scholarly journals Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells

2018 ◽  
Author(s):  
Kevin Y. Chen ◽  
Daniel M. Zuckerman ◽  
Philip C. Nelson
Keyword(s):  
Gene Expression ◽  
Error Correction ◽  
Stochastic Simulation ◽  
Single Molecule ◽  
Stochastic Simulation Algorithm ◽  
Molecular Processes ◽  
Statistical Measures ◽  
Mean And Variance ◽  
The Mean ◽  
Cellular Control

AbstractStochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplement.

scholarly journals Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Kevin Y. Chen ◽  
Daniel M. Zuckerman ◽  
Philip C. Nelson
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Error Correction ◽  
Stochastic Simulation ◽  
Single Molecule ◽  
Stochastic Simulation Algorithm ◽  
Stochastic Error ◽  
Statistical Measures ◽  
Mean And Variance ◽  
Cellular Control

ABSTRACT Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential equation approach by generating typical system histories, instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch—that is, without recourse to black-box packages. In some cases, their results can also be compared directly with single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies involving gene expression and error correction, respectively. For gene expression, stochastic simulation results are compared with experimental data, an important research exercise for biophysics students. For error correction, several proofreading models are compared to find the minimal components necessary for sufficient accuracy in translation. Animations of the stochastic error correction models provide insight into the proofreading mechanisms. Code samples and resulting animations showing results are given in the online Supplemental Material.

scholarly journals Genetic Basis of Transcriptome Diversity in Drosophila melanogaster

2015 ◽  
Author(s):  
Wen Huang ◽  
Mary Anna Carbone ◽  
Michael Magwire ◽  
Jason Peiffer ◽  
Richard Lyman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drosophila Melanogaster ◽  
Quantitative Trait ◽  
Genetic Basis ◽  
Trait Variation ◽  
Regulatory Variants ◽  
Genome Wide ◽  
Mean And Variance ◽  
The Mean

Understanding how DNA sequence variation is translated into variation for complex phenotypes has remained elusive, but is essential for predicting adaptive evolution, selecting agriculturally important animals and crops, and personalized medicine. Here, we quantified genome-wide variation in gene expression in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP). We found that a substantial fraction of the Drosophila transcriptome is genetically variable and organized into modules of genetically correlated transcripts, which provide functional context for newly identified transcribed regions. We identified regulatory variants for the mean and variance of gene expression, the latter of which could often be explained by an epistatic model. Expression quantitative trait loci for the mean, but not the variance, of gene expression were concentrated near genes. This comprehensive characterization of population scale diversity of transcriptomes and its genetic basis in the DGRP is critically important for a systems understanding of quantitative trait variation.

scholarly journals Genetic basis of transcriptome diversity in Drosophila melanogaster

2015 ◽  
Vol 112 (44) ◽  
pp. E6010-E6019 ◽  
Author(s):  
Wen Huang ◽  
Mary Anna Carbone ◽  
Michael M. Magwire ◽  
Jason A. Peiffer ◽  
Richard F. Lyman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drosophila Melanogaster ◽  
Dna Sequence ◽  
Quantitative Trait ◽  
Genetic Basis ◽  
Protein Coding ◽  
Regulatory Variants ◽  
Genome Wide ◽  
Mean And Variance ◽  
The Mean

Understanding how DNA sequence variation is translated into variation for complex phenotypes has remained elusive but is essential for predicting adaptive evolution, for selecting agriculturally important animals and crops, and for personalized medicine. Gene expression may provide a link between variation in DNA sequence and organismal phenotypes, and its abundance can be measured efficiently and accurately. Here we quantified genome-wide variation in gene expression in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), increasing the annotated Drosophila transcriptome by 11%, including thousands of novel transcribed regions (NTRs). We found that 42% of the Drosophila transcriptome is genetically variable in males and females, including the NTRs, and is organized into modules of genetically correlated transcripts. We found that NTRs often were negatively correlated with the expression of protein-coding genes, which we exploited to annotate NTRs functionally. We identified regulatory variants for the mean and variance of gene expression, which have largely independent genetic control. Expression quantitative trait loci (eQTLs) for the mean, but not for the variance, of gene expression were concentrated near genes. Notably, the variance eQTLs often interacted epistatically with local variants in these genes to regulate gene expression. This comprehensive characterization of population-scale diversity of transcriptomes and its genetic basis in the DGRP is critically important for a systems understanding of quantitative trait variation.

Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics

2010 ◽  
pp. 198-218
Author(s):  
Andre S. Ribeiro ◽  
John J. Grefenstette ◽  
Stuart A. Kauffman
Keyword(s):  
Gene Expression ◽  
Stochastic Simulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Stochastic Dynamics ◽  
Single Gene ◽  
Stochastic Simulation Algorithm ◽  
Simulation Algorithm ◽  
Gene Regulatory ◽  
Modeling Strategy

We present a recently developed modeling strategy of gene regulatory networks (GRN) that uses the delayed stochastic simulation algorithm to drive its dynamics. First, we present experimental evidence that led us to use this strategy. Next, we describe the stochastic simulation algorithm (SSA), and the delayed SSA, able to simulate time-delayed events. We then present a model of single gene expression. From this, we present the general modeling strategy of GRN. Specific applications of the approach are presented, beginning with the model of single gene expression which mimics a recent experimental measurement of gene expression at single-protein level, to validate our modeling strategy. We also model a toggle switch with realistic noise and delays, used in cells as differentiation pathway switches. We show that its dynamics differs from previous modeling strategies predictions. As a final example, we model the P53-Mdm2 feedback loop, whose malfunction is associated to 50% of cancers, and can induce cells apoptosis. In the end, we briefly discuss some issues in modeling the evolution of GRNs, and outline some directions for further research.

