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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 two domains of Oct1 (POUS [bottom right] and POUHD [top right]) and the SOX2 (left) transcription factors bind adjacently to the Hoxb1 element on DNA. Sox2 and POUS interact by binding cooperatively to adjacent sites to facilitate the binding of co-activator OBF-1 (Williams et al. 2004). The image is generated from PDB file 1O4X.
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evt151-F1: The two domains of Oct1 (POUS [bottom right] and POUHD [top right]) and the SOX2 (left) transcription factors bind adjacently to the Hoxb1 element on DNA. Sox2 and POUS interact by binding cooperatively to adjacent sites to facilitate the binding of co-activator OBF-1 (Williams et al. 2004). The image is generated from PDB file 1O4X.

Mentions: In figure 1, the structure of two transcription factors that bind adjacent sites in a promoter cooperatively to recruit a third transcription factor is shown (Williams et al. 2004). For this mechanism to operate, the interacting transcription factors need to be on the same face of DNA and over short distances, and this can be accomplished with bent structures when the phasing of the binding sites is maintained. This process can apply both to transcription factor interactions that are necessary to recruit additional transcription factors to the promoter (including the basal apparatus) and to proteins that dimerize on DNA (see Funnell and Crossley 2012 for a review). Both the insertion and deletion processes as well as the mutational process of binding sites fading in and out can lead to changes in spacing between transcription factor binding sites that can affect transcription. This larger level organization of promoter regions has not been described in computational approaches examining the evolution of gene expression from homologous promoter regions. Considering the physical structure of DNA as well as proteins in the evolution of gene expression may be important.Fig. 1.—


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

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

The two domains of Oct1 (POUS [bottom right] and POUHD [top right]) and the SOX2 (left) transcription factors bind adjacently to the Hoxb1 element on DNA. Sox2 and POUS interact by binding cooperatively to adjacent sites to facilitate the binding of co-activator OBF-1 (Williams et al. 2004). The image is generated from PDB file 1O4X.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evt151-F1: The two domains of Oct1 (POUS [bottom right] and POUHD [top right]) and the SOX2 (left) transcription factors bind adjacently to the Hoxb1 element on DNA. Sox2 and POUS interact by binding cooperatively to adjacent sites to facilitate the binding of co-activator OBF-1 (Williams et al. 2004). The image is generated from PDB file 1O4X.
Mentions: In figure 1, the structure of two transcription factors that bind adjacent sites in a promoter cooperatively to recruit a third transcription factor is shown (Williams et al. 2004). For this mechanism to operate, the interacting transcription factors need to be on the same face of DNA and over short distances, and this can be accomplished with bent structures when the phasing of the binding sites is maintained. This process can apply both to transcription factor interactions that are necessary to recruit additional transcription factors to the promoter (including the basal apparatus) and to proteins that dimerize on DNA (see Funnell and Crossley 2012 for a review). Both the insertion and deletion processes as well as the mutational process of binding sites fading in and out can lead to changes in spacing between transcription factor binding sites that can affect transcription. This larger level organization of promoter regions has not been described in computational approaches examining the evolution of gene expression from homologous promoter regions. Considering the physical structure of DNA as well as proteins in the evolution of gene expression may be important.Fig. 1.—

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