21,120 research outputs found
A stochastic model dissects cell states in biological transition processes
Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations
Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series
To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions
Probing host pathogen cross-talk by transcriptional profiling of both Mycobacterium tuberculosis and infected human dendritic cells and macrophages
This study provides the proof of principle that probing the host and the microbe transcriptomes simultaneously is a valuable means to accessing unique information on host pathogen interactions. Our results also underline the extraordinary plasticity of host cell and pathogen responses to infection, and provide a solid framework to further understand the complex mechanisms involved in immunity to M. tuberculosis and in mycobacterial adaptation to different intracellular environments
Experimental and computational applications of microarray technology for malaria eradication in Africa
Various mutation assisted drug resistance evolved in Plasmodium falciparum strains and insecticide
resistance to female Anopheles mosquito account for major biomedical catastrophes standing against
all efforts to eradicate malaria in Sub-Saharan Africa. Malaria is endemic in more than 100 countries and
by far the most costly disease in terms of human health causing major losses among many African
nations including Nigeria. The fight against malaria is failing and DNA microarray analysis need to keep
up the pace in order to unravel the evolving parasite’s gene expression profile which is a pointer to
monitoring the genes involved in malaria’s infective metabolic pathway. Huge data is generated and
biologists have the challenge of extracting useful information from volumes of microarray data.
Expression levels for tens of thousands of genes can be simultaneously measured in a single
hybridization experiment and are collectively called a “gene expression profile”. Gene expression
profiles can also be used in studying various state of malaria development in which expression profiles
of different disease states at different time points are collected and compared to each other to establish
a classifying scheme for purposes such as diagnosis and treatments with adequate drugs. This paper
examines microarray technology and its application as supported by appropriate software tools from
experimental set-up to the level of data analysis. An assessment of the level of microarray technology
in Africa, its availability and techniques required for malaria eradication and effective healthcare in
Nigeria and Africa in general were also underscored
Spatiotemporal dynamics of the postnatal developing primate brain transcriptome.
Developmental changes in the temporal and spatial regulation of gene expression drive the emergence of normal mature brain function, while disruptions in these processes underlie many neurodevelopmental abnormalities. To solidify our foundational knowledge of such changes in a primate brain with an extended period of postnatal maturation like in human, we investigated the whole-genome transcriptional profiles of rhesus monkey brains from birth to adulthood. We found that gene expression dynamics are largest from birth through infancy, after which gene expression profiles transition to a relatively stable state by young adulthood. Biological pathway enrichment analysis revealed that genes more highly expressed at birth are associated with cell adhesion and neuron differentiation, while genes more highly expressed in juveniles and adults are associated with cell death. Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex. Using network analysis, we identified 27 co-expression modules containing genes with highly correlated expression patterns that are associated with specific brain regions, ages or both. In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes. This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD
maigesPack: A Computational Environment for Microarray Data Analysis
Microarray technology is still an important way to assess gene expression in
molecular biology, mainly because it measures expression profiles for thousands
of genes simultaneously, what makes this technology a good option for some
studies focused on systems biology. One of its main problem is complexity of
experimental procedure, presenting several sources of variability, hindering
statistical modeling. So far, there is no standard protocol for generation and
evaluation of microarray data. To mitigate the analysis process this paper
presents an R package, named maigesPack, that helps with data organization.
Besides that, it makes data analysis process more robust, reliable and
reproducible. Also, maigesPack aggregates several data analysis procedures
reported in literature, for instance: cluster analysis, differential
expression, supervised classifiers, relevance networks and functional
classification of gene groups or gene networks
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