A method of identifying a natural product comprising NP—[X].sub.n is provided. The method includes several steps. The first step includes selecting an organism having a biosynthetic pathway for producing the natural product comprising NP—[X].sub.n using a bioinformatics algorithm. The second step includes preparing a sample suspected to contain NP—[X].sub.n including a complex cellular metabolite mixture from an organism. The third step includes reacting the sample suspected to contain NP—[X].sub.n with reactivity probe Y according to Scheme I: Scheme I. NP—[X].sub.n represents a natural product NP having a chemical moiety X that is susceptible to chemical modification by reactivity probe Y to form at least one product adduct NP—[X].sub.n-m [Z].sub.n in which chemical moiety X reacts with reactivity probe Y to form adduct Z, wherein n ranges from 1 to about 10 and m is at least 1 and m≦n. The fourth step includes optionally dereplicating the product collection of at least one known labeled metabolite to provide a depleted product collection including at least one unknown labeled metabolite. The fifth step includes determining the structure of the at least one unknown labeled metabolite, thereby identifying the natural product comprising NP—[X].sub.n.

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The present invention is applicable in the field of molecular dynamics, said invention consisting of computing methods for predicting the structure and function of biomolecules, and particularly of proteins, by means of simulating the protein folding process and the interaction process of proteins with other biomolecules in a solvent. More particularly, the invention relates to a method and a system for controlling simulation stability and for choosing the timestep used in the numerical integration of the equations of motion. The invention successfully reduces the molecular dynamics simulation time by means of optimizing the timestep choice through an adaptive control or allowing larger timesteps correcting the trajectories based on a power control.

This invention relates to systems and methods for evaluating the differentiality of a set of discrete random variables between two or more conditions, such as a malignant condition responding to treatment regime and one that is not. It also provides for the identification and selection of drugs that act in coordinated manner to phenocopy a genetic network of a malignant condition that responds to at least an immune checkpoint blockade agent.

Methods involving the use of mathematical models of competitive ligand-receptor binding to characterize mixtures of ligands in terms of compositions and properties of the component ligands have been developed. The associated mathematical equations explicitly relate component ligand physical-chemical properties and mole fractions to measurable properties of the mixture including steady state binding activity, 1/K.sub.d,apparent or equivalently 1/EC50, and kinetic rate constants k.sub.on,apparent and k.sub.off,apparent allowing; 1) component ligand physical property determination and 2) mixture property predictions. Additionally, mathematical equations accounting for combinatorial considerations associated with ligand assembly are used to compute ligand mole fractions. The utility of the methods developed is demonstrated using published experimental ligand-receptor binding data obtained from mixtures of afucosylated antibodies that bind FcγRIIIa (CD16a) to: 1) extract component ligand physical property information that has hitherto evaded researchers 2) predict experimental observations and 3) provide explanations for unresolved experimental observations.

Generation of biomolecule sequence coevolution data structures, matrices, scores, and sectors are described. Generally, the generated coevolution data removes covariant noise due to phylogenetic drift and can reveal coevolution of residue positions in multiple phylogenetic distances. Scores can be built upon the data structures and matrices to reveal sectors of residue positions that function and evolve together. Furthermore, the coevolution data structures, matrices, scores, and sectors can be used to predict structure or function of residue variants.

BioMEMS/NEMS appliance biologically monitors an individual, using biosensors to detect cellular components. Data is simulated or analyzed using systems-biology software, which provides diagnostic or therapeutic guidance.

The present invention provides systems, methods and software for improving robustness of transcriptomic data analysis, the method including receiving control cell transcriptomic data (C) and cell transcriptomic data (S) under study for a gene, calculating a fold change ratio (fc) for the gene, repeating these steps for a plurality of genes, grouping co-expressed genes into modules, estimating gene importance factors based on a network topology, mapped from a plurality of the modules and obtaining a insilico Pathway Activation Network Decomposition Analysis (iPANDA) value, wherein the iPANDA value has a Pearson coefficient greater than a Pearson coefficient associated with another platform for manipulating the same data.

A novel method to predict toxicity and dose-dependent effects of an agent based on transcriptomic data analysis, by determining a predictive toxicogenomics space (PTGS) score. The PTGS score helps to predict and model the toxicity of compounds typically consisting of chemicals, pharmaceuticals, cosmetics and agrochemicals. The invention further comprises methods of deriving the PTGS score, as well as computer programs to calculate PTGS scores.

A method of extracting organ metabolic functions using oral glucose tolerance tests (OGTTs) comprises: a step of measuring an amount of change over time in plasma glucose and plasma insulin at a regular time interval after a subject has consumed a certain quantity of a glucose solution; a step of extracting parameters associated with organ metabolism in a human body by applying the amount of change over time of the measured glucose and insulin to a human organ function model; and a step of diagnosing the metabolic function state of the subject using the extracted parameters.

A computer-implemented method for designing a biological model provides a set of biological models, each biological model comprising a plurality of elements and interactions between elements. Next the method provides groups of elements identified as identical, each element having an associated biological model. The method moves an element from a first group to a second group in order to correct the grouping of the elements; updates both groups; and creates a combined model by combining the set of biological models according to the updated groups.