Abstract: This disclosure describes techniques for generating a visual path through a metabolic network, techniques include receiving reaction data for a starting metabolite, the reaction data comprising one or more potential reaction steps of the starting metabolite with one or more neighbor metabolites, generating from the one or more neighbor metabolites, a precursor metabolite list of precursor metabolites and a successor metabolite list of successor metabolites based on the reaction data, generating a user interface comprising the visual path that includes interactive visual data, the interactive visual data indicating each metabolite from the precursor metabolite list and the successor metabolite list, wherein each metabolite from the precursor metabolite list and the successor metabolite list is selectable and indicates a corresponding potential reaction step of the one or more potential reaction steps of the starting metabolite, and outputting the user interface for display at a display device.

Abstract: Biological and chemical systems are represented as a symbolic model. Minimal sets of elements of the system are identified from the model, for example, by automatic inference. The model can be constructed from Boolean propositions that are mapped to a binary decision diagram. The model can also be probed using a branch and bound algorithm.

Abstract: Disclosed are methods, software, and systems for comparing biopolymer sequences. The model includes at least two different characterizations of states of matching between segments of sequences at defined positions. Examples of states of matching include: similarity and dissimilarity between objects, as well as similarity to a reference, e.g., a reference sequence or a sequence profile. A topology of particular match states can be used to identify classes of sequences, e.g., preprohormone sequences.

Abstract: A model is used to represent a set of structured data objects that include elements at defined positions. The model includes distributions of vectors, each distribution corresponding to particular positions in the respective structured data objects, each of the vectors comprising values for the particular positions; and comparing a given set of structured data objects to the model to determine a likelihood that the given set is represented by the model. At least some of the distributions of the model differ such that different states of matching are indicated. Distributions of the model can indicate: dissimilarity between the structured data objects at defined positions; similarity between the structured data objects at defined positions; or similarity to a reference structure data object at defined positions.

Abstract: A model is used to represent a set of structured data objects that include elements at defined positions. The model includes distributions of vectors, each distribution corresponding to particular positions in the respective structured data objects, each of the vectors comprising values for the particular positions; and comparing a given set of structured data objects to the model to determine a likelihood that the given set is represented by the model. At least some of the distributions of the model differ such that different states of matching are indicated. Distributions of the model can indicate: dissimilarity between the structured data objects at defined positions; similarity between the structured data objects at defined positions; or similarity to a reference structure data object at defined positions.