ArticleScientific reports2020
Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Foundations of Gerophysics.Aging · 2026Article
- Evolution from Composome to RNA Replicase.Life (Basel, Switzerland) · 2026Review
- A Causal Regulation Modeling Algorithm for Temporal Events with Application toInternational journal of molecular sciences · 2024Article
- Tiny Guides, Big Impact: Focus on the Opportunities and Challenges of miR-Based Treatments for ARDS.International journal of molecular sciences · 2024Review
- A multi-scale expression and regulation knowledge base for Escherichia coli.Nucleic acids research · 2023Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Systems Biology and Omics Approaches for Complex Human Diseases.Biomolecules · 2023Article
- Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments.Entropy (Basel, Switzerland) · 2022Article
- Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition.Science advances · 2022Article
- Combined Transcriptomic and Proteomic Profiling ofInternational journal of molecular sciences · 2022Article
- GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.Frontiers in bioinformatics · 2021Article
- ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes.Scientific reports · 2020Article
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2 authors.
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Abstract
For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.
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