ArticleBMC systems biology2011
Convergence of logic of cellular regulation in different premalignant cells by an information theoretic approach.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 27 citations in OpenAlex.
- Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts.Translational oncology · 2023Article
- TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation.Entropy (Basel, Switzerland) · 2020Article
- Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.Biomolecules · 2020Article
- Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green MicroalgaCells · 2019Article
- Intracellular redox potential is correlated with miRNA expression in MCF7 cells under hypoxic conditions.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Personalized disease signatures through information-theoretic compaction of big cancer data.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Article
- A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes.The journal of physical chemistry. B · 2016Article
- Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Critical Points in Tumorigenesis: A Carcinogen-Initiated Phase Transition Analyzed via Single-Cell Proteomics.Small (Weinheim an der Bergstrasse, Germany) · 2016Article
- Statistical thermodynamics of transcription profiles in normal development and tumorigeneses in cohorts of patients.European biophysics journal : EBJ · 2015Article
- Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Computational surprisal analysis speeds-up genomic characterization of cancer processes.PloS one · 2014Article
- Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.PloS one · 2014Article
- miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Free energy rhythms in Saccharomyces cerevisiae: a dynamic perspective with implications for ribosomal biogenesis.Biochemistry · 2013Article
- Article
- On a fundamental structure of gene networks in living cells.Proceedings of the National Academy of Sciences of the United States of America · 2012Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurprisal analysis is a thermodynamic-like molecular level approach that identifies biological constraints that prevents the entropy from reaching its maximum. To examine the significance of altered gene expression levels in tumorigenesis we apply surprisal analysis to the WI-38 model through its precancerous states. The constraints identified by the analysis are transcription patterns underlying the process of transformation. Each pattern highlights the role of a group of genes that act coherently to define a transformed phenotype.
resultsWe identify a major transcription pattern that represents a contraction of signaling networks accompanied by induction of cellular proliferation and protein metabolism, which is essential for full transformation. In addition, a more minor, "tumor signature" transcription pattern completes the transformation process. The variation with time of the importance of each transcription pattern is determined. Midway through the transformation, at the stage when cells switch from slow to fast growth rate, the major transcription pattern undergoes a total inversion of its weight while the more minor pattern does not contribute before that stage.
conclusionsA similar network reorganization occurs in two very different cellular transformation models: WI-38 and the cervical cancer HF1 models. Our results suggest that despite differences in a list of transcripts expressed in different cancer models the rationale of the network reorganization remains essentially the same.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.