Method of generating a dynamic pathway map
A method of generating a dynamic pathway map (DPM) is provided. The method includes accessing a model database that stores a probabilistic pathway model that comprises a plurality of pathway elements, a first number of the plurality of pathway elements are cross-correlated and assigned an influence level for at least one pathway on the basis of known attributes, a second number of the plurality of pathway elements are cross-correlated and assigned an influence level for at least one pathway on the basis of assumed attributes. The method includes measuring a patient sample to identify measured attributes of the patient sample and using a plurality of the measured attributes of the patient sample, via an analysis engine, to modify the probabilistic pathway model to obtain the DPM, wherein the DPM has reference pathway activity information for a particular pathway, the reference pathway indicating deviations from the probabilistic pathway model.
1. A processor-based method of generating a dynamic pathway map (DPM), comprising:
accessing a model database that stores a probabilistic pathway model that comprises a plurality of pathway elements;
assigning an influence level for at least one pathway of a first number of the plurality of pathway elements on the basis of known attributes;
cross correlating the first number of the plurality of pathway elements;
assigning an influence level for at least one pathway on the basis of assumed attributes;
measuring a patient sample to identify measured attributes of the patient sample, based on a genome-scale assay;
modifying the probabilistic pathway model by using a plurality of the measured attributes for a plurality of elements of the patient sample, via an analysis engine, the modifying comprising:
obtaining a factor graph representing states of entities in a cell and interactions between the entities, wherein the factor graph encodes a state of the cell using a random variable for each entity and wherein the factor graph has reference pathway activity information for a particular pathway, the reference pathway indicating deviations from the probabilistic pathway model; and
formulating a treatment option for the patient based on the reference pathway activity of the factor graph, wherein at least one of the above method operations is performed through a processor.
2. The method of claim 1 wherein the pathway is within a regulatory pathway network, a signaling pathway network, or a network of distinct pathway networks.
3. The method of claim 1 wherein the pathway element is a protein selected from the group consisting of a receptor, a hormone binding protein, a kinase, a transcription factor, a methylase, a histone acetylase, and a histone deacetylase or a nucleic acid is selected from the group consisting of a genomic regulatory sequence, a regulatory RNA, and a trans-activating sequence.
4. The method of claim 1 wherein the reference pathway activity information is specific with respect to a normal tissue, a diseased tissue, an ageing tissue, or a recovering tissue.
5. The method of claim 1 wherein the known attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.
6. The method of claim 1 wherein the assumed attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.
7. The method of claim 1 wherein the measured attributes are selected from the group consisting of a mutation, a differential genetic sequence object, a gene copy number, a transcription level, a translation level, a protein activity, and a protein interaction.
8. The method of claim 1 , wherein modifying the probabilistic pathway model comprises:
converting the probabilistic pathway into a directed graph with each edge of the directed graph labeled with one of a positive or a negative influence.
9. The method of claim 8 further comprising:
converting each of the interactions to a single edge of the directed graph.