Deciphering Cell Fate Decisions with Logic-Incorporated Gene Regulatory Networks

Q: How do changes in epigenetic levels shape the noise profile of cell populations during aging

During aging, changes in epigenetic levels can significantly impact the noise profile of cell populations. Epigenetic modifications, such as DNA methylation and histone modifications, play a crucial role in regulating gene expression patterns. As cells age, alterations in these epigenetic marks can lead to changes in the accessibility of genes for transcription factors, influencing the overall gene expression landscape. These changes in epigenetic levels can directly affect the noise profile within cell populations. For example, variations in DNA methylation patterns can result in altered gene expression variability among individual cells. Cells with different epigenetic profiles may exhibit distinct levels of stochasticity or heterogeneity in their gene expression due to differential regulation at the chromatin level. Moreover, as cells age and undergo epigenetic remodeling, certain genes may become more prone to fluctuations or noise in their expression levels. This increased noise could potentially drive shifts in fate decisions by altering the balance between different lineage-specifying factors within a cell population. The interplay between epigenetic changes and noise profiles during aging highlights the intricate relationship between cellular aging processes and regulatory dynamics.

Q: What implications does altering signals have on the stability of stem cell fates under different logic motifs

Altering signals has significant implications on the stability of stem cell fates under different logic motifs. In a scenario where signals are manipulated asymmetrically (e.g., increasing one lineage-specific factor while keeping another constant), stem cell fate decisions are influenced by both driving forces and regulatory logic embedded within gene regulatory networks. Under different logic motifs like AND-AND or OR-OR configurations, changing signals asymmetrically leads to distinct outcomes regarding stability of stem cell fates. In an AND-AND motif system with signal induction favoring differentiation towards one lineage over another, stem cells tend to lose their undifferentiated state quickly due to sequential bifurcations driven by induced signaling cues. Conversely, under an OR-OR motif configuration with similar signal manipulation inducing differentiation towards specific lineages but maintaining some flexibility through intermediate states like progenitors before terminal commitment occurs gradually ensures accuracy but slows down progression compared to AND-AND-like systems. Therefore, altering signals asymmetrically influences how stem cells maintain their undifferentiated state or transition towards specific lineages based on underlying logic motifs present within gene regulatory networks governing fate decisions.

Q: How can expression variance patterns be leveraged to identify key regulators in complex gene regulatory networks

Expression variance patterns offer valuable insights into identifying key regulators within complex gene regulatory networks (GRNs). By analyzing temporal patterns of variation across multiple genes rather than focusing solely on mean expressions allows for a deeper understanding of network dynamics and functional relationships among genes involved in fate decisions. To leverage expression variance patterns for identifying key regulators: Cluster Analysis: Utilize clustering algorithms like fuzzy c-means clustering to group genes based on similar temporal variance patterns. Functional Enrichment Analysis: Conduct enrichment analysis on clusters identified from variance patterns to uncover biological functions associated with these groups of genes. Regulatory Network Construction: Build TF-centric regulatory networks using filtered TFs from clusters exhibiting significant variation trends over time. Network Visualization: Visualize interactions among TFs extracted from high-variance clusters along with known supermodules or functional modules related to specific cellular processes. 5 .Validation Through Experimental Data: Compare predicted key regulators derived from variance analysis against experimental data sets measuring actual regulator activities during fate transitions for validation purposes. By integrating computational modeling approaches that consider both mean expressions and temporal variances across multiple genes simultaneously enables researchers to identify critical regulators orchestrating complex fate decision processes within GRNs effectively

核心概念

Gene regulatory networks and logic motifs play a crucial role in shaping cell fate decisions under different driving forces.

要約

Organisms utilize gene regulatory networks (GRNs) for fate decisions.
The intricate regulatory mechanisms of transcription factors (TFs) in GRNs are explored.
Noise-driven and signal-driven modes impact cell fate decisions differently.
Logic motifs influence the bias of fate decisions under noise-driven mode.
Signal-driven mode shows a trade-off between progression and accuracy in cell fate decisions.
Computational models reveal insights into hematopoiesis, embryogenesis, and trans-differentiation.
The chemical-induced reprogramming of human erythroblasts to induced megakaryocytes is analyzed using an OR-OR-like motif.

