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Stochastic hybrid modeling of DNA replication across a complete genome.

ABSTRACT: DNA replication in eukaryotic cells initiates from hundreds of origins along their genomes, leading to complete duplication of genetic information before cell division. The large number of potential origins, coupled with system uncertainty, dictates the need for new analytical tools to capture spatial and temporal patterns of DNA replication genome-wide. We have developed a stochastic hybrid model that reproduces DNA replication throughout a complete genome. The model can capture different modes of DNA replication and is applicable to various organisms. Using genome-wide data on the location and firing efficiencies of origins in the fission yeast, we show how the DNA replication process evolves during S-phase in the presence of stochastic origin firing. Simulations reveal small regions of the genome that extend S-phase to three times its reported duration. The low levels of late replication predicted by the model are below the detection limit of techniques used to measure S-phase length. Parameter sensitivity analysis shows that increased replication fork speeds genome-wide, or additional origins are not sufficient to reduce S-phase to its reported length. We model the redistribution of a limiting initiation factor during S-phase and show that it could shorten S-phase to the reported duration. Alternatively, S-phase may be extended, and what has traditionally been defined as G2 may be occupied by low levels of DNA synthesis with the onset of mitosis delayed by activation of the G2/M checkpoint.

SUBMITTER: Lygeros J

PROVIDER: S-EPMC2527905 | biostudies-other | 2008 Aug

REPOSITORIES: biostudies-other

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Stochastic hybrid modeling of DNA replication across a complete genome.

Lygeros J J Koutroumpas K K Dimopoulos S S Legouras I I Kouretas P P Heichinger C C Nurse P P Lygerou Z Z

Proceedings of the National Academy of Sciences of the United States of America 20080819 34

DNA replication in eukaryotic cells initiates from hundreds of origins along their genomes, leading to complete duplication of genetic information before cell division. The large number of potential origins, coupled with system uncertainty, dictates the need for new analytical tools to capture spatial and temporal patterns of DNA replication genome-wide. We have developed a stochastic hybrid model that reproduces DNA replication throughout a complete genome. The model can capture different modes ...[more]

PMID: 18713859

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Project description:Deterministic models of biochemical processes at the subcellular level might become inadequate when a cascade of chemical reactions is induced by a few molecules. Inherent randomness of such phenomena calls for the use of stochastic simulations. However, being computationally intensive, such simulations become infeasible for large and complex reaction networks. To improve their computational efficiency in handling these networks, we present a hybrid approach, in which slow reactions and fluxes are handled through exact stochastic simulation and their fast counterparts are treated partially deterministically through chemical Langevin equation. The classification of reactions as fast or slow is accompanied by the assumption that in the time-scale of fast reactions, slow reactions do not occur and hence do not affect the probability of the state. Our new approach also handles reactions with complex rate expressions such as Michaelis-Menten kinetics. Fluxes which cannot be modeled explicitly through reactions, such as flux of Ca(2+) from endoplasmic reticulum to the cytosol through inositol 1,4,5-trisphosphate receptor channels, are handled deterministically. The proposed hybrid algorithm is used to model the regulation of the dynamics of cytosolic calcium ions in mouse macrophage RAW 264.7 cells. At relatively large number of molecules, the response characteristics obtained with the stochastic and deterministic simulations coincide, which validates our approach in the limit of large numbers. At low doses, the response characteristics of some key chemical species, such as levels of cytosolic calcium, predicted with stochastic simulations, differ quantitatively from their deterministic counterparts. These observations are ubiquitous throughout dose response, sensitivity, and gene-knockdown response analyses. While the relative differences between the peak-heights of the cytosolic [Ca(2+)] time-courses obtained from stochastic (mean of 16 realizations) and deterministic simulations are merely 1%-4% for most perturbations, it is specially sensitive to levels of G(βγ) (relative difference as large as 90% at very low G(βγ)).

Dataset Information

Stochastic hybrid modeling of DNA replication across a complete genome.

Publications

Stochastic hybrid modeling of DNA replication across a complete genome.

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