BEACON is a $25 million NSF Science and Technology Center headquartered at Michigan State University that celebrates its second anniversary in August 2020. It includes 5 partner universities and over 400 affiliated researchers, students, and faculty studying evolution across diverse fields including microbiology, robotics, genetics, zoology, and plant biology. The mission of BEACON is to illuminate and harness the power of evolution through research in biological evolution, digital evolution, and evolutionary applications to advance science, technology, and benefit society. Researchers at BEACON generate large amounts of genomic and simulation data that challenge traditional computing and require new algorithmic approaches. BEACON aims to train students in data-intensive biology and develop robust

1. BEACON
Center for the Study of Evolution in Action
An NSF Science and Technology Center
Headquartered at Michigan State University
Funded in 2010, at $25 million for first five years –
Celebrating our second anniversary in August, funding
expected through 2020
5 Partner Universities, 131 Faculty Members
42 Postdocs – 180 Graduate/47 UG Students;
445 people total
Diverse Research – Microbiology, Robotic
Swarms, Genetic Algorithms, Zoology, Computational
Evolution, Plant Biology, and many other areas

2. MISSION: Illuminating and harnessing the power of
evolution in action to advance science and technology
and benefit society
Three cross-cutting themes of BEACON research
Biological Evolution Digital Evolution Evolutionary Applications

3. Extreme compute: Avida
• Avida: digital model for evolution
Self-replicating computer programs
• 100k CPU days per PhD thesis
• ~100 GB of data per run to analyze
Much less to preserve/archive
• Low memory: < 1 GB of RAM/run
• GPGPU not useful
• Data archiving: community & university standards
• Bottom line:
Fairly traditional compute use (more cores, mo’ better!)

4. Extreme data: NGS data
• Sequencing non-model organisms & communities
– Soil metagenomics
– Non-model animal transcriptomics
• $10k sequencing/week => 1 TB of data
Assembly requires 2x bigmem machine-weeks (512+ GB of RAM)
• RAM and I/O limited.
• “Big graph” problem with no locality
Not easily distributable.
• Estimate 5-50 Tbp of sequence needed/sample
~1m genomes/sample
…multiple samples/thesis.
• Community & university data archiving stds.
Growth in sequencing capacity is outpacing Moore’s Law;
new algorithmic approaches needed.

5. Our efforts
1. Training
- Biology has become data-intensive quite quickly!
- Most biologists are not trained in effective use of computation.
- Grad students are extremely motivated!
- We run intro courses & many focused workshops:
Intro grad course; Software Carpentry (Sloan); Analyzing Next-
Gen Sequencing Data (NIH); metagenomics.
2. Well-integrated layer of “cyberinfrastructure research”
- Faculty research programs, labs incorporate development of robust
community of software for modeling, simulation, data analysis.
- Algorithm research is tightly integrated with biological research
programs; e.g. novel compression approaches provide significant
leverage on next-gen sequencing problems.
- Exploration & adaptation to loosely coupled, poor I/O platforms (i.e.
the Amazon cloud) to enable flexible extension of compute capacity.
- …underappreciated, underfunded.