NZ712105B2 - Interrogatory cell-based assays and uses thereof - Google Patents

Abstract

method for identifying a modulator of a biological system comprises (1) obtaining a first data set representing measured expression levels of a plurality of genes in cells associated with the biological system; (2) obtaining a first control data set representing measured expression levels of a plurality of genes in control cells; (3) obtaining a second data set from the cells associated with the biological system representing a measured functional activity or a measured cellular response of the cells associated with the biological system; (4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the control cells; (5) generating a first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system, wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set; (6) generating a second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set; (7) generating a differential causal relationship network from the first causal relationship network model and the second causal relationship network model; and (8) identifying a causal relationship unique in the biological system from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the biological system. rality of genes in control cells; (3) obtaining a second data set from the cells associated with the biological system representing a measured functional activity or a measured cellular response of the cells associated with the biological system; (4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the control cells; (5) generating a first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system, wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set; (6) generating a second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set; (7) generating a differential causal relationship network from the first causal relationship network model and the second causal relationship network model; and (8) identifying a causal relationship unique in the biological system from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the biological system.

Description

In certain embodiments of the various methods, the consensus causal relationship network is generated among a first data set and second data set obtained from the model for the biological system, wherein the model comprises cells associated with the biological system, and wherein the first data set represents expression levels of a plurality of genes in the cells and the second data set represents a functional activity or a cellular response of the cells, using a programmed computing device, wherein the generation of the consensus causal relationship network is not based on any known biological relationships other than the first data set and the second data set.

In another aspect, the invention relates to a method for identifying a modulator of a biological system, said method comprising: (1) obtaining a first data set representing measured expression levels of a plurality of genes in cells associated with the biological system; (2) obtaining a first control data set representing measured expression levels of a plurality of genes in control cells; (3) obtaining a second data set from the cells associated with the biological system representing a measured functional activity or a measured cellular response of the cells associated with the biological system; (4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the control cells; (5) generating a first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system, wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set; (6) generating a second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set; (7) generating a differential causal relationship network from the first causal relationship network model and the second causal relationship network model; and (8) identifying a causal relationship unique in the biological system from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the biological system.

In another aspect, the invention relates to a method for providing a disease model for use in a platform method, comprising: establishing a disease model for a disease process, using disease related cells, to represent a characteristic aspect of the disease process, wherein the disease model is useful for generating disease model data sets used in the platform method; thereby providing a disease model for use in a platform method. 12934549MAE (12934549_1):HJG In another aspect, the invention relates to a method for obtaining a first data set and second data set from a disease model for use in a platform method, comprising: (1) obtaining a first data set from a disease model for use in a platform method, wherein the disease model comprises disease related cells, and wherein the first data set represents expression levels of a plurality of genes in the disease related cells; (2) obtaining a second data set from the disease model for use in a platform method, wherein the second data set represents a functional activity or a cellular response of the disease related cells; thereby obtaining a first data set and second data set from the disease model; thereby obtaining a first data set and second data set from a disease model for use in a platform method.

In another aspect, the invention relates to a method for identifying a modulator of a disease process, said method comprising: (1) generating a consensus causal relationship network among a first data set and second data set obtained from a disease model, wherein the disease model comprises disease cells, and wherein the first data set represents expression levels of a plurality of genes in the disease related cells and the second data set represents a functional activity or a cellular response of the disease related cells, using a programmed computing device, wherein the generation of the consensus causal relationship network is not based on any known biological relationships other than the first data set and the second data set; (2) identifying, from the consensus causal relationship network, a causal relationship unique in the disease 12934549MAE (12934549_1):HJG Figure 8: Schematic representation of the systematic interrogation using MIMS and collection of response data from the "omics" cascade.

Figure 9: Sketch of the components employed to build the In vitro models representing normal and diabetic states.

Figure 10: Schematic representation of the informatics platform REFS™ used to generate causal networks of the protein as they relate to disease pathophysiology.

Figure 11: Schematic representation of the approach towards generation of differential network in diabetic versus normal states and diabetic nodes that are restored to normal states by treatment with MIMS. In this schematic representation of (Normal vs.

Diabetic) Vs. (Diabetic T1 vs. Diabetic), the unique edges of Normal in the normal vs. disease delta network are compared with the unique edges of Disease T1 in the disease vs. disease T1 delta network. Edges in the intersection of normal and disease T1 are disease edges that were restored to normal when treated with T1.

