Methods to Analyze Biological Networks

• US 20080261820A1
• Filed: 08/01/2006
• Published: 10/23/2008
• Est. Priority Date: 08/01/2005
• Status: Abandoned Application

• Current Assignee: Mount Sinai School of Medicine (Sinai Health System)
• Original Assignee: Icahn School of Medicine at Mount Sinai (The Mount Sinai Health System, Inc.)
• Inventors: Ma'ayan, Avi, Iyengar, Ravi
• Application Number:
  US11/997,632
• Publication Number:
  US 20080261820A1
• Time in Patent Office: Days
• US Class Current: 506/8
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/30 Dynamic-time models

Abstract

The present invention relates to a family of graph-theory based methods for the analysis of intracellular signaling networks created from biomedical literature using data-mining processes or acquired through high-content experiments. The methods of the present invention can be used to identify functional dynamic modules within biological networks that can be analyzed quantitatively for input/output relationships. In particular, the present invention relates to a computer-aided method for the in-silico analysis of signaling and other cellular interaction pathways to rank drug targets, identify biomarkers, predict side effects, and classify/diagnose patients.

Claims

1. A method for identifying and ranking new drug targets for a known drug from an interaction data set which comprises

a) collecting a plurality of information units, each of said units containing biochemical data describing an interaction between two interacting molecules,

b) constructing an interaction data set from said collected information units, in which each of said molecules represents a node and said interaction between said interacting molecules represents a link between two nodes,

c) storing the interaction data set in an extractable form,

d) selecting from the interaction data set a list of nodes shown to be altered in a cell upon treatment with said known drug as an algorithmic starting point,

e) applying one or more graph theory based algorithms to the interaction data set using each node in the selected list of nodes as a starting point to identify a new list of nodes, connected to each node in the selected list, through any number of interconnected nodes,

f) compiling the number of instances in which each node appears in the new list of nodes, and

g) selecting as drug targets those molecules corresponding to nodes with the highest number of instances.

2. The method of claim 1 wherein creating a list of algorithmic starting points comprises

i) obtaining experimental data from an experiment where the known drug was administered,

ii) obtaining experimental data from an experiment where the known drug was not administered, and

iii) creating a list of biomolecules that have an observable change when comparing the results of the experiment in step (i) with the experiment in step (ii).

3. The method of claim 1 which comprises collecting the information units from published literature.

4. The method of claim 1 which comprises collecting the information units from experimental data.

5. The method of claim 1 which comprises generating at least one visual or textual representation of the interaction data for the list of nodes derived from the algorithmic analysis.

6. The method of claim 1 wherein the interaction data set comprises interactions from a cellular signal transduction pathway.

7. The method of claim 1 wherein the interaction data set comprises interactions from a cellular metabolic pathway.

8. The method of claim 1 wherein the interacting molecules comprise peptides, proteins or nucleic acids.

9. The method of claim 1 wherein said list of nodes connected to the selected node is a list of potential non-therapeutic targets of said known drug.

10. The method of claim 9 wherein the non-therapeutic target is a side-effect of the known drug.

11. The method of claim 1 which comprises storing the interaction data set on a computer.

12. The method of claim 1 which comprises generating said visual or textual representations of the connectivity data on a computer.

13. The method of claim 1 which comprises performing the graph theory based algorithm on a computer.

14. The method of claim 13 wherein the graph theory based algorithm is a depth-first search algorithm.

15. A method for screening to find potential new drug targets for a known drug using an interaction data set which comprises

a) collecting a plurality of information units, each of said units containing biochemical data describing an interaction between two interacting molecules,

b) constructing an interaction data set from said collected information units, in which each of said molecules represents a node and said interaction between said interacting molecules represents a link between two nodes,

c) storing the interaction data set in an extractable form,

d) selecting from the information data set a node known to interact with said known drug as an algorithmic starting point,

e) applying one or more graph theory based algorithms to the interaction data set using the selected node as a starting point to identify a list of nodes connected to the selected node, through any number of interconnected nodes, and

f) comparing the number of interconnected nodes between the input node and each node from the list of nodes.

g) selecting as potential new drug targets those nodes having the lowest number of interconnected nodes.

16. The method of claim 15 wherein the information units are collected from published literature.

17. The method of claim 15 wherein the information units are collected from experimental data.

18. The method of claim 15 which comprises generating at least one visual or textual representation of the interaction data for the list of nodes derived from the algorithmic analysis.

19. The method of claim 15 wherein the interaction data set comprises interactions from a cellular signal transduction pathway.

20. The method of claim 15 wherein the interaction data set comprises interactions from a cellular metabolic pathway.

21. The method of claim 15 wherein the interacting molecules comprise peptides, proteins or nucleic acids.

22. The method of claim 15 wherein said list of nodes connected to the selected node is a list of potential non-therapeutic targets of said known drug.

23. The method of claim 22 wherein the non-therapeutic target is a side-effect of the known drug.

24. The method of claim 15 wherein the interaction data set is stored on a computer.

25. The method of claim 15 wherein generating visual or textual representations of the connectivity data is performed on a computer.

26. The method of claim 15 wherein the graph theory based algorithm is performed on a computer.

27. The method of claim 26 wherein the graph theory based algorithm is a depth-first search algorithm.