Interrogatory cell-based assays and uses thereof

Patent metadata

Field	Value
Publication number	US-10061887-B2
Application number	US-201213607587-A
Country	US
Kind code	B2
Filing date	Sep 7, 2012
Priority date	Apr 2, 2012
Publication date	Aug 28, 2018
Grant date	Aug 28, 2018

Abstract

Described herein are methods for identifying a modulator of a biological system. Some methods include establishing a model for the biological system comprising cells, obtaining a first data set representing global proteomic in the cells, and obtaining a second data set representing one or more functional activities or cellular responses of the cells including global enzymatic activity and/or an effect of the global enzyme activity on the enzyme metabolites or substrates in the cells. The method also includes generating a consensus causal relationship network among the global proteomic changes and the one or more functional activities or cellular responses based solely on the first and second data sets using a programmed computing device, and identifying a causal relationship unique in the biological system from the consensus causal relationship network, wherein at least one enzyme associated with the unique causal relationship is identified as a modulator of the biological system.

First claim

The invention claimed is:

1. A method for identifying a modulator of a biological system, the method comprising:
(1) obtaining a first data set from a model for the biological system, wherein the model comprises cells associated with the biological system, and wherein the first data set represents global proteomic changes in the cells associated with the biological system;
(2) obtaining a second data set from the model, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with the biological system, and wherein said one or more functional activities or cellular responses of the cells comprises global enzymatic activity and/or an effect of the global enzyme activity on the enzyme metabolites or substrates in the cells associated with the biological system;
(3) generating a computer implemented first causal relationship network model among the global proteomic changes and the one or more functional activities or cellular responses based solely on the first and second data sets using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein generating the computer-implemented first causal relationship network model comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the global proteomic changes, and the one or more functional activities or cellular responses of the cells associated with the biological system including the global enzymatic activity and/or effect of the global enzyme activity on the enzyme metabolites or substrates in the cells associated with the biological system;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, and wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network;
wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first and second data sets;

(4) generating a computer-implemented differential causal relationship network from the first causal relationship network model and a second computer-implemented causal relationship network model based on control cell data using a computing device by steps including:
(i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
(ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and

(5) identifying a causal relationship unique in the biological system from the differential causal relationship network, wherein at least one enzyme associated with the unique causal relationship is identified as a modulator of the biological system.

2. The method of claim 1 , wherein the first data set further represents lipidomic data characterizing the cells associated with the biological system.

3. The method of claim 2 , wherein the first causal relationship network model is generated among the global proteomic changes, lipidomic data, and the one or more functional activities or cellular responses of the cells, and wherein said one or more functional activities or cellular responses of the cells comprises global enzymatic activity and/or the effect of the global enzymatic activity on at least one enzyme metabolite or substrate.

4. The method of claim 1 , wherein the first data set further represents one or more of lipidomic, metabolomic, transcriptomic, genomic and SNP data characterizing the cells associated with the biological system.

5. The method of claim 4 , wherein the first causal relationship network model is generated among the global proteomic changes, the one or more of lipidomic, metabolomic, transcriptomic, genomic, and SNP data, and the one or more functional activities or cellular responses of the cells, wherein said one or more functional activities or cellular responses of the cells comprises global enzymatic activity and/or the effect of the global enzymatic activity on at least one enzyme metabolite or substrate and further comprises one or more of bioenergetics, cell proliferation, apoptosis, organellar function, cell migration, tube formation, chemotaxis, extracellular matrix degradation, sprouting, and a genotype-phenotype association actualized by functional models selected from ATP, ROS, OXPHOS, and Seahorse assays.

6. The method of claim 1 , wherein the global enzyme activity comprises global kinase activity.

7. The method of claim 1 , wherein the effect of the global enzyme activity on the enzyme metabolites or substrates is associated with the phospho proteome of the cells.

8. The method of claim 1 , wherein the second data set representing one or more functional activities or cellular responses of the cells further comprises one or more of bioenergetics, cell proliferation, apoptosis, organellar function, cell migration, tube formation, chemotaxis, extracellular matrix degradation, sprouting, and a genotype-phenotype association actualized by functional models selected from ATP, ROS, OXPHOS, and Seahorse assays.

9. The method of claim 1 , wherein the model of the biological system comprises an in vitro culture of cells associated with the biological system, optionally further comprising a matching in vitro culture of control cells.

10. The method of claim 9 , wherein the in vitro culture of the cells is subject to an environmental perturbation, and the in vitro culture of the matching control cells includes identical cells not subject to the environmental perturbation.

11. The method of claim 10 , wherein the environmental perturbation comprises one or more of contact with a bioactive agent, a change in culture condition, introduction of a genetic modification/mutation, and introduction of a vehicle that causes a genetic modification/mutation.

12. The method of claim 10 , wherein the environmental perturbation comprises contacting the cells with an enzymatic activity inhibitor.

13. The method of claim 12 , where

Assignees

• Vishnudas Vivek K
• Sarangarajan Rangaprasad
• Narain Niven Rajin
• Du Min
• Walshe Tony
• Berg Llc

Inventors

• Vishnudas Vivek K
• Sarangarajan Rangaprasad
• Narain Niven Rajin
• Du Min
• Walshe Tony

Classifications

• A61P35/00

  Antineoplastic agents · CPC title

• A61P3/10

  for hyperglycaemia, e.g. antidiabetics · CPC title

• A61P35/02

  specific for leukemia · CPC title

• A61P9/10

  for treating ischaemic or atherosclerotic diseases, e.g. antianginal drugs, coronary vasodilators, drugs for myocardial infarction, retinopathy, cerebrovascula insufficiency, renal arteriosclerosis · CPC title

• A61P9/00

  Drugs for disorders of the cardiovascular system · CPC title