# Events & News

# CS Colloquium

Category | Lecture |

Date | Thursday, November 20, 2014 |

Time | 11:00 am |

Concludes | 12:15 pm |

Location | Gould-Simpson 906 |

Details | Please join us for coffee and light refreshments at 11am in Gould-Simpson 906. Faculty Host: John Kececioglu |

Speaker | Anna Ritz |

Title | PhD |

Affiliation | Virginia Tech |

## Signaling Hypergraphs

Cells communicate with each other to perform their functions within

the body. When a cell receives an external signal from the

environment, it responds with a series of molecular reactions that

alters the cell's behavior, e.g., causing it to divide, move, or

self-destruct. These reactions constitute networks called "signaling

pathways" whose alterations can cause diseases such as

cancers. Directed graphs are the most common representation of

signaling pathways, making them amenable to a wide array of

graph-theoretic algorithms. In this talk, I argue that directed graphs

are inaccurate representations of the underlying biology of signaling

reactions. I describe an alternative mathematical representation

called the "signaling hypergraph." First, I illustrate how signaling

hypergraphs overcome many limitations of graph-based

representations. Second, I present a mixed integer linear program to

solve the NP-hard shortest hyperpath problem. Finally, I apply the

algorithm to a well-known signaling pathway, and describe how the

shortest hyperpaths better represent signaling reactions than the

corresponding shortest paths in graphs. Signaling hypergraphs

exemplify how careful attention to the underlying biology can drive

developments in a largely unexplored field of computer science.

## Biography

Anna Ritz is a postdoctoral research associate in T. M. Murali's group

at Virginia Tech. She obtained her Ph.D. and Sc.M. in Computer Science

from Brown University, where she was awarded a National Science

Foundation Graduate Research Fellowship. She received a B.A. in

Computer Science at Carleton College. Dr. Ritz's postdoctoral

research areas involve analyzing and modeling human signaling

pathways, from automatically reconstructing pathways to developing

alternative mathematical representations of signaling events. Her

postdoctoral work is a departure from her Ph.D. research, where she

studied structural variation in human genomes using single-molecule

``third-generation'' sequencing technologies.