Two copies of the same gene (labeled with different fluorescent proteins) are measured simultaneously in single cells. One copy is green, the other red. If the green and red signals are uncorrelated across cells, what does this indicate about the dominant source of noise?
AExtrinsic noise dominates, because shared cellular factors affect both copies equally
BIntrinsic noise dominates, because the independent fluctuations in each gene copy's transcription are not coordinated
CThere is no noise — the measurements are simply inaccurate
DThe genes are on different chromosomes, which eliminates noise
This is the classic dual-reporter experiment by Elowitz et al. (2002). If noise were purely extrinsic (variations in ribosomes, polymerase, growth rate), both copies would fluctuate together — when the cell has more ribosomes, both green and red expression increase. Correlated fluctuations = extrinsic noise. Uncorrelated fluctuations (one copy high while the other is low in the same cell) can only arise from randomness intrinsic to each copy's individual transcription and translation events. The relative contribution of intrinsic vs. extrinsic noise depends on expression level and gene characteristics.
Question 2 True / False
Stochastic gene expression noise is always detrimental to cell function and is minimized by natural selection.
TTrue
FFalse
Answer: False
While some gene circuits (like those controlling essential metabolic enzymes) have evolved to minimize noise through negative feedback and high copy numbers, noise is functionally beneficial in many contexts. Bacterial persistence — where a small fraction of cells stochastically enters a dormant state resistant to antibiotics — is a bet-hedging strategy that relies on gene expression noise. Probabilistic cell fate decisions in the immune system and in stem cell differentiation also exploit noise to generate phenotypic diversity from genetically identical populations. Natural selection shapes noise levels to match functional requirements.
Question 3 Short Answer
Why does the Gillespie algorithm, rather than deterministic ODEs, become necessary for modeling gene expression in single cells?
Think about your answer, then reveal below.
Model answer: ODEs describe average behavior over large populations but assume continuous concentrations — valid when molecule numbers are large. In single cells, key molecular species exist in very small numbers (often 1-10 mRNA molecules per gene, a few transcription factor molecules at a promoter). At these copy numbers, the discrete, probabilistic nature of individual reaction events (one mRNA molecule being made, one being degraded) produces significant fluctuations around the ODE solution. The Gillespie algorithm simulates each individual reaction event as a stochastic process, correctly capturing the probability distributions of molecular species over time rather than just the mean.
The Gillespie algorithm (also called the stochastic simulation algorithm, SSA) is exact for well-mixed chemical systems: it samples the time to the next reaction and which reaction occurs from the appropriate probability distributions. For gene expression with low mRNA copy numbers, Gillespie simulations produce the characteristic bursty, noisy expression patterns seen in single-cell experiments — behavior that is entirely invisible to ODE models, which only describe population averages.