Interrogatory cell-based assays and uses thereof

A method for identifying a modulator of a disease process, said method comprising:

• (1) obtaining a first data set from disease-related cells, wherein the first data set represents measured expression levels of one or more genes in the disease-related cells, measured lipidomics data for the disease-related cells, measured metabolomics data for the disease-related cells, or a combination of the aforementioned;
• (2) obtaining a second data set from the disease-related cells, wherein the second data set represents a measured functional activity or a measured cellular response of the disease-related cells;
• (3) generating a computer-implemented first causal relationship network model relating the expression levels of the one or more genes in the disease-related cells, the lipidomics data for the disease-related cells, the metabolomics data for the disease-related cells, or the combination of the aforementioned and the functional activity or cellular response of the disease-related cells based on the first data set and the second data set 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 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 expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned and the functional activity or cellular response in the disease related cells;
• (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 relationships in the first causal relationship network model and causality in the first causal relationship network model are determined based on the first data set and the second data set and not based on previously identified or known biological relationships between variables;
• (4) identifying, from the computer-implemented causal relationship network model, a causal relationship unique in the disease process, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of a disease process.