificial condition of a cell without pacing and with no calcium exchange with the extracellular medium. Both the full subcellular model and the minimal model present the same scenarios depending on the calcium load: two stationary states, one with closed ryanodine receptors (RyR) and most calcium in the cell stored in the sarcoplasmic reticulum (SR), and another, with open RyRs and a depleted SR. In between, calcium oscillations may appear. The robustness of these oscillations is determined by the amount of calsequestrin (CSQ). The lack of this buffer in the SR enhances the appearance of oscillations. The minimal model allows us to relate the stability of the oscillating state to the nullcline structure of the system, and find that its range of existence is bounded by a homoclinic and a Hopf bifurcation, resulting in a sudden transition to the oscillatory regime as the cell calcium load is increased. Adding a small amount of noise to the RyR behavior increases the parameter region where oscillations appear and provides a gradual transition from the resting state to the oscillatory regime, as observed in the subcellular model and experimentally.


Assuntos
Cálcio/metabolismo , Miócitos Cardíacos/metabolismo , Animais , Calsequestrina/metabolismo , Modelos Biológicos , Canal de Liberação de Cálcio do Receptor de Rianodina/metabolismo , Retículo Sarcoplasmático/metabolismo , Processos Estocásticos , Frações Subcelulares/metabolismo
20.
Proc Natl Acad Sci U S A ; 117(37): 22674-22683, 2020 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-32868438

RESUMO

Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.


Assuntos
Células/química , Modelos Biológicos , Bioquímica , Células/metabolismo , Fenômenos Químicos , Redes Reguladoras de Genes , Cinética , Modelos Teóricos , Proteínas/genética , Proteínas/metabolismo , Processos Estocásticos
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA