The CompBio group includes students and faculty from biology, computer science, and mathematics from Reed College, a liberal arts school in Portland, Oregon. It is led by Dr. Anna Ritz, a computer scientist and Associate Professor of Biology at Reed.
Kat Thompson ’20 – Computer Science Major
Jiarong Li ’21 – Math/Computer Science Major
Tayla Isensee ’21 – Biology Major
Usman Hafeez ’21 – Computer Science Major
Tunc Kose ’22 – Computational Biology Major
The projects described on this post are related to other faculty research in the Biology Department at Reed College. Dr. Derek Applewhite is a cell biologist who studies cell morphology (Dr. Ritz and Dr. Applewhite also have an NSF RUI grant to study cell contractility). Dr. Kara Cerveny is a developmental biologist who studies retina development. Dr. Sam Fey is a community ecologist that studies aquatic ecosystems. Other blog posts may mention research by neurobiologist Dr. Erik Zornik, behavioral neuroscientist Dr. Suzy Renn, and computer scientist Dr. Mark Hopkins.
The contributors of this blog are supported by different funding sources. Refer to Dr. Ritz’s website for more information.
Collaborative Research Experience for Undergraduates (CREU)
This blog grew out of a weekly journal as part of a collaborative REU with Dr. Derek Applewhite and students Alex King & Miriam Bern. The REU was sponsored by the Computing Research Association Committee on the Status of Women in Computing Research (CRA-W), in collaboration with the iAAMCS AYUR program. Both CREU and iAAMCS are funded by the National Science Foundation.
M. J. Murdock Charitable Trust College Research Program for the Natural Sciences (2016-2019)
This project supported undergraduate summer researchers to computationally study dysregulated signaling in colorectal cancer.
National Science Foundation (NSF) CAREER Award (2018-2023)
Many projects fall under Dr. Ritz’s NSF grant Network-Based Signaling Pathway Analysis: Methods and Tools for Turning Theory into Practice. This project aims to expand network-based modeling to analyzing signaling pathways in disease and non-human systems. The grant also has a major aim that builds on hypergraph theory as an alternative representation to network-based methods.