Category Archives: Lab Members

This sections shows all the lab members.

Daniel Cuevas

Jeremy Frank

 

 

 

Daniel Cuevas space

Location: GMCS 429

Phone: +1 619 594-3137

Email: jfrank@sciences.sdsu.edu

Jeremy’s lab blog

BIO:

I grew up in North County San Diego, attending SDSU as an undergraduate. During that time, working in biotech opened my eyes to the exciting field of microbiology. I got my first experiences in academic research working with Stan Maloy learning bacterial genetics during my last two years at SDSU. During grad school, I transitioned into computational biology and microbial ecology working with Gary Olsen at UIUC. After obtaining my Ph.D., I decided to try something completely different and moved to Denmark to work on host-virus system of the extremophile archaeon Sulfolobus islandicus (grows at 80ºC/176ºF, pH 2…about the same as our stomach), which as its name implies, comes from acidic hot springs in Iceland. After travelling in Europe for a few years, I came back to the US to work at the interface of systems and reductionist biology with two great players in the field, Rob Edwards and Forest Rohwer.

POSITION:

Postdoc, joint between the labs of Rob Edwards and Forest Rohwer

RESEARCH:
Bacterial viruses (phage) are the most abundant and diverse biological entities on Earth (phage abundance/diversity is ~10x greater than that of prokaryotes, which in turn are ~100x greater than that of eukaryotes). Phage preform a vital role in environmental nutrient cycles through how they interact with their bacterial hosts. These interactions can take one of a few different routes, both ultimately culminating in the replication of phage. During bacterial infection, phage modulate their host’s metabolism and energy dynamics, which can result in changing host population size by either causing outright death of the host or providing a physiological advantage promoting host survival.
Even with an understanding of the importance, abundance and diversity of phage and their effect on nutrient cycling, we know relatively little about phage gene content; roughly 70% of all phage genes are of unknown function. Of the genes that have been characterized, they can be classified into one of three different functional groups: (i) phage replication, (ii) phage structural proteins, (iii) proteins that affect host metabolism. Genes involved in phage replication tend to be well conserved and are relatively easy to identify based on sequence similarity searches, while phage structural and metabolic genes tend to be more variable. Computational methods (e.g., artificial neural networks) are being employed to identify potential structural proteins, to much success, while metabolic proteins require more wet lab-based identification methodologies. Through working with Drs. Rob Edwards, Forest Rohwer and Anca Segall, I have developed a pipeline to predict functions for phage metabolic genes through the combination of physiological methods (phenotype microarrays and metabolomics) and genomics (metabolic modeling and metabolic networks) by creating a computational interface between biological and genomic information. Functional predictions are subsequently verified through the use of bacterial genetics and physiology.
RESEARCH:

Bacterial viruses (phage) are the most abundant and diverse biological entities on Earth (phage abundance/diversity is ~10x greater than that of prokaryotes, which in turn are ~100x greater than that of eukaryotes). Phage preform a vital role in environmental nutrient cycles through how they interact with their bacterial hosts. These interactions can take one of a few different routes, both ultimately culminating in the replication of phage. During bacterial infection, phage modulate their host’s metabolism and energy dynamics, which can result in changing host population size by either causing outright death of the host or providing a physiological advantage promoting host survival.
Even with an understanding of the importance, abundance and diversity of phage and their effect on nutrient cycling, we know relatively little about phage gene content; roughly 70% of all phage genes are of unknown function. Of the genes that have been characterized, they can be classified into one of three different functional groups: (i) phage replication, (ii) phage structural proteins, (iii) proteins that affect host metabolism. Genes involved in phage replication tend to be well conserved and are relatively easy to identify based on sequence similarity searches, while phage structural and metabolic genes tend to be more variable. Computational methods (e.g., artificial neural networks) are being employed to identify potential structural proteins, to much success, while metabolic proteins require more wet lab-based identification methodologies. Through working with Drs. Rob Edwards, Forest Rohwer and Anca Segall, I have developed a pipeline to predict functions for phage metabolic genes through the combination of physiological methods (phenotype microarrays and metabolomics) and genomics (metabolic modeling and metabolic networks) by creating a computational interface between biological and genomic information. Functional predictions are subsequently verified through the use of bacterial genetics and physiology.

