From DNA to FBA: how to build your own genome-scale ...
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From DNA to FBA: how to build your own genome-scale metabolic model Daniel A Cuevas 1 , Taylor G O’Connell 2 , Blaire Robinson 2 , Tucker Lopez 3 , Kristen Aguinaldo 3 , Rebecca de Wardt 3 , Rhaya Alkafaji 3 , Thiago Bruce 3 , Elizabeth Dinsdale 3 , and Robert A Edwards 1,2,4 1 Computational Science Research Center, 2 Biological and Medical Informatics Research Center, 3 Department of Biology, 4 Department of Computer Science, San Diego State University, CA Introduction Genomic data has become exponentially inexpensive to generate and increasingly accessible. Limitations with traditional homology-based bioinformatics algorithms often identify in gaps in our knowledge. Genome-scale metabolic models offer rapid analysis and a unique perspective to genomic data by connecting an organism’s genome to its phenome. Can we exploit bacterial metabolic networks to fill these genomic gaps? PyFBA Workflow Culture bacterial isolates and prepare samples for DNA sequencing. Perform whole-genome shotgun sequencing using next-generation sequencing platforms. Rapid Annotations using Subsystem Technology Annotate encoded genes by predicting their functional roles using freely available online tools, such as RAST, KEGG and UniProtKB. Obtain a mapping from functional roles to enzymes to bacterial biochemical reactions using enzyme repositories. Roles Enzymes Reactions Combine all biochemical reactions into a mathematical construct for use in flux-balance analysis simulations. Identity reaction gaps in the metabolic network by using PyFBA gap-fill to assert growth in environments where growth is observed. Conclusions Databases Roles Enzymes Reactions Bacteria Case Study: Kelp Forest Isolates 4,748 functional roles 4,067 enzyme complexes 34,700 reactions 53 diverse bacteria • 5,088+ growth profiles • 23+ metabolic models PyFBA provides capabilities to use metabolic functional annotations to build draft FBA models. Combined with phenotypic growth data, PyFBA can predict the metabolic reactions missing from draft models using biochemical databases and customized gap- filling algorithms. Generating more metabolic models and growth profiles will help elucidate and establish patterns that describe our missing knowledge. Acknowledgements & Funding MCB-1330800 DUE-1323809 Argonne National Labs Contact presenter: [email protected] Out of our 53 diverse bacteria, 21 are isolated genomes from various Southern California kelp forests and 12 of those genomes share strong similarities to Vibrio cyclitrohpicus. The size of the genome correlates with the number of reactions a genome’s model contains. Eight Vibrio cyclitrohpicus models were gap-filled on a rich- nutrient LB media source using the PyFBA gap-fill algorithm. The number of added reactions is consistent. From the eight gap-filled models, a group of reactions were consistently added to assert growth on LB media. A total of 16 reactions were required by 3 or more models – 11 of those reactions were transporter proteins. GitHub: http://linsalrob.github.io/PyFBA PyPI: https://pypi.python.org/pypi/PyFBA
Transcript of From DNA to FBA: how to build your own genome-scale ...
From DNA to FBA: how to build your own genome-scale metabolic modelDaniel A Cuevas1, Taylor G O’Connell2, Blaire Robinson2, Tucker Lopez3, Kristen Aguinaldo3,
Rebecca de Wardt3, Rhaya Alkafaji3, Thiago Bruce3, Elizabeth Dinsdale3, and Robert A Edwards1,2,4
1Computational Science Research Center, 2Biological and Medical Informatics Research Center, 3Department of Biology,4Department of Computer Science, San Diego State University, CA
IntroductionGenomic data has become exponentially inexpensive to generate and increasingly accessible. Limitations with traditional homology-based bioinformatics algorithms often identify in gaps in our knowledge. Genome-scalemetabolic models offer rapid analysis and a unique perspective to genomic data by connecting an organism’s genome to its phenome. Can we exploit bacterial metabolic networks to fill these genomic gaps?
PyFBA Workflow
Culture bacterial isolatesand prepare samples forDNA sequencing.
Perform whole-genomeshotgun sequencing usingnext-generation sequencingplatforms.
Rapid Annotations usingSubsystem Technology
Annotate encoded genes by predicting their functionalroles using freely available online tools, such asRAST, KEGG and UniProtKB.
Obtain a mapping from functional rolesto enzymes to bacterial biochemicalreactions using enzyme repositories.
Roles Enzymes Reactions
Combine all biochemical reactions into amathematical construct for use in flux-balanceanalysis simulations.
Identity reaction gaps in the metabolic networkby using PyFBA gap-fill to assert growth inenvironments where growth is observed.
ConclusionsDatabasesRoles
Enzymes
Reactions
Bacteria
Case Study: Kelp Forest Isolates
4,748 functional roles
4,067 enzyme complexes
34,700 reactions
53 diverse bacteria• 5,088+ growth profiles• 23+ metabolic models
PyFBA provides capabilities to use metabolic functional annotations to build draft FBA models.
Combined with phenotypic growth data, PyFBA can predict the metabolic reactions missing from draft models using biochemical databases and customized gap-filling algorithms.
Generating more metabolic models and growth profiles will help elucidate and establish patterns that describe our missing knowledge.
Acknowledgements &Funding
MCB-1330800DUE-1323809
ArgonneNational
Labs Contact presenter: [email protected]
Out of our 53 diverse bacteria, 21 are isolated genomes fromvarious Southern California kelp forests and 12 of thosegenomes share strong similarities to Vibrio cyclitrohpicus. Thesize of the genome correlates with the number of reactions agenome’s model contains.
Eight Vibrio cyclitrohpicus models were gap-filled on a rich-nutrient LB media source using the PyFBA gap-fill algorithm. Thenumber of added reactions is consistent.
From the eight gap-filled models, a group of reactionswere consistently added to assert growth on LB media.A total of 16 reactions were required by 3 or moremodels – 11 of those reactions were transporterproteins.
GitHub: http://linsalrob.github.io/PyFBAPyPI: https://pypi.python.org/pypi/PyFBA
