scholarly journals Moment-Based Estimation of State-Switching Rates in Cell Populations

2022 ◽  
Author(s):  
Michael M Saint-Antoine ◽  
Abhyudai Singh
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Tumor Cell ◽  
Simulated Data ◽  
Computational Method ◽  
Cell Populations ◽  
Biologically Relevant ◽  
Fluctuation Test ◽  
State Switching

In isogenic cell populations, cells can switch back and forth between different gene expression states. These expression states can be biologically relevant. For example, a certain expression state may cause a tumor cell to be resistant to treatment, while another state may leave it vulnerable to treatment. However, estimating the rates of state-switching can be difficult, because experimentally measuring a cell's transcriptome often involves destroying the cell, so it can only be measured once. In this paper, we propose a computational method to estimate the rate of switching between expression states, given data from a Luria-Delbrück style fluctuation test that is experimentally simple and feasible. We then benchmark this method using simulated data to test its efficacy, with varying assumptions made about cell cycle timing distribution in the simulations.

Arginine Vasopressin Inhibition of Cyclin D1 Gene Expression Blocks the Cell Cycle and Cell Proliferation in the Mouse Y1 Adrenocortical Tumor Cell Line†

Biochemistry ◽  
2003 ◽  
Vol 42 (7) ◽  
pp. 2116-2121 ◽  
Author(s):  
Telma T. Schwindt ◽  
Fábio L. Forti ◽  
Maria Ap. Juliano ◽  
Luiz Juliano ◽  
Hugo A. Armelin
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Tumor Cell ◽  
Cyclin D1 ◽  
Cell Line ◽  
Arginine Vasopressin ◽  
Tumor Cell Line ◽  
Adrenocortical Tumor

scholarly journals A Self-Attention Model for Inferring Cooperativity between Regulatory Features

2020 ◽  
Author(s):  
Fahad Ullah ◽  
Asa Ben-Hur
Keyword(s):  
Gene Expression ◽  
Simulated Data ◽  
Relevant Information ◽  
Regulatory Elements ◽  
Dnase I ◽  
Attention Mechanism ◽  
Feature Interaction ◽  
Biologically Relevant ◽  
Attention Model ◽  
Biological Phenomena

AbstractMotivationDeep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problemResultsWe present SATORI, a Self-ATtentiOn based model to predict Regulatory element Interactions. Our approach combines convolutional and recurrent layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. We evaluate our method on simulated data and three complex datasets: human TAL1-GATA1 transcription factor ChIP-Seq, DNase I Hypersensitive Sites (DHSs) in human promoters across 164 cell lines, and genome-wide DNase I-Seq and ATAC-Seq peaks across 36 arabidopsis samples. In each of the three experiments SATORI identified numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of these experimentally verified TF-TF interactions than the existing Feature Interaction Score, and also has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactionsAvailabilityThe source code for SATORI is available at https://github.com/fahadahaf/[email protected]

Persistent cell proliferation signals correlates with increased glycolysis in tumor hypoxia microenvironment across cancer types

2020 ◽  
Author(s):  
Jinfen Wei ◽  
Kaitang Huang ◽  
Meiling Hu ◽  
Zixi Chen ◽  
Yunmeng Bai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Tumor Cell ◽  
Gene Expression Signature ◽  
Gene Set ◽  
Expression Signature ◽  
Molecular Features ◽  
Cancer Types ◽  
Pan Cancer