Figure 12: A representative differential network in diabetic versus normal states.

Figure 13: A schematic representation of a node and associated edges of interest (Node1in the center). The cellular functionality associated with each edge is represented.

Figure 14: High level flow chart of an exemplary method, in accordance with some embodiments.

Figure 15A-15D: High level schematic illustration of the components and process for an AI-based informatics system that may be used with exemplary embodiments.

Figure 16: Flow chart of process in AI-based informatics system that may be used with some exemplary embodiments.

Figure 17: Schematically depicts an exemplary computing environment suitable for practicing exemplary embodiments taught herein.

Figure 18: Illustration of case study design described in Example 1.

Figure 19: Effect of CoQ10 treatments on downstream nodes.

Figure 20: CoQ10 treatment decreases expression of LDHA in cancer cell line HepG2.

Figure 21: Exemplary protein interaction consensus network at 70% fragment frequency based on data from Paca2, HepG2 and THLE2 cell lines. (10822248_1):GGG Figure 22: Proteins responsive to LDHA expression simulation in two cancer cell lines were identified using the platform technology.

Figure 23: Ingenuity Pathway Assist® analysis of LDHA – PARK7 network identifies TP53 as upstream hub. (10822248_1):GGG

Claims (34)

WE CLAIM:

1. A method for identifying a modulator of a biological system, said method comprising: (1) obtaining a first data set representing measured expression levels of a plurality of genes in cells associated with the biological system; (2) obtaining a first control data set representing measured expression levels of a plurality of genes in control cells; (3) obtaining a second data set from the cells associated with the biological system representing a measured functional activity or a measured cellular response of the cells associated with the biological system; (4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the control cells; (5) generating a first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system, wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set; (6) generating a second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set; (7) generating a differential causal relationship network from the first causal relationship network model and the second causal relationship network model; and (8) identifying a causal relationship unique in the biological system from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the biological system.

2. The method of claim 1, wherein the biological system is a disease process, and wherein the cells associated with the biological system are disease related cells.

3. The method of claim 1 or 2, wherein the generated first causal relationship network model is refined via in silico simulation based on the input data to provide a confidence level of prediction for one or more causal relationships within the first causal relationship network model.

4. The method of claim 1 or 2, further comprising generating a delta-delta causal relationship network based on the differential causal relationship network and a second differential causal relationship network generated solely based on data obtained from comparison cells. #12934340 AH26(12934340_1):HJG

5. The method of claim 4, wherein the comparison cells are normal cells.

6. The method of claim 1 or 2, wherein the first causal relationship network model is based on a consensus network model produced by evolving an ensemble of trial networks, and wherein generating the first causal relationship network model comprises: determining a Bayesian probabilistic score for each network fragment in a set of network fragments based on the first data set and the second data set; creating an ensemble of trial networks, each trial network constructed from a different subset of the set of network fragments; and evolving each trial network through local transformations resulting in an ensemble of evolved trial networks forming a consensus relationship network.

7. The method of claim 6, wherein generating the first causal relationship network model further comprises: applying simulated perturbations to each node in the consensus network model while observing the effects on other nodes to obtain information regarding directionality of each relationship in the consensus network model; and applying the obtained information regarding directionality of each relationship to the consensus network model to obtain the first causal relationship network model.

8. The method of claim 1 or 2, wherein the first causal relationship network model and the second causal relationship network model are both Bayesian networks.

9. The method of claim 2, wherein the disease process is cancer, diabetes, obesity or cardiovascular disease.

10. The method of claim 9, wherein the cancer is lung cancer, breast cancer, prostate cancer, melanoma, squamous cell carcinoma, colorectal cancer, pancreatic cancer, thyroid cancer, endometrial cancer, bladder cancer, kidney cancer, a solid tumor, leukemia, non-Hodgkin lymphoma, or a drug- resistant cancer.

11. The method of claim 1, wherein the modulator stimulates or promotes the biological system.

12. The method of claim 1, wherein the modulator inhibits the biological system.

13. The method of claim 2, wherein the modulator stimulates or promotes the disease process. #12934340 AH26(12934340_1):HJG

14. The method of claim 2, wherein the modulator inhibits the disease process.

15. The method of claim 2, wherein the modulator shifts an energy metabolic pathway specifically in disease related cells from a glycolytic pathway towards an oxidative phosphorylation pathway.

