I believe plants are the most important life forms on earth. They give us the air we breathe, clothes we wear, houses we live in, energy we burn and drugs that cure our diseases or make us feel better. Plants will give us all of these until we last and then outlive us all. Despite all this, we know very little about how they do what they do. Even for the best-studied species, Arabidopsis thaliana (a wild mustard), we know about less than 20% of what its genes do and how or why they do it.
We want to uncover the molecular mechanisms underlying adaptive traits in plants to understand how these traits evolved. A bottleneck in achieving our goals is the limited understanding of the functions of most genes in plant genomes. With a sequenced genome as a starting point, we are building genome-wide molecular networks of genes and proteins using a combination of computational and empirical approaches. Using these networks, we want to elucidate functions of uncharacterized genes rapidly and systematically. Ultimately we are interested in finding patterns of network evolution to identify the evolutionary paths of functional innovation for adaptation.
The questions that we are pursuing are:
* Why are plants so robust to genetic and environmental perturbations and how do they express this resilience?
* How is plant metabolism wired and how does it evolve?
The approaches and projects we are developing to answer the questions are:
Computational framework to predict metabolic networks of plants
Reconstruction of co-function networks in plants
Identification of genome-wide genetic interaction network of plants
Empirical testing of plant metabolic networks using genetic and metabolomic approaches
Novel method of measuring functional similarities
Identification of all genes involved in complex traits such as salt tolerance
Computational and empirical identification of signaling pathways and complexes
Identification of novel classes of transcription factor regulators
Characterization of novel gene families
We employ several methods in our quest: 1) combination of computational modeling and targeted experimental testing in the lab; 2) systematic collection of large-scale data needed for the modeling through collaboration with other labs; 3) robust, quantitative analysis of the data and the models. Our work is inherently embedded in collaborations with other labs both at Carnegie and other institutions.
This is my vision of our mode of operation. Our own lab is most invested in the synthesis aspect of the ‘research engine’ with a growing component on experimentation. But we collaborate with many excellent labs in all three aspects.
My group has two types of trainees: postdoctoral researchers who are in their last leg of training before launching their own groups or establishing independence in other ways, and research assistants who typically come right after college or a masters program without much, if any, research experience. Besides these opposite ends of the training spectrum, everyone has his or her unique personality, style of learning, and background. So I try to adjust the style and type of training according to the individual.
My goal is to foster an environment for the trainee to become an independent, effective, and successful leader. I strive to help them learn how to identify interesting, solvable problems and foster an engaging environment for them to learn and apply innovative methods to solve the problems. One way I do this is by having an interdisciplinary group where people with different training and cultural backgrounds come and develop a variety of projects. The synergism we have achieved through this is evident in our lively group meetings that typically last for 2-2½ hours where essentially everyone brings up interesting suggestions and ideas.
I recruit postdocs who not only have clear potential but also have areas that I feel I can definitively help improve during their tenures in my lab. In addition to conducting phone and in-person interviews, I request a document outlining a plan of research projects, with which I communicate extensively by email before I make an offer. I feel that each new member that comes to my lab is special and unique and I feel a sense of duty to see him/her flourish intellectually and personally during his/her tenure in my lab. I advise postdocs on career development informally throughout their tenure, but formally during the interview and after 1 year into the tenure. Towards the end of the first year, I ask the postdocs to come up with a list of their strengths and weaknesses and we discuss these points, particularly with a focus on improving the weaknesses catered for their career directions.
I emphasize training postdocs on effective writing and speaking. The postdocs typically participate in writing one grant proposal during his/her tenure and write at least one first-author publication in which he/she leads the process from beginning to end, with my guidance. They also have ample opportunities in preparing and delivering presentations of their work. Informally, each postdoc gives presentations during weekly group meetings (6-8 people). There are additional opportunities for giving presentations, such as the department-wide post-doc only ‘round table’ meetings (~20-30 people) and the biweekly internal seminar series attended by the whole department (~70-80 people). Typically postdocs in my group give ~6-8 presentations/year. Much is gained also by listening to others speak about their research and, being located at a university, we have access to a number of cutting-edge seminar series. At Carnegie, postdocs have an opportunity to meet with the invited seminar speakers during lunch and post-seminar reception. My postdocs typically attend one scientific conference/year. Presenting a poster or an oral presentation is highly encouraged.
