A method for identifying a modulator of a disease process, said method comprising: establishing a disease model for the disease process, the disease model comprising disease cells or disease-associated cells; obtaining a first data set from the disease model, wherein the first data set represents expression levels of a plurality of genes in the disease cells or disease-associated cells; obtaining a second data set from the disease model, wherein the second data set represents a functional activity or a cellular response of the disease cells or disease-associated cells; generating a consensus causal relationship network based on an ensemble of Bayesian networks of causal relationships among 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 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; generating a differential causal relationship network from comparison of the consensus causal relationship network based on data from the disease model with a comparison causal relationship network based on comparison cell data from comparison cells; and identifying a causal relationship unique in the disease process as part of the differential causal relationship network that is uniquely present in the consensus causal relationship network based on data from the disease model including disease cells or disease-associated cells and absent in the comparison causal relationship network based on the comparison cell data from the comparison cells, wherein the identified causal relationship unique in the disease process is connected to a parent node representing an input variable and a child node representing an input variable, wherein the parent node and the child node are connected by an edge of a directed acyclic graph representing the disease model, wherein the input variable for the parent node or the input variable for the child node corresponds to a gene, which is a gene associated with the identified unique causal relationship, and wherein the gene associated with the unique causal relationship is identified as a modulator of the disease process, wherein the ensemble of Bayesian networks on which the consensus causal relationship network is based includes quantitative probabilistic directional information regarding the causal relationships among the expression levels of the plurality of genes and the functional activity or cellular response; wherein the disease process is cancer, diabetes, obesity or cardiovascular disease; wherein the disease cells or disease-associated cells in the disease model are subjected to one or more disease-relevant environmental stimuli and are subjected to an environmental perturbation, which is any external physical and/or chemical stimulus that may affect cellular function; where the comparison cell data is from comparison cells that are subjected to one or more disease-relevant environmental stimuli and are not subjected to the environmental perturbation; and wherein the comparison cells are untreated cells or unperturbed cells. establishing a disease model for the disease process, the disease model comprising disease cells or disease-associated cells; obtaining a first data set from the disease model, wherein the first data set represents expression levels of a plurality of genes in the disease cells or disease-associated cells; obtaining a second data set from the disease model, wherein the second data set represents a functional activity or a cellular response of the disease cells or disease-associated cells; generating a consensus causal relationship network based on an ensemble of Bayesian networks of causal relationships among 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 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; generating a differential causal relationship network from comparison of the consensus causal relationship network based on data from the disease model with a comparison causal relationship network based on comparison cell data from comparison cells; and identifying a causal relationship unique in the disease process as part of the differential causal relationship network that is uniquely present in the consensus causal relationship network based on data from the disease model including disease cells or disease-associated cells and absent in the comparison causal relationship network based on the comparison cell data from the comparison cells, wherein the identified causal relationship unique in the disease process is connected to a parent node representing an input variable and a child node representing an input variable, wherein the parent node and the child node are connected by an edge of a directed acyclic graph representing the disease model, wherein the input variable for the parent node or the input variable for the child node corresponds to a gene, which is a gene associated with the identified unique causal relationship, and wherein the gene associated with the unique causal relationship is identified as a modulator of the disease process,