New technological and experimental advances have resulted in the massive accumulation of biological measurements, requiring computational analysis to facilitate data interpretation. I develop algorithms to analyze complex biological datasets that draw from core areas of computer science and mathematics.

Computational Systems Biology: Signaling Pathway Analysis

Signaling pathways describe the series of reactions that occur when a cell receives an external stimulus and elicits a downstream transcriptional response. I develop methods to computationally analyze signaling pathways in manually-curated databases such as KEGG, Reactome, NetPath, and SPIKE. One aspect of my work in signaling pathway analysis involves automatically reconstructing human signaling pathways from protein-protein interaction (PPI) data. I found that the notion of complexes, complex rearrangement, and regulatory interactions cannot be accurately described by directed graphs due to their inherent pairwise nature. In another line of research, I formalize and design algorithms for signaling hypergraphs, a reaction-centric representation that better captures the complexity of complex assembly and regulation found in signaling pathways.

Other Research: Structural Variant Detection

I study large rearrangements of DNA in human genomes called structural variants (SVs) which are an important contributor to genetic variation. SVs are also associated with a host of cancers, where somatic SVs -- variants that are acquired within an individual's lifetime -- are important for determining putative driver mutations. I have developed algorithms that identify SVs from different experimental data. To accommodate new third-generation sequencing platforms, I formalized a multi-linked read that generalizes the concept of paired reads. I developed first an integer linear program and later a Markov Chain Monte Carlo (MCMC) method for SV detection from multi-linked reads based on the sequencing platforms introduced by Pacific Biosciences. In the context of cancer biology, I developed an algorithm to identify recurrent fusion genes, where an SV combines two genes into a single, "hybrid" gene. This work formed the foundation of my Ph.D. dissertation, and I continue to work in this area with collaborators at other institutions.

Research Posters

Interested in doing research with me? Many of the research projects described below have been explored as student thesis or summer research topics. The posters below provide a sense of undergraduate research I advise.

ACM-BCB 2017
Ibrahim Youssef and Anna Ritz. Breaking Ties in Weighted Interactomes.
Nicholas Franzese, Barney Potter, Adam Groce, James Fix, and Anna Ritz. Hyperpath Relaxations for Signaling Pathway Analysis.
ACM-BCB 2016
Karl Menzel, Suzy CP Renn, and Anna Ritz. Copy Number Variation and Adaptive Evolutionary Radiations across the African Cichlid phylogeny.
Barney Potter, James Fix, and Anna Ritz. Modeling Cell Signaling Networks with Prize-Collecting Subhypernetworks. Winner of the Best Poster Award.
Nicole Ezell and Anna Ritz. Reconstructing Neuronal Signaling Pathways With the Potential for Disruption in Schizophrenia.

ritz portrait


Biology Department
Reed College
3203 SE Woodstock Blvd.
Portland, OR 97202-8199

Email icon aritz-at-reed-dot-edu

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