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Related Concept Videos

Mitogens and the Cell Cycle02:38

Mitogens and the Cell Cycle

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Mitogens and their receptors play a crucial role in controlling the progression of the cell cycle. However, the loss of mitogenic control over cell division leads to tumor formation. Therefore, mitogens and mitogen receptors play an important role in cancer research. For instance, the epidermal growth factor (EGF) - a type of mitogen and its transmembrane receptor (EGFR), decides the fate of the cell's proliferation. When EGF binds to EGFR, a member of the ErbB family of tyrosine kinase...
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The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
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Several external and internal factors influence the initiation and inhibition of cell division. For instance, the death of nearby cells or the release of human growth hormone (hGH) promotes cell division. In contrast, lack of hGH or crowding of cells can inhibit cell division.
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Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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Inhibition of Cdk Activity02:34

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The orderly progression of the cell cycle depends on the activation of Cdk protein by binding to its cyclin partner. However, the cell cycle must be restricted when undergoing abnormal changes. Most cancers correlate to the deregulated cell cycle, and since Cdks are a central component of the cell cycle, Cdk inhibitors are extensively studied to develop anticancer agents. For instance, cyclin D associates with several Cdks, such as Cdk 4/6, to form an active complex. The cyclin D-Cdk4/6 complex...
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  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Low-frequency Erk And Akt Activity Dynamics Are Predictive Of Stochastic Cell Division Events.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Low-frequency Erk And Akt Activity Dynamics Are Predictive Of Stochastic Cell Division Events.

Related Experiment Video

Spatial and Temporal Analysis of Active ERK in the C. elegans Germline
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Spatial and Temporal Analysis of Active ERK in the C. elegans Germline

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Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.

Jamie J R Bennett1, Alan D Stern2, Xiang Zhang3

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

NPJ Systems Biology and Applications
|June 4, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models predict cell division by analyzing intracellular signaling dynamics. ERK pathway activity over the cell cycle, not just initial response, is key for predicting cell fate and cancer progression.

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Area of Science:

  • Cellular Biology
  • Systems Biology
  • Computational Biology

Background:

  • Intracellular signaling pathways like ERK1/2 (ERK) and Akt1/2 (Akt) are crucial for cell fate decisions.
  • Single-cell response heterogeneity complicates understanding the link between signaling dynamics and cell division.
  • Predicting cell division based on signaling dynamics is vital for cancer research.

Purpose of the Study:

  • To develop and validate machine learning models for predicting cell division events using single-cell signaling dynamics.
  • To identify which signaling pathways (ERK, Akt) and their dynamic features are most predictive of cell division.
  • To assess the generalizability of the predictive models across different cell types.

Main Methods:

  • Collected time-course data of ERK and Akt activity in single MCF10A cells after growth factor stimulation.
  • Applied discrete wavelet transforms (DWTs) to extract low-frequency features from signaling time courses.
  • Utilized Ensemble Integration framework for data integration and building predictive machine learning models.

    • Main Results:

    • Machine learning models effectively predicted cell division in MCF10A cells (F-measure=0.524, AUC=0.726).
    • ERK signaling dynamics were more predictive of cell division than Akt signaling dynamics.
    • Combining ERK and Akt data improved predictive performance; models generalized to RPE cells.

    Conclusions:

    • Intracellular signaling dynamics, particularly ERK activity throughout the cell cycle, are powerful predictors of cell division.
    • Machine learning, combined with feature extraction techniques like DWT, offers a robust approach to deciphering complex cell fate decisions.
    • This data-driven methodology has potential applications in understanding and predicting cellular behavior in various biological contexts, including disease.