🏵️Stochasticity in Biological Systems plus AI Expansion

Biological systems are inherently noisy, with stochasticity influencing cellular behaviors. This randomness drives phenotypic switching, binary decisions like cell fate, and dynamic transitions between stable states. Mathematical models like the Chemical Master Equation and linear noise approximations are used to study this variability. Gene regulatory networks and protein bursting amplify stochastic effects. Experimental techniques such as flow cytometry and single-cell analysis provide data on dynamic cellular behavior. Potential landscapes visualize stochastic dynamics, showing stable states and transitions. Understanding this inherent variability is crucial for comprehending biological mechanisms.