Abstract: Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.

Abstract: Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.

Abstract: Image analysis methods and apparatus are used for distinguishing live and dead cells. The methods may involve segmenting an image to identify the region(s) occupied by one or more cells and determining the presence of a particular live-dead indicator feature within the region(s). In certain embodiments, the indicator feature is a cytoskeletal component such as tubulin. Prior to producing an image for analysis, cells may be treated with a marker that highlights the live-dead indicator in the image. In the case of tubulin, the marker will co-locate with tubulin and provide a signal that is captured in the image (e.g., a fluorescent emission).

Abstract: Methods for generating models for predicting biological activity of a stimulus test population of cells are provided. The models may be used to classify or predict the effect of stimuli on cells. In certain embodiments, the methods involve receiving data comprising values for dependent variables associated with stimuli as applied to cell populations; preparing a set of cell populations based on the data received; identifying a subset of the cell populations to be used in generating a model from data associated with the subset, wherein the model is provided to predict activity of a test population.

Abstract: Methods for generating models for predicting biological activity of a stimulus test population of cells are provided. The models may be used to classify or predict the effect of stimuli on cells. In certain embodiments, the methods involve receiving data comprising values for dependent variables associated with stimuli; preparing a set of cell populations based on the data received; identifying a subset of the cell populations to be used in generating a model from data associated with the subset, wherein the model is provided to predict activity of a test population.

Abstract: A method of generating classification models to predict biological activity of a population of cells is provided. In certain embodiments, the method involves a) receiving a training set having values for independent and dependent variables associated with populations of cells; b) clustering the training set; c) randomly selecting, with replacement, clusters of cell populations to construct multiple bootstrap samples of the size of the training set; and d) generating a random forest model for each bootstrap sample, wherein the ensemble of random forest models may be used to classify the test population. Also provided are methods of predicting whether a test population of cells exhibits a pathology or biological activity. In certain embodiments, the methods involve applying data about the test population of cells to an ensemble of random forest models. The prediction may be made by aggregating the predictions of the random forest models in the ensemble.

Abstract: A method for calculating distances between stimulus response curves (e.g., dose response curves) allows classification of stimuli. The response curves show how the phenotype of one or more cells changes in response to varying levels of the stimulus. Each “point” on the curve represents quantitative phenotype or signature for cell(s) at a particular level of stimulus (e.g., dose of a therapeutic). The signatures are multivariate phenotypic representations of the cell(s). They include various features of the cell(s) obtained by image analysis. To facilitate the comparison of stimuli, distances between points on the response curves are calculated. First, the response curves may be aligned on a coordinate representing a separate distance, r, from a common point of negative control (e.g., the point where no stimulus is applied). Integration on r may be used to compute the distance between two response curves. The distance between response curves is used to classify stimuli.

Abstract: Image analysis methods and apparatus are used for distinguishing live and dead cells. The methods may involve segmenting an image to identify the region(s) occupied by one or more cells and determining the presence of a particular live-dead indicator feature within the region(s). In certain embodiments, the indicator feature is a cytoskeletal component such as tubulin. In certain embodiments, the methods may involve determining the value of an indicator expression that is based on cellular components such as DNA and/or cellular protein. Prior to producing an image for analysis, cells may be treated with a marker that highlights the live-dead indicator in the image.

Abstract: Image analysis methods and apparatus are used for distinguishing live and dead cells. The methods may involve segmenting an image to identify the region(s) occupied by one or more cells and determining the presence of a particular live-dead indicator feature within the region(s). In certain embodiments, the indicator feature is a cytoskeletal component such as tubulin. Prior to producing an image for analysis, cells may be treated with a marker that highlights the live-dead indicator in the image. In the case of tubulin, the marker will co-locate with tubulin and provide a signal that is captured in the image (e.g., a fluorescent emission).

Abstract: A method for calculating distances between stimulus response curves (e.g., dose response curves) allows classification of stimuli. The response curves show how the phenotype of one or more cells changes in response to varying levels of the stimulus. Each “point” on the curve represents quantitative phenotype or signature for cell(s) at a particular level of stimulus (e.g., dose of a therapeutic). The signatures are multivariate phenotypic representations of the cell(s). They include various features of the cell(s) obtained by image analysis. To facilitate the comparison of stimuli, distances between points on the response curves are calculated. First, the response curves may be aligned on a coordinate representing a separate distance, r, from a common point of negative control (e.g., the point where no stimulus is applied). Integration on r may be used to compute the distance between two response curves. The distance between response curves is used to classify stimuli.

Abstract: Methods, apparatus, and computer programs for investigating and characterising side effects of a treatment having an intended or on-target effect on cells are described. The method can include identifying a group of on-target cellular features of the plurality of cells which are affected by the treatment and are related to the on-target effect. A group of off-target cellular features can also be identified which are different to the on-target cellular features and which are also affected by the treatment and which are related to the side effect. A measure of the side effect based on the off-target cellular features can be obtained. The treatment can then be characterised based on the measure of the side effect. A further method involves capturing an image of the population of treated cells and deriving cellular features from the image.