336 CATALYZING INQUIRY
ence majors should be exposed to engineering principles and analysis, should receive quantitative
training in a manner integrated with biological content, and should develop enough familiarity with
computer science that they can use information technology effectively in all aspects of their research.
10.2.2.3.1 EngineeringIn arguing for exposure to engineering, the report noted that the notion of func-
tion (of a device or organism) is common to both engineering and biology, but not to mathematics,
physics, or chemistry. Echoing the ideas described in Chapter 6 of this report, BIO2010 concluded:
Understanding function at the systems level requires a way of thinking that is common to many engi-
neers. An engineer takes building blocks to build a system with desired features (bottom-up). Creating
(or re-creating) function by building a complex system, and getting it to work, is the ultimate proof that
all essential building blocks and how they work in synchrony are truly understood. Getting a system to
work typically requires (a) an understanding of the fundamental building blocks, (b) knowledge of the
relation between the building blocks, (c) the system’s design, or how its components fit together in a
productive way, (d) system modeling, (e) construction of the system, and (f) testing the system and its
function(s).... Organisms can be analyzed in terms of subsystems having particular functions. To under-
stand system function in biology in a predictive and quantitative fashion, it is necessary to describe and
model how the system function results from the properties of its constituent elements.The pedagogical conclusion was clear in the report:
Understanding cells, organs, and finally animals and plants at the systems level will require that the
biologist borrow approaches from engineering, and that engineering principles are introduced early in
the education of biologists.... Students should be frequently confronted throughout their biology curric-
ulum with questions and tasks such as how they would design ‘xxx,’ and how they would test to see
whether their conceptual design actually works. [For example,] they should be asked to simulate their
system, determine its rate constants, determine regimes of stability and instability, investigate regulatory
feedback mechanisms, and other challenges.A second dimension in which engineering skills can be useful is in logistical planning. There are
many areas in biology now where it is relatively easy to conceive of an important experiment, but
drawing out the implications of the experiment involves a combinatorial explosion of analytical effort
and thus is not practical to carry out. It is entirely plausible that many important biological discoveries
will depend on both the ability to conceive an experiment and the ability to reconceive and restructure
it logistically so that it is, in fact, doable. Engineers learn to apply their fundamental scientific knowl-
edge in an environment constrained by nonscientific concerns, such as cost or logistics, and this ability
will be critically important for the biologist who must undertake the restructuring described above.
Box 10.1 provides a number of examples of engineering for life science majors.
10.2.2.3.2 Quantitative Training In its call for greater quantitative training, the BIO2010 report echoed
that of other commentators.^8 Recognizing that quantitative analysis, modeling, and prediction play
important roles in today’s biomedical research (and will do so increasingly in the future), the report
noted the importance to biology students of understanding concepts such as rate of change, modeling,
equilibrium, and stability, structure of a system, and interactions among components, and argued that
every student should acquire the ability to analyze issues arising in these contexts in some depth, using
analytical methods (including paper-and-pencil techniques) and appropriate computational tools. As
part of a necessary background, the report suggested that an appropriate course of study would include
aspects of probability, statistics, discrete models, linear algebra, calculus and differential equations,
modeling, and programming (Box 10.2).
(^8) See for example, A. Hastings and M.A. Palmer, “A Bright Future for Biologists and Mathematicians,” Science 299(5615):2003-
2004, 2003, available at http://www.biosino.org/bioinformatics/a%20bright%20future.pdf.