Dataset Information

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

ABSTRACT: The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values "supporting" a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular "Methods" in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field-hopefully leading to significant steps forward in respect to our understanding on the origin of life.

SUBMITTER: Liang Y

PROVIDER: S-EPMC8752021 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

Liang Yuzhen Y Yu Chunwu C Ma Wentao W

PLoS computational biology 20211229 12

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values "supporting" a hypothetical scene (that is, leaving the parameter-justification a later job when su ...[more]

PMID: 34965249

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Dataset Information

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

Publications

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

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