Dr. Janaka N Edirisinghe, PhD, MSc, BSc - University of Chicago/Argonne National Laboratory - Assistant Scientist

Dr. Janaka N Edirisinghe

PhD, MSc, BSc

University of Chicago/Argonne National Laboratory

Assistant Scientist

Chicago, IL | United States

Main Specialties: Biology

Additional Specialties: Microbial Physiology, Computational Biology


Top Author

Dr. Janaka N Edirisinghe, PhD, MSc, BSc - University of Chicago/Argonne National Laboratory - Assistant Scientist

Dr. Janaka N Edirisinghe

PhD, MSc, BSc

Introduction

Primary Affiliation: University of Chicago/Argonne National Laboratory - Chicago, IL , United States

Specialties:

Additional Specialties:

Experience

Jan 2012
Research scientist
Jan 2012
Research scientist

Publications

13Publications

128Reads

1151Profile Views

141PubMed Central Citations

KBase: The United States Department of Energy Systems Biology Knowledgebase.

Nat Biotechnol 2018 07;36(7):566-569

Computer Science and Math, Computer Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.

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http://dx.doi.org/10.1038/nbt.4163DOI Listing
July 2018
11 Reads
3 Citations
41.514 Impact Factor

Reconstruction and Analysis of Central Metabolism in Microbes.

Methods Mol Biol 2018 ;1716:111-129

Computation Institute, University of Chicago, Chicago, IL, USA.

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http://dx.doi.org/10.1007/978-1-4939-7528-0_5DOI Listing
July 2018
12 Reads

Metabolic Reconstruction and Modeling Microbial Electrosynthesis.

Sci Rep 2017 08 21;7(1):8391. Epub 2017 Aug 21.

Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.

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http://dx.doi.org/10.1038/s41598-017-08877-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5566340PMC
August 2017
13 Reads
1 Citation
5.080 Impact Factor

A novel signal transduction protein: Combination of solute binding and tandem PAS-like sensor domains in one polypeptide chain.

Protein Sci 2017 04 6;26(4):857-869. Epub 2017 Mar 6.

Biosciences Division, Argonne National Laboratory, Argonne, Illinois, 60439.

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http://dx.doi.org/10.1002/pro.3134DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368068PMC
April 2017
8 Reads
2.854 Impact Factor

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

Front Microbiol 2016 24;7:1819. Epub 2016 Nov 24.

Computation Institute, University of ChicagoChicago, IL, USA; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA.

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http://dx.doi.org/10.3389/fmicb.2016.01819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5121216PMC
November 2016
14 Reads
2 Citations
3.941 Impact Factor

Modeling central metabolism and energy biosynthesis across microbial life.

BMC Genomics 2016 Aug 8;17:568. Epub 2016 Aug 8.

Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL, 60439, USA.

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http://dx.doi.org/10.1186/s12864-016-2887-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977884PMC
August 2016
7 Reads
2 Citations
3.990 Impact Factor

Constructing and Analyzing Metabolic Flux Models of Microbial Communities

Springer Protocols Handbooks

Here we provide a broad overview of current research in modeling the growth and behavior of microbial communities, while focusing primarily on metabolic flux modeling techniques, including the reconstruction of individual species models, reconstruction of mixed-bag models, and reconstruction of multi-species models. We describe how flux balance analysis may be applied with these various model types to explore the interactions of a microbial community with its environment, as well as the interactions of individual species with each other. We demonstrate all discussed model reconstruction and analysis approaches using the Department of Energy’s Systems Biology Knowledgebase (KBase), constructing and importing genome-scale metabolic models of Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii, and subsequently combining them into a community model of the gut microbiome. We also use KBase to explore how these species interact with each other and with the gut environment, exploring the trade-offs in information provided by applying each metabolic flux modeling approach. Overall, we conclude that no single approach is better than the others, and often there is much to be gained by applying multiple approaches synergistically when exploring the ecology of a microbial community.

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June 2016
8 Reads

From DNA to FBA: how to build your own genome-scale metabolic model

Front. Microbiol. | doi: 10.3389/fmicb.2016.00907

Frontiers in Microbiology

Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.

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May 2016
13 Reads

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction.

Methods Mol Biol 2014 ;1191:19-45

Computation Institute, University Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA.

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http://dx.doi.org/10.1007/978-1-4939-1170-7_2DOI Listing
May 2015
8 Reads

Comparative genomics of cultured and uncultured strains suggests genes essential for free-living growth of Liberibacter.

PLoS One 2014 8;9(1):e84469. Epub 2014 Jan 8.

Microbiology and Cell Science Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida, United States of America.

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0084469PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885570PMC
September 2014
11 Reads
12 Citations
3.234 Impact Factor