Methods for design of genetic circuits are provided.

Methods for design of genetic circuits are provided.

Systems and methods are provided for predicting cardiotoxicity of molecular parameters of a compound. A computer can provide as input to a machine learning algorithm the molecular parameters of the compound. The molecular parameters can include at least structural information about the compound. The machine learning algorithm can have been trained using respective molecular parameters of compounds known to have cardiotoxicity and of compounds known not to have cardiotoxicity. The computer can receive as output from the machine learning algorithm a representation of the predicted cardiotoxicity of each molecular parameter of at least a subset of the molecular parameters of the compound.

Systems and methods are provided for predicting cardiotoxicity of molecular parameters of a compound. A computer can provide as input to a machine learning algorithm the molecular parameters of the compound. The molecular parameters can include at least structural information about the compound. The machine learning algorithm can have been trained using respective molecular parameters of compounds known to have cardiotoxicity and of compounds known not to have cardiotoxicity. The computer can receive as output from the machine learning algorithm a representation of the predicted cardiotoxicity of each molecular parameter of at least a subset of the molecular parameters of the compound.

The invention relates to a computer-implemented method for predicting a genome-related feature (458) from genomic data of multiple individuals (402), the method comprising: —receiving (102) genomic marker data (434, 442) of each of the individuals, the genomic marker data being indicative of a plurality of first marker positions assigned to identified marker variants (1140-1142) and multiple second (1144) marker positions have a missing or ambiguous marker variant assignment; —computing (104) a haplotype-block library (448) comprising a plurality of haplotype-blocks (1126-1136), each haplotype-block comprising start and stop coordinates and a series of marker positions referred to as ‘comparison marker positions’ lying within the start and stop coordinates; —performing (106) a haplotype-block-guided marker imputation; —supplementing (108) the genomic marker data with the imputed marker variants; and —using the supplemented genomic marker data (454) for computationally predicting the feature (458) of the individuals.

The invention relates to a computer-implemented method for predicting a genome-related feature (458) from genomic data of multiple individuals (402), the method comprising: —receiving (102) genomic marker data (434, 442) of each of the individuals, the genomic marker data being indicative of a plurality of first marker positions assigned to identified marker variants (1140-1142) and multiple second (1144) marker positions have a missing or ambiguous marker variant assignment; —computing (104) a haplotype-block library (448) comprising a plurality of haplotype-blocks (1126-1136), each haplotype-block comprising start and stop coordinates and a series of marker positions referred to as ‘comparison marker positions’ lying within the start and stop coordinates; —performing (106) a haplotype-block-guided marker imputation; —supplementing (108) the genomic marker data with the imputed marker variants; and —using the supplemented genomic marker data (454) for computationally predicting the feature (458) of the individuals.

Methods are provided for the rational design of stable communities of microbes for benefiting the health of a host organism, including human and/or animal health. The methods describe design of microbial consortia based on providing and/or complementing key functionalities lacking or underrepresented in the microbiome of an organism having a disorder or disease as compared to healthy subjects. The consortia are designed to possess metabolic interdependencies for improved engrafting, stability and performance of the consortium. Compositions that include the designed microbial consortia are provided for treatment of disorders/diseases involving chronic inflammation, infection, and the combination of chronic inflammation and infection including inflammatory bowel disease and related disorders. The compositions are also broadly applicable for the treatment of neurological, metabolic and oncology-related conditions.

Methods are provided for the rational design of stable communities of microbes for benefiting the health of a host organism, including human and/or animal health. The methods describe design of microbial consortia based on providing and/or complementing key functionalities lacking or underrepresented in the microbiome of an organism having a disorder or disease as compared to healthy subjects. The consortia are designed to possess metabolic interdependencies for improved engrafting, stability and performance of the consortium. Compositions that include the designed microbial consortia are provided for treatment of disorders/diseases involving chronic inflammation, infection, and the combination of chronic inflammation and infection including inflammatory bowel disease and related disorders. The compositions are also broadly applicable for the treatment of neurological, metabolic and oncology-related conditions.

A system for generating a model for predicting a molecular property of a variant of a molecule is provided. For each of a plurality of variants of the molecule, the system for each structural feature, aggregates the values for the structural features of the residues of the molecule that were modified to form the variant to form a feature vector for the variant. The system assigns the value for the molecular property of the variant to the feature vector wherein the feature vector and the assigned value form training data. The system then generates the model for predicting a value for the molecular property using the training data for the plurality of variants.

A system for generating a model for predicting a molecular property of a variant of a molecule is provided. For each of a plurality of variants of the molecule, the system for each structural feature, aggregates the values for the structural features of the residues of the molecule that were modified to form the variant to form a feature vector for the variant. The system assigns the value for the molecular property of the variant to the feature vector wherein the feature vector and the assigned value form training data. The system then generates the model for predicting a value for the molecular property using the training data for the plurality of variants.