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Inside individual cells, expression of genes is inherently stochastic andmanifests as cell-to-cell variability or noise in protein copy numbers. Sinceproteins half-lives can be comparable to the cell-cycle length, randomness incell-division times generates additional intercellular variability in proteinlevels. Moreover, as many mRNA/protein species are expressed at low-copynumbers, errors incurred in partitioning of molecules between the mother anddaughter cells are significant. We derive analytical formulas for the totalnoise in protein levels for a general class of cell-division time andpartitioning error distributions. Using a novel hybrid approach the total noiseis decomposed into components arising from i) stochastic expression; ii)partitioning errors at the time of cell-division and iii) random cell-divisionevents. These formulas reveal that random cell-division times not only generateadditional extrinsic noise but also critically affect the mean protein copynumbers and intrinsic noise components. Counter intuitively, in some parameterregimes noise in protein levels can decrease as cell-division times become morestochastic. Computations are extended to consider genome duplication, where thegene dosage is increased by two-fold at a random point in the cell-cycle. Wesystematically investigate how the timing of genome duplication influencesdifferent protein noise components. Intriguingly, results show that noisecontribution from stochastic expression is minimized at an optimal genomeduplication time. Our theoretical results motivate new experimental methods fordecomposing protein noise levels from single-cell expression data.Characterizing the contributions of individual noise mechanisms will lead toprecise estimates of gene expression parameters and techniques for alteringstochasticity to change phenotype of individual cells.

Decomposing variability in protein levels from noisy expression, genome duplication and partitioning errors during cell-divisions