Computer simulations capable of exploring the consequences of different scenarios aren’t just for virtual characters, as in The Sims series; they also promise to enrich our understanding of genome architecture and function. In fact, computer simulations of nuclear DNA have been around for about 25 years, modeling the large-scale 3D organization and dynamics of chromosomes with increasing sophistication. Nearly all of these efforts feature in a systematic assessment presented in the January 2014 issue of International Review of Cell and Molecular Biology. This assessment (“Computational Models of Large-Scale Genome Architecture”) not only looks back, categorizing the main simulation types, it also looks forward, outlining the prospects of in silico research into nuclear DNA.
The authors of the assessment, physicists Angelo Rosa, Ph.D., (International School for Advanced Studies in Trieste) and Christophe Zimmer, Ph.D., (Pasteur Institute in Paris), aimed their presentation at biologists. “We have made minimal use of mathematical formulas, which hamper reading,” said Dr. Rosa. “The paper is actually also interesting for physicists and mathematicians who are approaching this new field for the first time.”
In keeping with its emphasis on biologist’s concerns, the paper notes that computational approaches are being developed to complement experimental approaches, including single-cell imaging and chromosome conformation capture methods. While these approaches are yielding rapidly growing quantitative datasets, they cannot provide a full understanding of genome architecture and function. Computational models, besides helping biologists sense of experimental data, also allow a quantitative understanding of how chromosomes fold, move, and interact.
The overview, asserted Dr. Rosa, is “a useful tool which, without going into mathematical detail, provides the biologist with an overview of the type of studies that will increasingly complement the more traditional approaches.” It identifies two broad approaches to computational modeling of genome architecture: direct modeling approaches and inverse modeling approaches.
Direct modeling uses a relatively small number of assumptions and quantitative parameters: “The behavior of chromosomes is then typically computed using polymer physics theories and/or numerical simulations. Such models can be used to predict a variety of observable quantities such as contact frequency maps or distances between loci, which can then be compared to experimental measurements.”
Inverse modeling uses rich experimental datasets (such as genome-wide contact matrices measured by chromosome conformation capture) to reconstruct chromosome structures: “Optimization methods are typically used to determine structures that are as much as possible consistent with these experimental data and with a small number of data-independent assumptions. These reconstructed models can then be used to determine quantities not directly accessible in the experimental data, such as positions of loci or chromosomes.”
“We already have software programs which, starting from experimental data, allow us to reconstruct the structure of specific portions of chromosomes,” added Dr. Rosa. “I think that if computers continue to evolve as they have done until now—and there’s no reason to doubt this—we’ll be able to reconstruct entire chromosomes.”
According to Dr. Rosa, the prospects of in silico research into nuclear DNA are twofold: “to understand in detail the dynamics of gene expression (the details of protein synthesis), and to identify precisely where the chromosomes are when DNA unravels in the nucleus.”