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統計

Chang et al. uncovered that hematopoietic stem cell population possesses intrinsic heterogeneity of Scal-1 expression.
Due to accessibility of signal manipulation, the signal-driven mode has been widely utilized for cell fate engineering.

引用

"Cells reside in a stationary landscape where decisions are made by switching through discrete valleys."
"The distortion of the landscape orchestrates fate transitions driven by extrinsic signals."

抽出されたキーインサイト

A Logic-incorporated Gene Regulatory Network Deciphers Principles in Cell Fate Decisions

by Xue,G., Zhan... 場所 www.biorxiv.org 04-21-2023

https://www.biorxiv.org/content/10.1101/2023.04.21.537440v2

深掘り質問

How do changes in epigenetic levels shape the noise profile of cell populations during aging

During aging, changes in epigenetic levels can significantly impact the noise profile of cell populations. Epigenetic modifications, such as DNA methylation and histone modifications, play a crucial role in regulating gene expression patterns. As cells age, alterations in these epigenetic marks can lead to changes in the accessibility of genes for transcription factors, influencing the overall gene expression landscape.
These changes in epigenetic levels can directly affect the noise profile within cell populations. For example, variations in DNA methylation patterns can result in altered gene expression variability among individual cells. Cells with different epigenetic profiles may exhibit distinct levels of stochasticity or heterogeneity in their gene expression due to differential regulation at the chromatin level.
Moreover, as cells age and undergo epigenetic remodeling, certain genes may become more prone to fluctuations or noise in their expression levels. This increased noise could potentially drive shifts in fate decisions by altering the balance between different lineage-specifying factors within a cell population. The interplay between epigenetic changes and noise profiles during aging highlights the intricate relationship between cellular aging processes and regulatory dynamics.

What implications does altering signals have on the stability of stem cell fates under different logic motifs

Altering signals has significant implications on the stability of stem cell fates under different logic motifs. In a scenario where signals are manipulated asymmetrically (e.g., increasing one lineage-specific factor while keeping another constant), stem cell fate decisions are influenced by both driving forces and regulatory logic embedded within gene regulatory networks.
Under different logic motifs like AND-AND or OR-OR configurations, changing signals asymmetrically leads to distinct outcomes regarding stability of stem cell fates. In an AND-AND motif system with signal induction favoring differentiation towards one lineage over another, stem cells tend to lose their undifferentiated state quickly due to sequential bifurcations driven by induced signaling cues.
Conversely, under an OR-OR motif configuration with similar signal manipulation inducing differentiation towards specific lineages but maintaining some flexibility through intermediate states like progenitors before terminal commitment occurs gradually ensures accuracy but slows down progression compared to AND-AND-like systems.
Therefore, altering signals asymmetrically influences how stem cells maintain their undifferentiated state or transition towards specific lineages based on underlying logic motifs present within gene regulatory networks governing fate decisions.

How can expression variance patterns be leveraged to identify key regulators in complex gene regulatory networks

Expression variance patterns offer valuable insights into identifying key regulators within complex gene regulatory networks (GRNs). By analyzing temporal patterns of variation across multiple genes rather than focusing solely on mean expressions allows for a deeper understanding of network dynamics and functional relationships among genes involved in fate decisions.
To leverage expression variance patterns for identifying key regulators:

Cluster Analysis: Utilize clustering algorithms like fuzzy c-means clustering to group genes based on similar temporal variance patterns.

Functional Enrichment Analysis: Conduct enrichment analysis on clusters identified from variance patterns to uncover biological functions associated with these groups of genes.

Regulatory Network Construction: Build TF-centric regulatory networks using filtered TFs from clusters exhibiting significant variation trends over time.

Network Visualization: Visualize interactions among TFs extracted from high-variance clusters along with known supermodules or functional modules related to specific cellular processes.

5 .Validation Through Experimental Data: Compare predicted key regulators derived from variance analysis against experimental data sets measuring actual regulator activities during fate transitions for validation purposes.
By integrating computational modeling approaches that consider both mean expressions and temporal variances across multiple genes simultaneously enables researchers to identify critical regulators orchestrating complex fate decision processes within GRNs effectively