Finally, my postdocs gain opportunities to learn how to mentor and advise students who come through our lab as summer interns or voluntary research assistants. Typically 3-4 summer interns and research assistants visit my lab per year.
Besides training people in the science, I consider training of the scientist to be equally important. I value and try to foster qualities such as intellectual generosity, intellectual integrity, creativity and vision. I believe these qualities distinguish a great scientist from a good scientist. Intellectual generosity does not merely stop at being generous in sharing results and reagents, but also ideas, constructive criticisms and empathy in other people’s problems. The best way to learn this, in my view, is by examples from role models. I am surrounded by colleagues and mentors with many of these qualities and I encourage people in my group to seek, recognize and appreciate such qualities in their peers, advisors, mentors and other scientists. Intellectual integrity is another quality I emphasize, mostly to the beginning researchers. When one is starting out in designing and performing experiments, it is very easy for one’s enthusiasm to overshadow the absolute objectivity required. I see this time and again where if students are aware of the expected result, they tend not to ‘see’ the unexpected result. I am sure this behavior is not always conscious. But that makes it even more critical to emphasize intellectual honesty. I also believe there are ways of fostering creativity and vision, but will not dwell on these further here.
Papers from our group are highly cited with a current H-Index (as of April 2013) of 34 on Google Scholar (See ciation page). Some of our papers are listed below.
Chen J, Lalonde S, Obrdlik P, Noorani Vatani A, Parsa SA, Vilariño C, Revuelta JL, Frommer WB and Rhee SY (2012) Uncovering Arabidopsis membrane protein interactome enriched in transporters using mating-based split ubiquitin assays and classification models. Frontiers in Plant Science 3(124): 1-14. [pdf]
Hwang S, Rhee SY, Marcotte, and Lee I (2011) Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network. Nature Protocols. 6(9):1429-1442. [pdf]
Lee I, Ambaru B, Thakkar P, Marcotte E, and Rhee SY (2010) Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nature Biotechnology. 2(28):149-156. [pdf][supplemental info]
Zhang P, Dreher K, Karthikeyan A, Chi A, Pujar A, Caspi R, Karp P, Kirkup V, Latendresse M, Lee C, Mueller LA, Muller R, Rhee SY (2010) Creation of a genome-wide metabolic pathway database for Populus trichocarpa using a new approach for reconstruction and curation of metabolic pathways for plants. Plant Physiology. 153(4):1479-91. [pdf]
Chen J, Ji L, Hsu W, Tan K-L, and Rhee SY (2009) Exploiting Domain Knowledge to Improve Biological Significance of Biclusters with Key Missing Genes. IEEE Technical Committee on Data Engineering Conference. ICED.2009.205: 1219 - 1222 [pdf]
Bassel GW, Gaudinier A, Brady SM, Hennig L, Rhee SY, and Smet ID (2012) Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks. The Plant Cell. October 2012, doi: http://dx.doi.org/10.1105/tpc.112.100776[pdf]
Chae L, Lee I, Shin J, Rhee SY (2012) Towards understanding how molecular networks evolve in plants. Current Opinion in Plant Biology. 15:177-184. [pdf]
Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, Hill DP, Kania R, Schaeffer M, St. Pierre S, Twigger S, White O, Rhee SY (2008) The future of biocuration. Nature. 455:47-50. [pdf]
Rhee SY, Wood V, Dolinski K and Draghici S. (2008) Use and Misuse of the Gene Ontology (GO) Annotations. Nature Review Genetics. 9(7):509-15. [pdf]
Rhee SY, Dickerson, J, and Xu, D (2006) Bioinformatics and its Applications in Plant Biology. Annual Review of Plant Biology. 57: 335-360 [pdf]
Bard, JL and Rhee, SY (2004) Ontologies in biology: design, applications and future challenges. Nature Review Genetics 5(3):213-22. [pdf]
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