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On the need for mechanistic models in computational genomics and metagenomics.

Liberles DA, Teufel AI, Liu L, Stadler T - Genome Biol Evol (2013)

Bottom Line: Addressing these important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed, instead of fitting data to off-the-shelf statistical distributions that do not enable mechanistic inference.Three examples are presented, the first involving ecological processes inferred from metagenomic data, the second involving mechanisms of gene regulation rooted in protein-DNA interactions with consideration of DNA structure, and the third involving existing models for the retention of duplicate genes that enables prediction of evolutionary mechanisms.This description of mechanistic models is generalized toward future developments in computational genomics and the need for biological mechanisms and processes in biological models.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, University of Wyoming.

ABSTRACT
Computational genomics is now generating very large volumes of data that have the potential to be used to address important questions in both basic biology and biomedicine. Addressing these important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed, instead of fitting data to off-the-shelf statistical distributions that do not enable mechanistic inference. Three examples are presented, the first involving ecological processes inferred from metagenomic data, the second involving mechanisms of gene regulation rooted in protein-DNA interactions with consideration of DNA structure, and the third involving existing models for the retention of duplicate genes that enables prediction of evolutionary mechanisms. This description of mechanistic models is generalized toward future developments in computational genomics and the need for biological mechanisms and processes in biological models.

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The hazard functions associated with the nonfunctionalization, neofunctionalization (plus nonfunctionalization), subfunctionalization (plus nonfunctionalization), and dosage balance (plus nonfunctionalization) processes as described by sample parameterization from equation (5) are shown. Equation (3) generates a similar set of curve shapes.
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evt151-F3: The hazard functions associated with the nonfunctionalization, neofunctionalization (plus nonfunctionalization), subfunctionalization (plus nonfunctionalization), and dosage balance (plus nonfunctionalization) processes as described by sample parameterization from equation (5) are shown. Equation (3) generates a similar set of curve shapes.

Mentions: In this case, the hazard function reflects the instantaneous rate of loss of a duplicate gene in a genome, dependent on the time it has survived in the genome. This modeling framework reflects a first step toward the development of mechanistic models for duplicate gene retention (see fig. 3). The models will need to be expanded to accommodate hybrid processes, like initial dosage balance followed by either subfunctionalization or neofunctionalization. In extending the model to a phylogenetic framework, the retention/loss model will need to be coupled to models that examine variability and complexity in the gene birth process. Further, the robustness of population genetic assumptions about the fixation process will need to be tested with more realistic simulations. Duplicate gene loss models represent an example where mechanistic model development in computational genomics is progressing but still at an early stage. As indicated, a more detailed technical discussion of these models and their development appears in a companion article (Zhao J, Teufel AI, Liu L, Liberles DA, manuscript submitted).Fig. 3.—


On the need for mechanistic models in computational genomics and metagenomics.

Liberles DA, Teufel AI, Liu L, Stadler T - Genome Biol Evol (2013)

The hazard functions associated with the nonfunctionalization, neofunctionalization (plus nonfunctionalization), subfunctionalization (plus nonfunctionalization), and dosage balance (plus nonfunctionalization) processes as described by sample parameterization from equation (5) are shown. Equation (3) generates a similar set of curve shapes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814209&req=5

evt151-F3: The hazard functions associated with the nonfunctionalization, neofunctionalization (plus nonfunctionalization), subfunctionalization (plus nonfunctionalization), and dosage balance (plus nonfunctionalization) processes as described by sample parameterization from equation (5) are shown. Equation (3) generates a similar set of curve shapes.
Mentions: In this case, the hazard function reflects the instantaneous rate of loss of a duplicate gene in a genome, dependent on the time it has survived in the genome. This modeling framework reflects a first step toward the development of mechanistic models for duplicate gene retention (see fig. 3). The models will need to be expanded to accommodate hybrid processes, like initial dosage balance followed by either subfunctionalization or neofunctionalization. In extending the model to a phylogenetic framework, the retention/loss model will need to be coupled to models that examine variability and complexity in the gene birth process. Further, the robustness of population genetic assumptions about the fixation process will need to be tested with more realistic simulations. Duplicate gene loss models represent an example where mechanistic model development in computational genomics is progressing but still at an early stage. As indicated, a more detailed technical discussion of these models and their development appears in a companion article (Zhao J, Teufel AI, Liu L, Liberles DA, manuscript submitted).Fig. 3.—

Bottom Line: Addressing these important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed, instead of fitting data to off-the-shelf statistical distributions that do not enable mechanistic inference.Three examples are presented, the first involving ecological processes inferred from metagenomic data, the second involving mechanisms of gene regulation rooted in protein-DNA interactions with consideration of DNA structure, and the third involving existing models for the retention of duplicate genes that enables prediction of evolutionary mechanisms.This description of mechanistic models is generalized toward future developments in computational genomics and the need for biological mechanisms and processes in biological models.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, University of Wyoming.

ABSTRACT
Computational genomics is now generating very large volumes of data that have the potential to be used to address important questions in both basic biology and biomedicine. Addressing these important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed, instead of fitting data to off-the-shelf statistical distributions that do not enable mechanistic inference. Three examples are presented, the first involving ecological processes inferred from metagenomic data, the second involving mechanisms of gene regulation rooted in protein-DNA interactions with consideration of DNA structure, and the third involving existing models for the retention of duplicate genes that enables prediction of evolutionary mechanisms. This description of mechanistic models is generalized toward future developments in computational genomics and the need for biological mechanisms and processes in biological models.

Show MeSH