A computer-implemented method for unbiasedly identifying at least one of a metabolic pathway altered in a disease model and a protein that alters a level of a metabolite feature in a disease model, the computer-implemented method comprising: A) obtaining a protein-protein-metabolite interactome (PPMI) network, the PPMI network including a graph with a first set of nodes, a second set of nodes, and a plurality of edges, each node of the first set of nodes representing a metabolite, each node of the second set of nodes representing a protein, and each edge of the plurality of edges connecting two nodes representing a known interaction between at least one of: a metabolite represented by a first node of the first set of nodes and a protein represented by a first node of the second set of nodes; and two proteins respectively represented by two nodes of the second set of nodes; B) identifying a difference between a first level of the metabolite feature acquired from a disease sample and a second level of the metabolite feature acquired from a control sample based at least in part on liquid chromatography and mass spectrometry; C) adding a sample node to the PPMI network, the sample node representing an unknown metabolite associated with the disease sample and having the first level of the metabolite feature; D) connecting the sample node representing the unknown metabolite with an edge to each node representing a known metabolite having a level of the metabolite feature that is within a predetermined range that includes the first level of the metabolite feature; E) assigning a penalty to each node in the PPMI network with a number of edges above a threshold so as to remove bias, each penalty being associated with at least one of: a degree of the second set of nodes; and a square degree of the first set of nodes; G) generating a recurrence score for each node and each edge in the PPMI network by applying a prize-collecting Steiner forest (PCSF) algorithm modified by adding random noise to each edge weight to the PPMI network; H) generating a family of solutions to the modified PCSF algorithm to infer a complex topology of metabolic interactions; I) performing background corrections for the family of solutions to calculate a significance of each solution so as to identify at least one of the altered metabolic pathway altered in the disease model and the protein that alters the level of the metabolite feature in the disease model; and J) transmitting an indication of the at least one of the altered metabolic pathway altered in the disease model and an indication of the protein that alters the level of the metabolite feature in the disease model for experimental validation, for identification of a therapeutic associated therewith, or both. A) obtaining a protein-protein-metabolite interactome (PPMI) network, the PPMI network including a graph with a first set of nodes, a second set of nodes, and a plurality of edges, each node of the first set of nodes representing a metabolite, each node of the second set of nodes representing a protein, and each edge of the plurality of edges connecting two nodes representing a known interaction between at least one of: a metabolite represented by a first node of the first set of nodes and a protein represented by a first node of the second set of nodes; and two proteins respectively represented by two nodes of the second set of nodes; a metabolite represented by a first node of the first set of nodes and a protein represented by a first node of the second set of nodes; and two proteins respectively represented by two nodes of the second set of nodes; B) identifying a difference between a first level of the metabolite feature acquired from a disease sample and a second level of the metabolite feature acquired from a control sample based at least in part on liquid chromatography and mass spectrometry; C) adding a sample node to the PPMI network, the sample node representing an unknown metabolite associated with the disease sample and having the first level of the metabolite feature; D) connecting the sample node representing the unknown metabolite with an edge to each node representing a known metabolite having a level of the metabolite feature that is within a predetermined range that includes the first level of the metabolite feature; E) assigning a penalty to each node in the PPMI network with a number of edges above a threshold so as to remove bias, each penalty being associated with at least one of: a degree of the second set of nodes; and a square degree of the first set of nodes; a degree of the second set of nodes; and a square degree of the first set of nodes; G) generating a recurrence score for each node and each edge in the PPMI network by applying a prize-collecting Steiner forest (PCSF) algorithm modified by adding random noise to each edge weight to the PPMI network; H) generating a family of solutions to the modified PCSF algorithm to infer a complex topology of metabolic interactions; I) performing background corrections for the family of solutions to calculate a significance of each solution so as to identify at least one of the altered metabolic pathway altered in the disease model and the protein that alters the level of the metabolite feature in the disease model; and J) transmitting an indication of the at least one of the altered metabolic pathway altered in the disease model and an indication of the protein that alters the level of the metabolite feature in the disease model for experimental validation, for identification of a therapeutic associated therewith, or both.