[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128503-en":3,"doc-seo-128503-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128503,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape","Fitness landscape of a biomolecule models activity as a function of sequence, where the distribution of functional variants and their connectivity within sequence space shape evolutionary trajectories. Sequence-space exploration is limited by combinatorial explosion, even with high-throughput assays that only partially alleviate the problem. This thesis integrates massively parallel experimental data with smart library design and advanced computational methods. It studies an RNA ligase ribozyme and uses evolutionary computation and supervised deep learning to infer epistasis, guide search, and provide experimental evidence for a large RNA neutral network.","Okinawa Institute of Science and Technology Graduate University  \nThesis submitted for the degree Doctor of Philosophy  \nMachine Learning Guided Exploration of  \nan Empirical Ribozyme Fitness Landscape  \nby Rachapun Rotrattanadumrong  \nunder the supervision of Prof. Yohei Yokobayashi August 2023  \nDeclaration of Original and Sole Authorship  \nI, Rachapun Rotrattanadumrong, declare that this thesis entitled “Machine learning guided exploration of an empirical ribozyme fitness landscape” and the data presented in it are original and my own work.  \nI confirm that:  \nl No part of this work has previously been submitted for a degree at this or any other university.  \nl References to the work of others have been clearly acknowledged. Quotations from the work of others have been clearly indicated and attributed to them.  \nl In cases where others have contributed to part of this work, such contribution has been clearly acknowledged and distinguished from my own work.  \nl None of this work has been previously published and pre-printed elsewhere, with the exception of the following:  \nRotrattanadumrong, R. and Yokobayashi, Y. (2022)‘Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning’, Nature communications, 13(1), p. 4847.  \n(In this work, I designed all experiments and computational models, perform all data collection and analysis, all model training and evaluation and wrote the manuscript. Yohei Yokobayashi supervised the experimental design, evaluate the data analysis and edited the manuscript.)  \nDate: 18th August 2023  \nSignature:  \nRachapun Rotrattanadumrong  \nAbstract  \nFitness landscape of a biomolecule is a representation of its activity as a function of its sequence. Properties of a fitness landscape determine how evolution proceeds. Therefore, the distribution of functional variants and more importantly, the connectivity of these variants within the sequence space are important scientific questions. Exploration of these spaces, however, is impeded by the combinatorial explosion of the sequence space. Highthroughput experimental methods have recently reduced this impediment but only modestly. Better computational methods are needed to fully utilize the rich information from these experimental data to better understand the properties of the fitness landscape. In this work, I seek to improve this exploration process by combining data from massively parallel experimental assay with smart library design using advanced computational techniques. I focus on an artificial RNA enzyme or ribozyme that can catalyze a ligation reaction between two RNA fragments. This chemistry is analogous to that of the modern RNA polymerase enzymes, therefore, represents an important reaction in the origin of life. In the first chapter, I discuss the background to this work in the context of evolutionary theory of fitness landscape and its implications in biotechnology. In chapter 2, I explore the use of processes borrowed from the field of evolutionary computation to solve optimization problems using real experimental sequence-activity data. In chapter 3, I investigate the use of supervised machine learning models to extract information on epistatic interactions from the dataset collected during multiple rounds of directed evolution. I investigate and experimentally validate the extent to which a deep learning model can be used to guide a completely computational evolutionary algorithm towards distant regions of the fitness landscape. In the final chapter, I perform a comprehensive experimental assay of the combinatorial region explored by the deep learning-guided evolutionary algorithm. Using this dataset, I analyze higher-order epistasis and attempt to explain the increased predictability of the region sampled by the algorithm. Finally, I provide the first experimental evidence of a large RNA‘neutral network’. Altogether, this work represents the most comprehensive experimental and computational stud","cbCainpDYjkoTN8l","https://ap.wps.com/l/cbCainpDYjkoTN8l","pdf",4861793,1,114,"English","en",105,"# Declaration of Original and Sole Authorship\n# Abstract\n# Acknowledgement\n# Table of Abbreviations\n# Dedication\n# Table of Contents\n# List of Figures\n# List of Tables\n# Chapter 1: Introduction\n# Chapter 2: Identification of functional ribozymes with high-throughput experimental ass","[{\"question\":\"What is the central problem studied in this thesis?\",\"answer\":\"The thesis addresses how to explore biomolecular fitness landscapes efficiently, focusing on how activity depends on sequence and how functional variants connect in sequence space.\"},{\"question\":\"How does the work combine computation with experiments?\",\"answer\":\"It integrates massively parallel experimental sequence-activity data with computational techniques, including evolutionary computation and supervised machine learning, to guide and evaluate exploration of fitness landscapes.\"},{\"question\":\"What ribozyme system is investigated?\",\"answer\":\"The study focuses on an artificial RNA enzyme (a ribozyme) that catalyzes a ligation reaction between two RNA fragments.\"}]","Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape | 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