PUBLICATIONS:

Frank, J. A. and S. J. Sørensen. 2011. Quantitative Metagenomic Analyses Based on Average Genome Size Normalization. Applied and Environmental Microbiology. 77:2513-21.
Guo, L., K. Brügger, L. Chao, S. A. Shah, H. Zheng, Y. Zhu, S. Wang, R. Lillesøl, L. Chen, J. Frank, D. Prangishvili, L. Paulin, Q. She, L. Huang and R. A. Garrett. 2011. Genome Analyses of Strains of Sulfolobus islandicus Model Organisms for Genetic and Virus- Host Interaction Studies. Journal of Bacteriology. 193:1672-80.
Rivera, A. J., J. A. Frank, R.Stumpf, B. A. Wilson, G. J. Olsen and A. A. Salyers. 2011. Inter-individual variation of the baboon vaginal ecosystem. American Journal of Primatology. 73:119-126.
Kim, T. K., S. M. Thomas, M. Ho, S. Sharma, C. I. Reich, J. A. Frank, K. M. Yeater, D. Biggs, N. Nakamura, R. Stumpf, S. R. Leigh, R. I. Tapping, S. R. Blanke, J. M. Slauch, H. R. Gaskins, J. S. Weisbaum, G. J. Olsen, L. L. Hoyer and B. A. Wilson. 2009. Heterogeneity of Vaginal Microbial Communities Within Individuals. Journal of Clinical Microbiology. 61:761-766.
Frank, J. A., C. I. Reich, S. Sharma, J. S. Weisbaum, B. A. Wilson and G. J. Olsen. 2008. Critical Evaluation of Two Primers Commonly Used for Amplification of Bacterial 16S rRNA Genes. Applied and Environmental Microbiology. 74: 2461-2470.

EDUCATION:

B.Sc. in Microbiology, San Diego State University, 2004

M.Sc. in Microbiology, University of Illinois Urbana-Champaign, 2006

Ph.D. in Microbiology, University of Illinois Urbana-Champaign, 2008

Postdoc, University of Copenhagen, 2009-2011

 

Daniel Cuevas

space

Location: GMCS 429

Phone: +1 619 594-3137

Email: dcuevas08@gmail.com

Daniel’s lab blog

 
About Me

I am a graduate student in the Bioinformatics and Medical Informatics department at San Diego State University. I have been working at Edwards Lab since June 2009 but have lived in the beautiful city of San Diego all my life. During my free time I enjoy sitting out in the sun, hanging out at the beach, playing sports, and reading up on new and awesome technologies on the web.

 

Industry Experience
  • Life Technologies – Ion Torrent R&D — Software Engineer / Bioinformatics

 

Education
  • Bachelor of Science in Computer Science. San Diego State University.

 

Research

SEED web services (for Java)

  • The SEED holds many publicly available genome DNA sequences which are annotated based on subsystems and protein functions. It also provides web services to those who are willing to develop applications based on the SEED API. By supplying many methods to obtain these microbial annotations, developers can use, manipulate, and create third party applications to aid their scientific research. My assignment involved learning to use and access the SEED API using the Java programming language. Through this I have written different examples to perform specific functions with the SEED database.

 

RTMg.os

  • OpenSocial Metagenomics (RTMg.os), is an OpenSocial-based application that is part of the RTMg suite of applications. RTMg.os allows the users of social networking sites to share their bioinformatic data and analysis with colleagues and friends. The application stores and retrieves metagenome-annotated data on a server. This allows the user to notify and update their friends about any new annotations that have been acquired, and then display the contents of their data in a tabular format. RTMg.os represents an entirely new way for scientists to collaborate on their data. For information on the entire RTMg project, go here.

 

Rapid Sequence Searching Using A Hashing Technique

  • By implementing a modified data structure to quickly and precisely discover exact nucleotide similarities between several sequenced metagenomes, specific DNA sequences can be analyzed and traced within different environmental regions.

 

Presentations 

Oral presentations:

  • San Diego State University Student Research Symposium (SDSU SRS), San Diego, CA, 2010
  • San Diego State University Student Research Symposium (SDSU SRS), San Diego, CA, 2011

Poster presentations:

  • General meeting of the American Society for Microbiology (ASM), San Diego, CA, 2010
  • San Diego Microbiology Group (SDMG) All-day meeting, San Diego, CA, 2010
  • Applied Computational Science and Engineering Student Support (ACSESS), San Diego, CA, 2010