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The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 12, p e1009761 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.<br />Author summary People have long been interested in the evolutionary processes through which life on our planet could have arisen from a non-life background. However, it seems that experimental studies in this field are proceeding slowly, perhaps owing to the complication of such processes. In the meantime, computer modeling has shown its potential to disclose the evolutionary mechanisms involved. Now a major difficulty of the computer modeling work is to justify the parameter-setting–on account of our limited knowledge on prebiotic chemistry and environments. Thus, people tend to explore the parameter space to seek parameter values in favor of the hypothetic scene and leave the parameter-justification a later job when sufficient knowledge is available. To date, the parameter-exploration is usually conducted manually (in many cases by trial and error), thus arduous and unpredictable. Inspired by the algorithm of machine-learning, we designed an automatic approach of parameter-exploration. The results showed that the approach is quite effective–that is, “good” parameter-sets in favor of hypothetic scenes in the origin of life can be found automatically. It is expected that such a machine-learning-like method would greatly enhance the efficiency of our evolutionary modeling studies on the origin of life in future.
- Subjects :
- Computer and Information Sciences
Evolutionary Processes
QH301-705.5
Origin of Life
Research and Analysis Methods
Biochemistry
Models, Biological
Computer Applications
Machine Learning
Cellular and Molecular Neuroscience
Nucleic Acids
Evolutionary Modeling
Differential Equations
Genetics
Ribozymes
Computer Simulation
Biology (General)
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Evolutionary Biology
Ecology
Nucleotides
Applied Mathematics
Simulation and Modeling
Biology and Life Sciences
Computational Biology
Proteins
Biological Evolution
Enzymes
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Enzymology
RNA
Mathematics
Algorithms
Research Article
Computer Modeling
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....ab662dd7807ed200b1e6c84657cf017e