Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production

Handle URI:
http://hdl.handle.net/10754/623742
Title:
Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production
Authors:
Motwalli, Olaa Amin ( 0000-0002-3392-5734 )
Abstract:
Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed. Although more than 140 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. In relation to the above-mentioned problems, we developed the first in silico screening method (FFASC) that evaluates the cyanobacterial strains’ potential for FFA production based on the strains’ proteome, which for the first time allows non-experimental selection of the most promising chassis for cyanofactories. The solution is based on the original problem formulation, optimization and ranking. To provide developers and researchers easy means for evaluation and assessment of the cyanobacterial strains potential for production of FFA, we developed the BioPS platform. In addition to being able to compare capacity for FFA production of any novel strain against 140 pre-valuate strains, BioPS can be used to explore characteristics and assessment rules in play for an individual strain. This is the first tool of this type developed. Finally, we developed a novel generic in silico method (PathDES) for ranking and selection of the most suitable pathways / sets of metabolic reactions, which suggests genetic modifications for improved metabolic productivity. The method heavily relies on optimization and integration of disparate information in a novel manner. It has been successfully used in connection with FFASC for design of cyanofactories. In conclusion, this study has contributed novel and unique methods, and tools for the field of bioinformatics, with applications towards the metabolic design of cyanofactories. We believe that these will be of good use to researchers and technology developers in this field.
Advisors:
Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Committee Member:
Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Gojobori, Takashi ( 0000-0001-7850-1743 ) ; Zhang, Zhang
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
28-May-2017
Type:
Dissertation
Appears in Collections:
Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.advisorBajic, Vladimir B.en
dc.contributor.authorMotwalli, Olaa Aminen
dc.date.accessioned2017-05-30T09:16:40Z-
dc.date.available2017-05-30T09:16:40Z-
dc.date.issued2017-05-28-
dc.identifier.urihttp://hdl.handle.net/10754/623742-
dc.description.abstractFinding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed. Although more than 140 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. In relation to the above-mentioned problems, we developed the first in silico screening method (FFASC) that evaluates the cyanobacterial strains’ potential for FFA production based on the strains’ proteome, which for the first time allows non-experimental selection of the most promising chassis for cyanofactories. The solution is based on the original problem formulation, optimization and ranking. To provide developers and researchers easy means for evaluation and assessment of the cyanobacterial strains potential for production of FFA, we developed the BioPS platform. In addition to being able to compare capacity for FFA production of any novel strain against 140 pre-valuate strains, BioPS can be used to explore characteristics and assessment rules in play for an individual strain. This is the first tool of this type developed. Finally, we developed a novel generic in silico method (PathDES) for ranking and selection of the most suitable pathways / sets of metabolic reactions, which suggests genetic modifications for improved metabolic productivity. The method heavily relies on optimization and integration of disparate information in a novel manner. It has been successfully used in connection with FFASC for design of cyanofactories. In conclusion, this study has contributed novel and unique methods, and tools for the field of bioinformatics, with applications towards the metabolic design of cyanofactories. We believe that these will be of good use to researchers and technology developers in this field.en
dc.language.isoenen
dc.subjectcomputationalen
dc.subjectmethodsen
dc.subjectdevelopmenten
dc.subjectcyanofactoriesen
dc.subjectfree fatty aciden
dc.subjectproductionen
dc.titleNovel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Productionen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberMoshkov, Mikhailen
dc.contributor.committeememberGojobori, Takashien
dc.contributor.committeememberZhang, Zhangen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameDoctor of Philosophyen
dc.person.id115866en
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