AbstractBackgroundAltered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. Given that the absence of multi-sample big data research about glycolysis, the molecular mechanisms involved in glycolysis or the relationships between glycolysis and tumor microenvironment are not fully studied. Thus, a more comprehensive approach in a pan-cancer landscape may be needed.MethodsHere, we develop a computational pipeline to study multi-omics molecular features defining glycolysis activity and identify molecular alterations that correlate with glycolysis. We apply a 22-gene expression signature to define the glycolysis activity landscape and verify the robustness using clinically defined glycolysis samples from several previous studies. Based on gene expression signature, we classify about 5552 of 9229 tumor samples into glycolysis score-high and score-low groups across 25 cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Moreover, using genomes and transcriptome data, we characterize the association of copy-number aberrations (CNAs), somatic single-nucleotide variants (SNVs) and hypoxia signature with glycolysis activity.FindingsGene set variation analysis (GSVA) score by gene set expression was verified robustly to represent glycolytic activity and highly glycolytic tumors presented a poor overall survival in some cancer types. Then, we identified various types of molecular features promoting tumor cell proliferation were associated with glycolysis activity. Our study showed that TCA cycle and respiration electron transport were active in glycolysis-high tumors, indicating glycolysis was not a symptom of impaired oxidative metabolism. The glycolytic score significantly correlated with hypoxia score across all cancer types. Glycolysis score was also associated with elevated genomic instability. In all tumor types, high glycolysis tumors exhibited characteristic driver genes altered by CNAs identified multiple oncogenes and tumor suppressors. We observed widespread glycolysis-associated dysregulation of mRNA across cancers and screened out HSPA8 and P4HA1 as the potential modulating factor to glycolysis. Besides, the expression of genes encoding glycolytic enzymes positively correlated with genes in cell cycle.InterpretationThis is the first study to identify gene expression signatures that reflect glycolysis activity, which can be easily applied to large numbers of patient samples. Our analysis establishes a computational framework for characterizing glycolysis activity using gene expression data and defines correlation of glycolysis with the hypoxia microenvironment, tumor cell cycle and proliferation at a pan-cancer landscape. The findings suggest that the mechanisms whereby hypoxia influence glycolysis are likely multifactorial. Our finding is significant not just in demonstrating definition value for glycolysis but also in providing a comprehensive molecular-level understanding of glycolysis and suggesting a framework to guide combination therapy that may block the glycolysis pathway to control tumor growth in hypoxia microenvironment.

Induction of Cell Death by Cytokines in Cell Cycle-Synchronous Tumor Cell Populations Restricted to G1and G2

1996 ◽  
Vol 223 (2) ◽  
pp. 259-267 ◽  
Author(s):  
Jörg Breder ◽  
Stephan Rüller ◽  
Elisabeth Rüller ◽  
Max Schlaak ◽  
Jürgen van der Bosch
Keyword(s):  
Cell Cycle ◽  
Cell Death ◽  
Tumor Cell ◽  
Cell Populations ◽  
Synchronous Tumor

scholarly journals Role for Histone Deacetylase 1 in Human Tumor Cell Proliferation

2007 ◽  
Vol 27 (13) ◽  
pp. 4784-4795 ◽  
Author(s):  
Silvia Senese ◽  
Katrin Zaragoza ◽  
Simone Minardi ◽  
Ivan Muradore ◽  
Simona Ronzoni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Tumor Cell ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Class I ◽  
Tumor Cell Proliferation ◽  
Specific Target ◽  
Class I Hdacs

ABSTRACT Posttranslational modifications of core histones are central to the regulation of gene expression. Histone deacetylases (HDACs) repress transcription by deacetylating histones, and class I HDACs have a crucial role in mouse, Xenopus laevis, zebra fish, and Caenorhabditis elegans development. The role of individual class I HDACs in tumor cell proliferation was investigated using RNA interference-mediated protein knockdown. We show here that in the absence of HDAC1 cells can arrest either at the G1 phase of the cell cycle or at the G2/M transition, resulting in the loss of mitotic cells, cell growth inhibition, and an increase in the percentage of apoptotic cells. On the contrary, HDAC2 knockdown showed no effect on cell proliferation unless we concurrently knocked down HDAC1. Using gene expression profiling analysis, we found that inactivation of HDAC1 affected the transcription of specific target genes involved in proliferation and apoptosis. Furthermore, HDAC2 downregulation did not cause significant changes compared to control cells, while inactivation of HDAC1, HDAC1 plus HDAC2, or HDAC3 resulted in more distinct clusters. Loss of these HDACs might impair cell cycle progression by affecting not only the transcription of specific target genes but also other biological processes. Our data support the idea that a drug targeting specific HDACs could be highly beneficial in the treatment of cancer.

Cell cycle analysis of mixed tumor cell populations

1975 ◽  
Vol 24 (1-2) ◽  
pp. 107-128 ◽  
Author(s):  
Birger Jansson ◽  
Laszlo Révész
Keyword(s):  
Cell Cycle ◽  
Tumor Cell ◽  
Cell Cycle Analysis ◽  
Cell Populations ◽  
Mixed Tumor

Estimating cell cycle model parameters using systems identification

2015 ◽  
Author(s):  
Edwin Francisco Juarez Rosales ◽  
Ahmadreza Ghaffarizadeh ◽  
Samuel H Friedman ◽  
Edmond Jonckheere ◽  
Paul Macklin
Keyword(s):  
Cell Cycle ◽  
Synthetic Data ◽  
Simulated Data ◽  
Model Parameters ◽  
Cycle Model ◽  
Biologically Relevant ◽  
Systems Identification ◽  
A Cell ◽  
Mixed Types ◽  
Advanced Version

A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.

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