ABSTRACT DNA double-strand breaks (DSBs) are the most lethal form of DNA damage and drive aging and cancer. A main source of spontaneous DSBs is replication stress, i.e. aberrations in DNA replication leading to slowing or stalling of replication forks. Replication stress can cause DSBs both directly and indirectly, and cells’ reaction to it is often heterogeneous, which leads to DSB patterns that are difficult to interpret. To overcome this challenge, we will use computer simulations to analyze DSB data and infer underlying mechanisms of DSB creation. We will build on and expand techniques we developed in the previous funding period: (1) i-BLESS: the most sensitive DSB detection method, allowing detection of 1 DSB in 100,000 cells; (2) quantitative DSB sequencing: the only approach that allows precise genome-wide measurement of absolute DSB frequencies (DSBs/cell); and (3) Repli-Sim: massive computer simulations of DNA replication that accurately reproduce both single-cell and population-wide data. Specifically, we will use a combination of innovative computational methods and experiments in the following Aims: 1) Elucidate the mechanisms of spatiotemporal regulation of DNA replication and how its disturbance causes replication stress. 2) Clarify and quantify consequences of replication stress and classify resulting DSBs 3) Characterize heterogeneity of cell population distribution of DSBs resulting from replication stress and infer its underlying mechanisms. The large-scale of our study will allow us to put each individual result into much broader context of all other results obtained, thus deepening its interpretation and allowing for classification of obtained DSB landscapes and inferred pathways regulating replication. Taken together, our results will lead to a system-level understanding of the mechanisms that cause and prevent replication stress and DSBs. Our project will raise the study of genomic instability to a new level by quantifying the mechanisms of replication stress and how they lead to DSBs. Our work will also provide methods and computational tools to further study genome instability and pave the way to use this knowledge to guide therapeutic decisions.