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Peter Larsen


Bldg: 446, APCF Room: A182
Phone: 630-252-3984


> Education:

  • 2006, MS, University of Illinois at Chicago, Bioengineering
  • 1993, BS, Purdue Univeristy, Biological Science

> Professional Experience:

  • 2009 – present:
Assistant Computational Biologist, Argonne National Laboratory
  • 2001 – 2008:
Bioinformatics Specialist, Core Genomics Facility, University of Illinois at Chicago
  • 1998 – 2001:
Research and Development, Vysis Inc.
  • 1997 – 1998:

Scientist, ThermoGen, Inc.

  • 1995 – 1997:
Scientist, FermaLogic, Inc.

> Research Interests:

Analysis methods for high-throughput genomic data for the functional and structural annotation of genomes, high-throughput expression analysis, the construction of biological interaction networks, and novel computational approaches to systems biology.

> Research Group:

> Publications:

  1. Simon Lax, Daniel P. Smith, Jarrad Hampton-Marcell, Sarah M. Owens, Kim M. Handley, Nicole M. Scott, Sean M. Gibbons, Peter Larsen, Benjamin D. Shogan, Sophie Weiss, Jessica L. Metcalf, Luke K. Ursell, Yoshiki Vázquez-Baeza, Will Van Treuren, Nur A. Hasan, Molly K. Gibson, Rita Colwell, Gautam Dantas, Rob Knight, Jack A. Gilbert. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 29 August 2014: Vol. 345 no. 6200 pp. 1048-1052.
  2. Peter E Larsen, Nicole Scott, Anton F Post, Dawn Field, Rob Knight, Yuki Hamada and Jack A Gilbert. Satellite remote sensing data can be used to model marine microbial metabolite turnover. The ISME Journal, 29 July 2014; doi: 10.1038/ismej.2014.107.
  3. Peter E. Larsen, Leland J. Cseke, R. Michael Miller, Frank R. Collart. Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks. Journal of Theoretical Biology, Volume 359, 21 October 2014, pp. 61–71.
  4. Yuki Hamada, Jack A. Gilbert, Peter E. Larsen, and Madeline J. Norgaard. Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensin. Photogrammetric Engineering & Remote Sensing. Vol. 80, No. 4, April 2014, pp. 311–321.
  5. Peter Larsen and Jack Gilbert. Mycrobial Bebop: Creating Music from Complex Dynamics in Microbial Ecology. PLoS ONE 2013, 8(3): e58119.
  6. Lumayag S, Haldin CE, Corbett NJ, Wahlin KJ, Cowan C, Turturro S, Larsen PE, Kovacs B, Witmer PD, Valle D, Zack DJ, Nicholson DA, Xu S. Inactivation of the microRNA-183/96/182 cluster results in syndromic retinal degeneration. Proc Natl Acad Sci U S A. 2013 Jan 22. [Epub ahead of print].
  7. Peter E Larsen and Frank R Collart. BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data. BMC Research Notes 2012, 5:275. [HIGHLY ACCESSED]
  8. Peter E. Larsen, Sean M. Gibbons and Jack A. Gilbert. Modeling Microbial Community Structure and Functional Diversity Across Time And Space. FEMS Microbiology Letters. Accepted manuscript online: 3 MAY 2012 09:14PM EST | DOI: 10.1111/j.1574-6968.2012.02588.x.
  9. Larsen PE, Field D, Gilbert JA. Predicting bacterial community assemblages using an artificial neural network approach. Nat Methods. 2012 Apr 15. doi: 10.1038/nmeth.1975. [Manuscript reviewed in Nature Methods, News and Views]
  10. Larsen P, Hamada Y, Gilbert J. Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction. J Biotechnol. 2012 Mar 23.
  11. Larsen PE, Collart F, Field D, Meyer F, Keegan KP, Henry CS, McGrath J, Quinn J, Gilbert JA. 2011. Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset. Microbial Informatics and Experimentation 2011, 1:4. [HIGHLY ACCESSED]
  12. Havel VE, Wool NK, Ayad D, Downey KM, Wilson CF, Larsen P, Djordjevic JT, Panepinto JC. Ccr4 Promotes Resolution of the ER Stress Response during Host Temperature Adaptation in Cryptococcus neoformans. Eukaryot Cell. 2011 May 20.
  13. Peter E Larsen, Avinash Sreedasyam, Geetika Trivedi, Gopi K Podila, Leland J Cseke and Frank R Collart. Using Next Generation Transcriptome Sequencing to Predict an Ectomycorrhizal Metabolome. BMC Systems Biology (2011), 5:70. [HIGHLY ACCESSED]
  14. Adler A, Park YD, Larsen P, Nagarajan V, Wollenberg K, Qiu J, Myers TG, Williamson PR. A novel specificity protein 1 (SP1)-like gene, regulating protein kinase C-1 (PKc1)-dependent cell-wall integrity and virulence factors in Cryptococcus neoformans. J Biol Chem. 2011 Apr 12.
  15. Henry CS, Overbeek R, Xia F, Best AA, Glass E, Gilbert J, Larsen P, Edwards R, Disz T, Meyer F, Vonstein V, Dejongh M, Bartels D, Desai N, D'Souza M, Devoid S, Keegan KP, Olson R, Wilke A, Wilkening J, Stevens RL. Connecting genotype to phenotype in the era of high-throughput sequencing. Biochim Biophys Acta. 2011 Mar 21.
  16. Peter Larsen, Frank Collart and Yang Dai, "Incorporating network topology improves prediction of protein interaction networks from transcriptomic data". International Journal of Knowledge discovery and Bioinformatics, 1(3), pp.1-19. 2010.
  17. Park YD, Panepinto J, Shin S, Larsen P, Giles S, Williamson PR. Mating pheromone in Cryptococcus neoformans is regulated by a transcriptional/degradative "futile" cycle. J Biol Chem. 2010 Nov 5;285(45):34746-56. Epub 2010 Aug 27.
  18. Peter E Larsen, Trivedi G, Sreedasyam A, Lu V, Podila GK, Collart FR. Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome. PLoS One. 2010 Jul 6;5(7):e9780.
  19. Peter Larsen and Yang Dai, Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks, Proceeding of the 5th International Symposium on Bioinformatics Research and Applications (eds. I. Mandoiu, G. Narasimhan, and Y. Zhang), Lecture Notes in Bioinformatics, Springer Verlag, Vol. 5542 (2009) pp. 40-51, 2009.
  20. Kedar Kulkarni, Peter Larsen and Andreas A. Linninger, “Assessing chronic liver toxicity based on relative gene expression data”, Journal of Theoretical Biology (2008), doi:10.1016/j.jtbi.2008.05.032.
  21. Peter Larsen, Eyad Almasri, Guanrao Chen and Yang Dai,” Incorporating Knowledge of Topology Improves Reconstruction of Interaction Networks from Microarray Data”, Lecture Notes in Bioinformatics, Vol. 4983 ( I.I. Mandoiu, Raj Sunderraman, and A. Xelikovsky), Springer Verlag, pp. 434-443, 2008.
  22. Eyad Almasri, Peter Larsen, Guanrao Chen and Yang Dai, “Incorporating Literature Knowledge in Baysian Network for Inferring Gene Networks with Gene Expression Data”, Lecture Notes in Bioinformatics, Vol. 4983 (eds. by I.I. Mandoiu, Raj Sunderraman, and A. Xelikovsky), Springer Verlag, pp. 184-195, 2008.
  23. Guanrao Chen, Peter Larsen, Eyad Almasri, Yang Dai, “Rank-based edge reconstruction for scale-free genetic regulatory networks”, BMC Bioinformatics (2008), 9:75.
  24. Peter Larsen, Eyad Almasri, Guanrao Chen, Yang Dai, “A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments”, BMC Bioinformatics (2007), 8:317. [HIGHLY ACCESSED]
  25. Peter Larsen, E. Almasri, G. Chen and Y. Dai, "Correlated discretized expression score: a method for identifying gene interaction networks from time course microarray expression data" Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp. 5842-5845.
  26. G. Chen, P. Larsen, E. Almasri and Y. Dai, "Sample scale-free gene regulatory network using gene ontology", Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp.5523-5526.
  27. Robert Folberg, Zarema Arbieva, Jonas Moses, Amin Hayee, Tone Sandal, ShriHari Kadkol, Amy Lin, Klara Valyi-Nagy, Suman Setty, Lu Leach, Patricia Chevez-Barrios, Peter Larsen, Dibyen Majumdar, Jacob Pe'er, Andrew Maniotis. “The generation of vasculogenic mimicry patterns dampens the invasive melanoma cell genotype and phenotype”. Am J Pathol (2006), 166:1187-203.
  28. Hessler, PE, PE Larsen, AI Constantinou, KH Schram, and JM Weber. “Isolation of isoflavones from soy-based fermentations of the erythromycin-producing bacterium Saccharopolyspora erythraea”Appl. Microbiol. Biotechnol.  1997 47(4) P398-404.

