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1. Introduction
New advancements
in biology (such as DNA sequencing) have generated huge quantities
of new and exciting data that promise to provide advances in disease
treatment and prevention, bioremediation, and biodiversity management.
But massive amounts of new data require novel and innovative methods
of data storage, analysis, and mining. Unfortunately, most computer
scientists are unaware of the biology problems that require computational
solutions and/or do not understand biological data well enough to know
what software tools and technologies could be applied. Furthermore,
biologists are rarely trained in computer science, and thus, have difficulty
applying complex computational methodologies to biological problems.
Consequently, much of the vital new biological information is underutilized,
and many problems remain unanswered.
The impact of this
impediment is even more pronounced when considering biodiversity questions.
There is a tremendous urgency to identify those
factors that result in major impacts on biodiversity, especially in light
of rapid species declines and extinctions. Biologists are collecting
vast amounts of data on the impact of global warming, the consequences
of environmental contamination, the destruction of natural resources,
and the cause of population declines. The development of new computational
methods would allow these data to be used more efficiently to model global
patterns of environmental and species change and understand biocomplexity
and the environment, with the ultimate goal of protecting and preserving
biodiversity.
Fortunately, bioinformatics,
the field devoted to developing computational solutions to expand the
use of biological data, is one of the fastest
growing subfields in biology. In fact, recently both the National Science
Foundation and the National Institute of Health identified bioinformatics
as “the essential underpinning of all biological fields in the
21st century” (NIH-NSF, 2002). Some have even argued that in the
very near future, most competitive life scientists will possess expertise
in both biological and computer science. (Park, 2001).
Unfortunately, the largest barrier to training the next generation of
bioinformaticians is that few students are attracted to both biology
and computer science. The problem is even more obvious in biodiversity
informatics where the data is collected in the field and the computational
methods are derived in the computer laboratory.
Here we describe an ongoing project designed to give students practical
training in both computer science and biology, and to foster their interest
in the integration of both fields. The project follows the new paradigm
of science education in which: 1) students perform real science as they
construct meaning and acquire understanding; 2) students develop thinking
processes and are encouraged to seek answers that enhance their knowledge
and acquire an understanding of the physical universe in which they live;
and 3) students are presented with problem-solving activities that incorporate
authentic, real-life questions and generalization to broader ideas and
applications (Christiansen, 1995).
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