Life ComputesLife Computes: A simultaneous: A simultaneous

Eliot Bush, Zachary Dodds, Ran Libeskind-Hadas, Harvey Mudd College

Curriculum

If CS had a central dogma, it would probably be the infinite loop:

while task not complete: work() We introduce looping structures such as this one so that students can implement transcription and translation. We extend these to find introns/exons probabilistically. This background allows students to tackle simple folding problems in module IV.

Weekly schedule

We examined BioCS1 students' performance on three final exam questions shared by all CS 1 students and three homework questions shared by Biology 1 students. The results show that the combined curriculum does not serve students poorly relative to traditional paths in each field.

Spurred by the growth of bioinformatics, many advanced courses in undergraduate Biology and CS curricula explore those fields' overlapping problems and ideas. Believing that tomorrow's Bio and CS fields will be even more interconnected than today's, we have created a novel introductory curriculum that can serve as Biology 1 and CS 1 for majors of either (or both) disciplines. We have evaluated this 18-month experiment and made its materials publically available for others to share.

Head-to-head comparisons

AcknowledgmentsWe gratefully acknowledge support by the Howard

Hughes Medical Institute, the National Science Foundation and funding from Harvey Mudd College.

The course begins with the biochemical context of cells and computational insights into nucleotide and protein spaces. Variables, list structures, and conditionals are motivated by -- and help to investigate -- these concepts both quantitatively and qualitatively.

Motivation Results

Module I: Biochem & CS Context

We organized this BioCS1 course to treat both subjects with equal weight. A biology professor lectures first each week; a CS lecture follows. Fri. labs and weekend HW reinforce the week's CS and Bio ideas via student programs.

Fri. afternoon labs require additional energy transport

We have followed the pilot population of 28 students through BioCS1 and their decisions following it. Whenever possible we have compared their feelings about the course and the results of their work with controls in the standard curriculum.

Student affect and continuing interest

Data show that students are as or more happy in BioCS1 as in traditional alternatives: we await students' major choices in May.

Module II: Central Dogmas

Module III: Modeling

Module IV: Algorithms

Module V: Final Projects

A student with no prior CS wrote this recursive program

in week 9 of the course.

Students investigate fixation via self-coded populations

Students write programs to model chemotaxic motion in Python turtle graphics

Within the first third of the term students implement the basic models of genetic information flow.

Students used 2d cellular automata to model ommatidia

growth and HIV's spread

Others created physical models of chemotaxis via hand-built tethered robots

Using Python and its libraries, even students without any background can and do program their own population dynamics and molecular evolution models, which are then used to investigate allele fixation and frequencies.

Recursion and iteration receive equal attention: the former enables quick prototyping of algorithms, such as RNA folding, which later are optimized via memoization or DP.

Materials and resources

All of our curricular resources, labs, assignments, support materials and lecture slides are freely available at this URL.

http://www.cs.hmc.edu/twiki/bin/view/CS6

3-week final projects cap students' BioCS1 work. All fundamentally mix CS and biology to solve a task.

BioCS1 students are comparable in performance to Bio1 and CS1 students on end-of-term assessments. Note that these

necessarily omit the material unique to BioCS1!

gradient

curriculum for Biology1 and CS1curriculum for Biology1 and CS1