> Book Chapters:

  1. Peter E. Larsen, Frank R. Collart, Yang Dai. Predicting Bacterial Community Assemblages using an Artificial Neural Network Approach. Artificial Neural Networks: Methods and Applications, Springer, New York [in press].
  2. Leland J. Cseke, Stan D. Wullschleger, Avinash Shreedasyam, Geetika Trivedi, Peter Larsen, Frank Collart. Chapter 12: Carbon Sequestration. Genomics & Breeding for Climate-Resilient Crops (ed. Chittaranjan Kole). Springer, New York [In press].
  3. Andreas Wilke, Peter Larsen, Jack A Gilbert. Next Generation Sequencing and the Future of Microbial Metagenomics. Horizon Scientific Press / Caister Academic Press [In press].
  4. Peter Larsen, Leland Cseke, Frank R Collart. Using Next Generation Transcriptome Sequencing to Predict an Ectomycorrhizal Metabolome. Molecular Microbial Ecology of the Rhizosphere. (Frans J. de Bruijn ed.), INRA/CNRS Laboratory of Plant-Microbe Interactions. 2013.
  5. Yang Dai, Eyad Almasri, Peter Larsen, Guanrao Chen, Structure Learning of Genetic Regulatory Networks Based on Knowledge Derived from Literature and Microarray Gene Expression Measurements, Book Chapter, Computational Methodologies in Gene Regulatory Networks ,(S. Das, D. Caragea, W. H. Hsu, S. M. Welch eds.), IGI Global, pp.289-309, 2009.

> Patents Issued

  • J Mark Weber, Peter E. Larsen, Mihn B. Luu, and Paul  Hessler.  1998.  Method and Composition for Enhancing Erythromycin Production.  U.S. Serial # 08/852,401.

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