Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911912282816512 |
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| author | Browning Jr., James S. L. Tauritz, Daniel R. Beckmann, John |
| author_facet | Browning Jr., James S. L. Tauritz, Daniel R. Beckmann, John |
| contents | The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins -- the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared to the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." The major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago, or maybe never evolved in the first place. We outline a computational approach to solving this problem. By merging evolutionary algorithms, machine learning (ML), and bioinformatics, we can facilitate the development of completely novel proteins which have never existed before. We envision this work forming a new sub-field of computational evolution we dub evolutionary algorithms simulating molecular evolution (EASME). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_08797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal Browning Jr., James S. L. Tauritz, Daniel R. Beckmann, John Neural and Evolutionary Computing Artificial Intelligence I.2.1 The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins -- the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared to the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." The major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago, or maybe never evolved in the first place. We outline a computational approach to solving this problem. By merging evolutionary algorithms, machine learning (ML), and bioinformatics, we can facilitate the development of completely novel proteins which have never existed before. We envision this work forming a new sub-field of computational evolution we dub evolutionary algorithms simulating molecular evolution (EASME). |
| title | Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal |
| topic | Neural and Evolutionary Computing Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2403.08797 |