Elinor Velasquez
Email address: elinor@soe.ucsc.edu
Research
CS261 Final Project, Fall Quarter 2013
Project title: DESIGNING ANTI-CANCER DRUGS AND DIRECTING ANTI-CANCER DRUG THERAPY
Team members: ELINOR VELASQUEZ (UCSC) AND BEN BONGALON (UCSC EXTENSION)
Description of our project: A web application was designed and implemented as a guide to be used by clinicians when designing the best drug
therapy for a specific cancer patient, given biological data derived from the patient's tumor tissue biopsy.
Image of our project:
Details about our project:
Implementation:
The visualization component uses the GraphViz tool, http://graphviz.org/, to generate metabolic graphs. Using GraphViz brings two key benefits. First the
metabolic pathways can be described at a high level, and the tool automatically takes care of laying out the graph. Secondly, GraphViz can generate the
graphs in SVG (Scalable Vector Graphics) format. This allows the graph to be arbitrarily resized without pixelation.
After the SVG graph is created, the D3.js tool, http://d3js.org/, is used to add anno- tations, highlight pathways and manipulate the image. D3.js is a
JavaScript library for manipulating documents based on data, including SVG. The entire application is hosted on Google App Engine,
https://developers.google.com/appengine/.
Algorithm for finding sites of highest metabolic activity in the patient's metabolic pathways:
An algorithm was developed for ranking sub-paths using edge weights and the topology of the graph. The algorithm is an adaptation of Dijkstra's classic algorithm
for computing the shortest path between two nodes A and B. In our case, the node A represents the substrates for a metabolic reaction, the node B
represents the products of that metabolic reaction, and the edge between node A and B represents the enzyme used to catalyze the reaction. The algorithm
inputs the edge weights and outputs a list of sub-paths, is ranked
from the sub-path with highest metabolic activity to the sub-path with least metabolic activity.
Algorithm for pre-determining efficacy of drug pre-drug therapy:
A metric was developed to estimate the efficacy of a proposed drug therapy for a cancer patient. Current implementation of the visualization permits the
clinician to inspect the ranked list of sub-paths and select one or more sub-paths to knock out. Note that the algorithm ranking sub-paths assumes that
at least one reaction is involved in a sub-path. A reaction usually
has both upstream and downstream reactions to it. If a reaction is knocked out, the downstream reactions will all be knocked out as well as some of the
upstream reactions if the primary reaction is reversible. The current implementation of the metric considers only downstream reactions.
References:
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2. Yan, H, Parsons, DW, Jin, G, McLendon, R, Rasheed, BA, Yuan, W, et al., IDH1 and IDH2 mutations in gliomas. New England J Medicine 360: 765773 (2009).
Romero, P, Wagg, J, Green, ML, Kaiser, D, Krummenacker, M, Karp, PD., Computational prediction of human metabolic pathways from the complete human genome.
Genome Biol. 6(1): R2 (2005).
3. Thiele, et al., A community-driven global reconstruction of human metabolism. Nature Biotechnol. 31(5): 419-25 (2013).
Karr, JR, Sanghvi, JC, Macklin, DN, Gutschow, MV, Jacobs, JM, Bolival, B, Assad-Garcia, N, Glass, JI, and Covert, MW, A whole-cell computational model
predicts phenotype from genotype. Cell 150: 389401 (2012).
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Oxidative Phosphorylation, Metabolite Transport, and Electrophysiology. J. Biol. Chem. 282: 24525-24537 (2007).
5. Ralph Patrick, Kim-Anh LeCao, Melissa Davis, Bostjan Kobe and Mikael Boden, Mapping the stabilome: a novel computational method for classifying metabolic
protein stability. BMC Systems Biology 6:60 (2012).
6. Caroline C. Friedel, Lars Dlken, Zsolt Ruzsics, Ulrich H. Koszinowski and Ralf Zimmer, Conserved principles of mammalian transcriptional regulation
revealed by RNA half-life. Nucleic Acids Research 37(17): e115 (2009).
A user's guide: Not yet available
index.html,
oncomira.jpg,
style.css,
d3.v3.min.js,
kreb.svg