Please join the eScience Institute Monday, January 28, 4:00 pm in EEB-303.
Refreshments will be provided.
John Gennari (UW)
John Gennari, PhD, received his doctorate in Computer Science (in
artificial intelligence) in 1990, and has been carrying out research in
biomedical informatics since 1994, starting with the Stanford Medical
Informatics group. His primary research focus is in knowledge
representation and knowledge sharing. John is extensively published in the
Biomedical Informatics literature, in application areas as diverse as
health care guidelines, biosimulation modeling and cell-signaling pathways.
Dr. Gennari joined the BHI faculty in 2002, and began working in synthetic
biology in 2009 in collaboration with Herb Sauro.
Versions and Variants: Adopting engineering principles to synthetic biology
Synthetic biology is a unique interdisciplinary field that combines a deep
understanding of genome-level biology, a technical ability to manipulate
and synthesize genes at the wet bench, and an engineering approach to the
design and development of novel constructs. As the field grows and matures,
it needs to apply principles from engineering, such as standardization and
modularity, to manage complexity. If synthetic biology components were
standardized and modular, then researchers could access libraries of
reusable components for adaptation and re-use in new designs, accelerating
the pace of science. Our research group has worked over the last several
years to help develop the Synthetic Biology Open Language (SBOL), a
standard for sharing and reusing synthetic biology designs. In this talk, I
report on several early successes with this RDF-based standard.
In addition to broad principles such as modularity, we have discovered that
day-to-day research in synthetic biology would also benefit from a more
specific software engineering idea: Version control management. In
collaboration with Herbert Sauro’s lab, we have developed prototype tools
for applying version control to the complexities of the wet-bench process
of biological construction. Ultimately, we aim to create intelligent CAD
tools that allow researchers to not only leverage standards and reusable
libraries of components, but also to carry out intelligent component
retrieval, error detection, workflow management, and other sorts of
decision support for the design and assembly of new biological constructs.
* February 13, 4 PM (EE303)
*Gary Johnson *
Waiting for Exascale
* March 13, 4 PM (EE303)
*Carlos Guestrin *(UW)
GraphLab: Making Fast Machine Learning on Big Data
Accessible to Data Scientists
* April 11, 4 PM (EE303)
*Barry Wark *(Physion Consulting)
* May 1, 4 PM (EE303)
*Jeff Gardner *(UW)
Simulating the Universe on Google’s Exacycle Platform
* May 13, 4 PM (EE303)
*Fernando Perez *(Berkeley)