The goal of the Biological Distributed Algorithms (BDA) workshop is to bring together computer scientists and biologists interested in studying the relationships between distributed algorithms and distributed biological systems, including ant colonies, slime molds, the immune system, and the brain. The workshop will include invited talks together with oral and poster presentations selected by the workshop's program committee.
This is the 4th BDA meeting. This year, the workshop will be co-located with a premiere distributed computing conference, PODC. In 2015, BDA was held as a stand-alone 2-day workshop at MIT in Boston, MA. In 2013 and 2014, BDA was co-located with another premiere distributed computing conference, DISC, in Austin, TX and Jerusalem, Israel, respectively. Each year, we have seen growing attendance and submissions.
The funds sought will be used to provide travel support for 10-15 students and post-docs, including women and minorities, to attend and present their work (oral or poster) at the workshop. Since young researchers, especially students and postdocs, tend to be the driving force behind successful interdisciplinary collaborations, we will dedicate most of the slots in the upcoming BDA meeting to works presented by students and postdocs. Our experience in the past BDA meetings shows that students and postdocs often have difficulty securing travel funds for such events, therefore we will encourage their participation by offering them generous travel grants. We hope to introduce young researchers to this exciting and emerging area of cross-disciplinary research, and help them establish collaborations for the future.
Distributed systems are prevalent in computer science and biology. Both domains rely on networks of interacting entities - molecules, cells, and organisms in biology; sensor, mobile, and autonomous robot devices in computer science - to coordinate output responses to inputs, route resources, and navigate through environments. Both types of systems also seek solutions to these problems that are inherently efficient, robust, and adaptive with respect to changes expected in natural and competitive environments. These connections suggest that by thinking computationally about the settings, requirements, and goals of information processing within distributed biological systems, we can achieve two complementary goals: First, we can use insights about how biological systems solve computational problems to aid in designing more flexible, robust, and adaptive distributed algorithms. Second, we can use, develop, or adapt existing distributed computing theory to help understand the behavior of biological systems and propose new, testable hypotheses to determine how well such models fit the observed behavior of the biological system. By bringing together both biologists and computer scientists, we aim to cross-fertilize the important and timely ideas that both domains can offer to one another.
