History of changes to: Synthetic biology and machine learning for next generation biofuels
Date Action Change(s) User
Nov. 27, 2023, 2:13 p.m. Added 35 {"external_links": []}
Nov. 20, 2023, 2:03 p.m. Added 35 {"external_links": []}
Nov. 13, 2023, 1:34 p.m. Added 35 {"external_links": []}
Nov. 6, 2023, 1:31 p.m. Added 35 {"external_links": []}
Aug. 14, 2023, 1:31 p.m. Added 35 {"external_links": []}
Aug. 7, 2023, 1:32 p.m. Added 35 {"external_links": []}
July 31, 2023, 1:34 p.m. Added 35 {"external_links": []}
July 24, 2023, 1:36 p.m. Added 35 {"external_links": []}
July 17, 2023, 1:34 p.m. Added 35 {"external_links": []}
July 10, 2023, 1:26 p.m. Added 35 {"external_links": []}
July 3, 2023, 1:26 p.m. Added 35 {"external_links": []}
June 26, 2023, 1:26 p.m. Added 35 {"external_links": []}
June 19, 2023, 1:27 p.m. Added 35 {"external_links": []}
June 12, 2023, 1:29 p.m. Added 35 {"external_links": []}
June 5, 2023, 1:33 p.m. Added 35 {"external_links": []}
May 29, 2023, 1:28 p.m. Added 35 {"external_links": []}
May 22, 2023, 1:29 p.m. Added 35 {"external_links": []}
May 15, 2023, 1:31 p.m. Added 35 {"external_links": []}
May 8, 2023, 1:37 p.m. Added 35 {"external_links": []}
May 1, 2023, 1:28 p.m. Added 35 {"external_links": []}
April 24, 2023, 1:35 p.m. Added 35 {"external_links": []}
April 17, 2023, 1:28 p.m. Added 35 {"external_links": []}
April 10, 2023, 1:25 p.m. Added 35 {"external_links": []}
April 3, 2023, 1:26 p.m. Added 35 {"external_links": []}
Jan. 28, 2023, 11:08 a.m. Created 43 [{"model": "core.projectfund", "pk": 28684, "fields": {"project": 5897, "organisation": 7, "amount": 0, "start_date": "2016-09-05", "end_date": "2020-11-30", "raw_data": 46476}}]
Jan. 28, 2023, 10:52 a.m. Added 35 {"external_links": []}
April 11, 2022, 3:47 a.m. Created 43 [{"model": "core.projectfund", "pk": 20812, "fields": {"project": 5897, "organisation": 7, "amount": 0, "start_date": "2016-09-05", "end_date": "2020-11-30", "raw_data": 27692}}]
April 11, 2022, 3:47 a.m. Created 41 [{"model": "core.projectorganisation", "pk": 78774, "fields": {"project": 5897, "organisation": 6381, "role": "STUDENT_PP_ORG"}}]
April 11, 2022, 3:47 a.m. Created 41 [{"model": "core.projectorganisation", "pk": 78773, "fields": {"project": 5897, "organisation": 2207, "role": "LEAD_ORG"}}]
April 11, 2022, 3:47 a.m. Created 40 [{"model": "core.projectperson", "pk": 48661, "fields": {"project": 5897, "person": 8484, "role": "STUDENT_PER"}}]
April 11, 2022, 3:47 a.m. Created 40 [{"model": "core.projectperson", "pk": 48660, "fields": {"project": 5897, "person": 46, "role": "SUPER_PER"}}]
April 11, 2022, 1:48 a.m. Updated 35 {"title": ["", "Synthetic biology and machine learning for next generation biofuels"], "description": ["", "\nPropane (C3H8) is a volatile hydrocarbon with highly favourable physicochemical properties as a fuel, in addition to existing global markets and infrastructure for storage, distribution and utilization in a wide range of applications. Consequently, propane is an attractive target product in research aimed at developing new renewable alternatives to complement currently used petroleum-derived fuels. This project focuses on the construction and evaluation of alternative microbial biosynthetic pathways for the production of renewable propane. This study will expand the metabolic toolbox for renewable propane production and provides new insight and understanding for the development of next-generation biofuel platforms. \n\nThis project will focus on new biocatalytic parts for metabolic engineering. Based on our crystal structures of ADO we have already identified residue hotspots within the active channel that when mutated give rise to improved variants (i.e. faster propane synthesis). We will assemble enzyme libraries in which we increase the frequency of residue changes throughout the enzyme by constructing synthetic DNA libraries of ADO. We will use Manchester's in-house SpeedyGenes and GeneGenie methodologies, which enable high fidelity gene synthesis and efficient production of error-corrected synthetic protein libraries at residues throughout the protein for directed evolution studies. Importantly, SpeedyGenes can accommodate multiple and (statistically) controlled combinatorial variant sequences while maintaining efficient enzymatic error correction. We will couple this new approach of making synthetic libraries to (i) machine learning approaches for active learning of sequence-activity relationships, and (ii) HTP single cell screening approaches that we have/are developing at Manchester.\n\nThe project will be based in the new BBSRC/EPSRC Synthetic Biology Centre in MIB (http://synbiochem.co.uk) providing state-of-the art infrastructure and training in synthetic biology methods.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
April 11, 2022, 1:48 a.m. Added 35 {"external_links": [21998]}
April 11, 2022, 1:48 a.m. Created 35 [{"model": "core.project", "pk": 5897, "fields": {"owner": null, "is_locked": false, "coped_id": "c11aef87-6060-4643-af53-c86952dc3038", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 27677, "created": "2022-04-11T01:42:00.052Z", "modified": "2022-04-11T01:42:00.052Z", "external_links": []}}]