16. The method of claim 1, wherein the cells associated with the biological system are subject to an environmental perturbation and the control cells are not subject to the environmental perturbation.

17. The method of claim 2, wherein the disease related cells are subject to an environmental perturbation and the control cells are not subject to the environmental perturbation.

18. The method of claim 16 or 17, wherein the environmental perturbation comprises one or more of a contact with an agent, a change in culture condition, an introduced genetic modification or mutation, and a vehicle that causes a genetic modification or mutation.

19. The method of claim 1 or 2, wherein the first data set comprises protein and/or mRNA expression levels of the plurality of genes.

20. The method of claim 1 or 2, wherein the first control data set comprises protein and/or mRNA expression levels of the plurality of genes.

21. The method of claim 1 or 2, wherein the first data set further comprises one or more of lipidomics data, metabolomics data, transcriptomics data, and single nucleotide polymorphism (SNP) data.

22. The method of claim 1 or 2, wherein the first control data set further comprises one or more of lipidomics data, metabolomics data, transcriptomics data, and single nucleotide polymorphism (SNP) data.

23. The method of claim 1 or 2, wherein the second data set comprises data indicative of one or more of bioenergetics profiling, cell proliferation, apoptosis, organellar function, a level of Adenosine Triphosphate (ATP), a level of Reactive Oxygen Species (ROS), a level of Oxidative Phosphorylation (OXPHOS), a level of Oxygen Consumption Rate (OCR) and a level of Extra Cellular Acidification Rate (ECAR).

24. The method of claim 1 or 2, wherein the second control data set comprises data indicative of one or more of bioenergetics profiling, cell proliferation, apoptosis, organellar function, a level of Adenosine Triphosphate (ATP), a level of Reactive Oxygen Species (ROS), a level of Oxidative Phosphorylation #12934340 AH26(12934340_1):HJG (OXPHOS), a level of Oxygen Consumption Rate (OCR) and a level of Extra Cellular Acidification Rate (ECAR).

25. The method of claim 1 or 2, wherein steps of (5) and (6) are carried out by an artificial intelligence (AI)-based informatics platform.

26. The method of claim 25, wherein the AI-based informatics platform is configured to generate a causal relationship network model by performing steps including the following: (a) creating a library of network fragments based on input data via a Bayesian Fragment Enumeration process; (b) creating an ensemble of trial networks, each trial network constructed from a different subset of the network fragments in the library; and (c) globally optimizing the ensemble of trial networks by evolving each trial network through local transformations to produce a consensus network model, the AI-based informatics platform including a multiple processors for evolution of the trial networks in parallel; wherein the first causal relationship network model is based on the consensus network model.

27. The method of claim 25, wherein the AI-based informatics platform receives all data input from the first data set, the first control data set, the second data set and the second control data set without applying a statistical cut-off point.

28. The method of claim 1 or 2, wherein the generated first causal relationship network model is a first simulation causal relationship network model; and wherein step (5) is performed by substeps comprising: generating a first consensus causal relationship network model based solely on the first data set and the second data set; and refining, by in silico simulation based on input data, the first consensus causal relationship network model to a first simulation causal relationship network model to provide a confidence level of prediction for one or more causal relationships within the first causal relationship network model.

29. The method of claim 1 or 2, further comprising validating the identified unique causal relationship in a biological system.

30. The method of claim 1 or 2, further comprising displaying a graphical representation of the generated differential causal relationship network. #12934340 AH26(12934340_1):HJG

31. The method of claim 1 or 2, further comprising storing a representation of the generating differential causal relationship network.

32. The method of claim 1 or claim 2, wherein the differential causal relationship network includes: relationships that are present in the first causal relationship network model but absent from the second causal relationship network model, or relationships that are present in the second causal relationship network model but absent from the first causal relationship network model; and relationships having at least one significantly different parameter in the first causal relationship network model than in the second causal relationship network model.

33. The method of claim 1 or 2, wherein generating the differential causal relationship network comprises steps including: i) for each relationship between two notes 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 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.

34. The method of claim 32 or claim 33, wherein the at least one significantly different parameter is a directionality of the relationship or a quantitative magnitude of the strength of the relationship.