Modeling genome-wide replication kinetics reveals a mechanism for regulation of replication timing

Microarrays are powerful tools to probe genome‐wide replication kinetics. The rich data sets that result contain more information than has been extracted by current methods of analysis. In this paper, we present an analytical model that incorporates probabilistic initiation of origins and passive replication. Using the model, we performed least‐squares fits to a set of recently published time course microarray data on Saccharomyces cerevisiae . We extracted the distribution of firing times for each origin and found that the later an origin fires on average, the greater the variation in firing times. To explain this trend, we propose a model where earlier‐firing origins have more initiator complexes loaded and a more accessible chromatin environment. The model demonstrates how initiation can be stochastic and yet occur at defined times during S phase, without an explicit timing program. Furthermore, we hypothesize that the initiators in this model correspond to loaded minichromosome maintenance complexes. This model is the first to suggest a detailed, testable, biochemically plausible mechanism for the regulation of replication timing in eukaryotes. Synopsis The kinetics of DNA replication must be controlled for cells to develop properly. Although the biochemical mechanisms of origin initiations are increasingly well understood, the organization of initiation timing as a genome‐wide program is still a mystery. With the advance of technology, researchers have been able to generate large amounts of data revealing aspects of replication kinetics. In particular, the use of microarrays to probe the replication fraction of budding yeast genome wide has been a successful first step towards unraveling the details of the replication program (Raghuraman et al , 2001 ; Alvino et al , 2007 ; McCune et al , 2008 ). On the surface, the microarray data shows apparent patterns of early and late replicating regions and seems to support the prevailing picture of eukaryotic replication—origins are positioned at defined sites and initiated at defined, preprogrammed times (Donaldson, 2005 ). Molecular combing, a single‐molecule technique, however, showed that the initiation of origins is stochastic (Czajkowsky et al , 2008 ). Motivated by these conflicting viewpoints, we developed a model that is flexible enough to describe both deterministic and stochastic initiation. We modeled origin initiation as probabilistic events. We first propose a model where each origin is allowed to