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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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Related in: MedlinePlus

Using the birth process in equation (5) and the death process in equation (6) (green curve), a survival function is simulated (black curve). This is fit statistically with a mixture of two Weibull distributions (blue curve), where the individual components are shown in red. Neither recovers the Weibull loss process of the green curve, with the decaying Weibull function following the data (black) more closely than the green generative process.
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evt151-F4: Using the birth process in equation (5) and the death process in equation (6) (green curve), a survival function is simulated (black curve). This is fit statistically with a mixture of two Weibull distributions (blue curve), where the individual components are shown in red. Neither recovers the Weibull loss process of the green curve, with the decaying Weibull function following the data (black) more closely than the green generative process.

Mentions: In using simpler models that include a combination of phenomenological parameters and mechanistic parameters (hybrid models), the question emerges whether the parameterization of the two components can remain accurate if there is not a clear separation between what is being fit by different parameters. To examine this, a birth–death model was described with a dampening periodic birth function (eq. 5) and a Weibull death function (eq. 6).(5)(6)This model was then fit with a mixture of Weibull distribution probability density functions (the simulated data and the R code used to fit the data are included as supplementary materials, Supplementary Material online). Two Weibull components were found to be statistically justified. Neither the mixture Weibull nor either of the individual Weibull components showed parameters consistent with those used in equation (6), which is a Weibull distribution (fig. 4). Identifying such a case required little effort and generating a reasonable fit was much more difficult, even with mixtures of these flexible distributions.Fig. 4.—


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

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

Using the birth process in equation (5) and the death process in equation (6) (green curve), a survival function is simulated (black curve). This is fit statistically with a mixture of two Weibull distributions (blue curve), where the individual components are shown in red. Neither recovers the Weibull loss process of the green curve, with the decaying Weibull function following the data (black) more closely than the green generative process.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evt151-F4: Using the birth process in equation (5) and the death process in equation (6) (green curve), a survival function is simulated (black curve). This is fit statistically with a mixture of two Weibull distributions (blue curve), where the individual components are shown in red. Neither recovers the Weibull loss process of the green curve, with the decaying Weibull function following the data (black) more closely than the green generative process.
Mentions: In using simpler models that include a combination of phenomenological parameters and mechanistic parameters (hybrid models), the question emerges whether the parameterization of the two components can remain accurate if there is not a clear separation between what is being fit by different parameters. To examine this, a birth–death model was described with a dampening periodic birth function (eq. 5) and a Weibull death function (eq. 6).(5)(6)This model was then fit with a mixture of Weibull distribution probability density functions (the simulated data and the R code used to fit the data are included as supplementary materials, Supplementary Material online). Two Weibull components were found to be statistically justified. Neither the mixture Weibull nor either of the individual Weibull components showed parameters consistent with those used in equation (6), which is a Weibull distribution (fig. 4). Identifying such a case required little effort and generating a reasonable fit was much more difficult, even with mixtures of these flexible distributions.Fig. 4.—

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
Related in: MedlinePlus