The Expression of Dopamine Receptors Gene and their Potential Role in Targeting Breast Cancer Cells with Selective Agonist and Antagonist Drugs. Could it be the Novel Insight to Therapy?

2019 ◽  
Vol 16 (2) ◽  
pp. 184-197 ◽  
Author(s):  
Hossein Bakhtou ◽  
Asiie Olfatbakhsh ◽  
Abdolkhaegh Deezagi ◽  
Ghasem Ahangari
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Cells ◽  
Dopamine Receptors ◽  
Breast Cancer Cells ◽  
D2 Receptors ◽  
Healthy Individuals ◽  
Agonist And Antagonist ◽  
The Mean ◽  
Mcf 7

Background:Breast cancer is one of the common causes of mortality for women in Iran and other parts of the world. The substantial increasing rate of breast cancer in both developed and developing countries warns the scientists to provide more preventive steps and therapeutic measures. This study is conducted to investigate the impact of neurotransmitters (e.g., Dopamine) through their receptors and the importance of cancers via damaging immune system. It also evaluates dopamine receptors gene expression in the women with breast cancer at stages II or III and dopamine receptor D2 (DRD2) related agonist and antagonist drug effects on human breast cancer cells, including MCF-7 and SKBR-3.Methods:The patients were categorized into two groups: 30 native patients who were diagnosed with breast cancer at stages II and III, with the mean age of 44.6 years and they were reported to have the experience of a chronic stress or unpleasant life event. The second group included 30 individuals with the mean age of 39 years as the control group. In order to determine the RNA concentration in all samples, the RNA samples were extracted and cDNA was synthesized. The MCF-7 cells and SKBR-3 cells were treated with dopamine receptors agonists and antagonists. The MTT test was conducted to identify oxidative and reductive enzymes and to specify appropriate dosage at four concentrations of dopamine and Cabergoline on MCF-7 and SKBR-3 cells. Immunofluorescence staining was done by the use of a mixed dye containing acridine orange and ethidiume bromide on account of differentiating between apoptotic and necrotic cells. Flow cytometry assay was an applied method to differentiate necrotic from apoptotic cells.Results:Sixty seven and thirty three percent of the patients were related to stages II and III, respectively. About sixty three percent of the patients expressed ER, while fifty seven percent expressed PR. Thirty seven percent of the patients were identified as HER-2 positive. All types of D2-receptors were expressed in PBMC of patients with breast cancer and healthy individuals. The expression of the whole dopamine receptor subtypes (DRD2-DRD4) was carried out on MCF-7 cell line. The results of RT-PCR confirmed the expression of DRD2 on SKBR-3 cells, whereas the other types of D2- receptors did not have an expression. The remarkable differences in gene expression rates between patients and healthy individuals were revealed in the result of the Real-time PCR analysis. The over expression in DRD2 and DRD4 genes of PBMCs was observed in the patients with breast cancer at stages II and III. The great amount of apoptosis and necrosis occurred after the treatment of MCF-7 cells by Cabergoline from 25 to 100 µmolL-1 concentrations.Conclusion:This study revealed the features of dopamine receptors associated with apoptosis induction in breast cancer cells. Moreover, the use of D2-agonist based on dopamine receptors expression in various breast tumoral cells could be promising as a new insight of complementary therapy in breast cancer.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

scholarly journals Groups and Individuals: Optical Flow Patterns of Broiler Chicken Flocks Are Correlated with the Behavior of Individual Birds

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 568
Author(s):  
Sabine G. Gebhardt-Henrich ◽  
Ariane Stratmann ◽  
Marian Stamp Dawkins
Keyword(s):  
Optical Flow ◽  
Broiler Chicken ◽  
Average Length ◽  
Flow Patterns ◽  
Group Level ◽  
Positive Correlation ◽  
Water Test ◽  
Mean And Variance ◽  
The Mean

Group level measures of welfare flocks have been criticized on the grounds that they give only average measures and overlook the welfare of individual animals. However, we here show that the group-level optical flow patterns made by broiler flocks can be used to deliver information not just about the flock averages but also about the proportion of individuals in different movement categories. Mean optical flow provides information about the average movement of the whole flock while the variance, skew and kurtosis quantify the variation between individuals. We correlated flock optical flow patterns with the behavior and welfare of a sample of 16 birds per flock in two runway tests and a water (latency-to-lie) test. In the runway tests, there was a positive correlation between the average time taken to complete the runway and the skew and kurtosis of optical flow on day 28 of flock life (on average slow individuals came from flocks with a high skew and kurtosis). In the water test, there was a positive correlation between the average length of time the birds remained standing and the mean and variance of flock optical flow (on average, the most mobile individuals came from flocks with the highest mean). Patterns at the flock level thus contain valuable information about the activity of different proportions of the individuals within a flock.

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