Noise and Hysteresis in Gene Regulatory Networks-AI Insights
PreviousMathematical Modeling and Analysis of Signaling Pathways and Reaction Networks-AI InsightsNextDelving into Battery Hysteresis-AI Insights
Last updated
Was this helpful?
Last updated
Was this helpful?
Gene regulatory networks (GRNs) are shaped by randomness and memory. Stochasticity, especially with low protein counts, leads to diverse cell fates and irreversibility. Hysteresis, where system state depends on history, further complicates GRNs, impacting processes like cancer metastasis. Mathematical modeling, using tools like the CME, helps analyze probability distributions and transition rates. Protein bursting and mRNA/protein degradation contribute to network complexity. Understanding the interplay of stochasticity and hysteresis is crucial for deciphering GRN mechanisms and their influence on cellular behavior.