Sandra L. Rodriguez-Zas, Ph.D. - US grants

One of the most important questions in biology is the origin of behavior: nature or nurture? This research will use genomic biology to liberate the study of behavior from the shackles of this dichotomy. The new paradigm is that the environment ("nurture"), which includes other individuals, impacts an inherited genome ("nature") by orchestrating gene expression during the lifetime of the animal. This project will analyze social behavior on an unprecedented whole-genome scale, using Apis mellifera the Western honey bee, as the model organism. Honey bees live in a complex society governed by an age-related division of labor, with each individual assuming many roles during her lifetime. Both genetic heredity and environmental conditions determine what role a bee performs, and when she performs it. The biology research will generate a unique database of gene expressions for all social behavior, recording brain gene expression for hundreds of individuals, each with a specific societal role. These microarray experiments utilize the recently sequenced genome, supported by state-of-the-art statistics. The informatics research will develop an interactive environment to analyze all information sources relevant to bee social behavior. These include genome databases from honey bee and related organisms, linked to complete scientific literature relevant to insect behavior. New text mining technology will integrate molecular description with information from physiology, behavior, neuroscience, and evolution. The BeeSpace environment will enable users to navigate a uniform space of diverse databases and literature sources for hypothesis development and testing. The software system will go beyond a searchable database, using statistical literature analyses to discover functional relationships between genes and behavior. This research will enable all scientists who study bee genes to live on the frontier of integrative biology, where biotechnology enables routine expression analysis and bioinformatics enables functional analysis unconstrained by pre-existing categories.

The broader impact of the interactive environment for functional analysis will be tested in an international community of laboratories studying honey bees and related organisms. Outreach for BeeSpace will provide integrated research and education experiences at the graduate and undergraduate levels, plus training courses and minority outreach at high school and middle school levels.

Recent advances in Natural Language Processing, in particular the ability to use unstructured data to answer natural language questions, are very exciting from an educational perspective. They offer the promise of systems that can automatically respond to students' questions, thus supporting not only a guided but also an open ended, exploration based, approach to learning.

Developing software that supports students' learning is all about constructing the right kind of environment for students, one that facilitates rather than inhibits inquiry through a known knowledge space and provides a jumping-off space for trying to find or generate new knowledge.

The goal of this project is to apply research in Computer Science -- particularly Natural Language Processing -- and the Learning Sciences, to developing an intelligent tutor that can provide this needed environment. This tutor will inhabit a human-computer interactive environment in which the computer is able to detect and track the user's cognitive and academic state and act based on this knowledge to aid the student in identifying and accessing relevant knowledge, contribute relevant factual information the student may need and guide the student in selecting potentially relevant subtasks.

The testbed domain in this project involves high school and undergraduate level students studying concepts in Bioinformatics -- building on the enormous amounts of biological data and software made freely available on the Web by the Bioinformatics community, and specifically, making use of the Biology Workbench system developed at NCSA.

In the context of this project, researchers will (1) develop the necessary machine learning, natural language and inference methods that can robustly support a level of natural language understanding that is sufficient to ``understand'' students and their queries well enough to direct it to the right material, make relevant suggestions and develop a meaningful dialog in the context of the subject matter; (2) create a system that is able to accommodate different student backgrounds and goals and behave appropriately, and learn as it does so; and (3) study how students learn and how to support students' learning in a computer-aided context.

This project will contribute to the understanding of how students learn in a computer aided environment and use it to develop improved methods for supporting learning in these environments. This has the potential for large educational impact for large classes, distance education, and self-paced instruction. The project's computational results in areas such as natural language based human machine interaction, adaptive dialog management, user-sensitive information retrieval and extraction, and machine learning, would be widely applicable to many other domains, including intelligent information access and interactive support systems for senior citizens and other groups.

An award has been made to the University of Illinois at Urbana-Champaign (UIUC) to establish an Undergraduate Research and Mentoring (URM) program. The program will provide one-year research experiences in quantitative biology and informatics to four cohorts of scholars, starting in the summer of 2011 and continuing through 2015. Each cohort consists of seven students who will explore problems using integrative quantitative inquiry. This program seeks to enhance the pool of multicultural and multidisciplinary workforce of scientists and professionals by offering experiential learning opportunities in computational and wet laboratories, as well as academic and career development activities, to community college students. Recruitment will be through collaborating Hispanic-serving institutions. The program will also provide long-term mentoring and will encourage family support and participation. Students will be able to select from topics in plant and animal bioinformatics, quantitative genetics and plant breeding, statistical genomics, architecture of complex traits, food nanotechnology, biological system modeling, and statistics for agriculture. Students will participate in (1) a 12-week, summer, intensive research immersion experience, (2) mentor-guided academic year research, and (3) an optional second summer internship experience. The research will be complemented with academic and career advancement, network development, and support activities. Project evaluation will assess the quality of research, oral presentations, and student and mentor interactions, as well as skills assessment using pre and post test instruments and student feedback on the program. More information is available at http://www.aces.uiuc.edu/Academics/Diversity/URM, or by contacting (Gustavo Caetano-Anolles (gca@uiuc.edu), Sandra Rodriguez-Zas (rodrgzzs@uiuc.edu), Jesse Thompson (jthomps5@uiuc.edu) or Maria Villamil (villamil@uiuc.edu).

This Integrative Graduate Education and Research Traineeship (IGERT) award is built around two grand challenges in biology: 1) How do genomes interact with the environment to produce biological diversity? and 2) How are biological systems integrated from molecules to ecosystems? Our training model is aimed at preparing students so that they are empowered to learn how an organism?s traits emerge from, and are continually shaped by, a complex interplay of genetic information stored in DNA and environmental information that an organism experiences throughout its life. This training will equip students with the knowledge, tools and perspectives needed to address pressing scientific and societal problems, including: effects of climate change on agriculture and food security; responses of organisms and ecosystems to anthropogenic influences on landscapes; emergence of infectious diseases, and influences of genes on behavior.

This proposal uses a ?back-to-the-future? educational model that asserts that the best way to use genomics to address grand challenges in biology is to have a graduate program that blends state-of-the-art training in genomics with an integrated, taxon-oriented, perspective. Our guiding principle is that to be able to use powerful new genomic resources as effectively as possible, students need to have strong foundation in the basic biology of the organisms they are studying. Another cornerstone of this IGERT is the recruiting and mentoring of underrepresented minorities into a scientific discipline. Because genomic topics often deal with provocative issues, students will also get training in scientific ethics and on how to communicate sensitive topics to the public.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.