Abstract: A method for predicting a readout of a second assay from a readout of a first assay is described. The method comprises obtaining, for one or more experimental conditions, a readout from the first assay; predicting, using the readout from the first assay, a readout from the second assay using a machine learning model comprising: first and second assay models that have been trained to provide a latent representation of a readout from the first and second assays, respectively, using training data comprising readouts from the first and second assays, respectively, for a plurality of experimental conditions; and a translation model that has been trained to predict a latent representation of the second assay model from a latent representation of the first assay model, using training data comprising readouts from the first and second assays for a plurality of experimental conditions.

Abstract: A computer implemented method for detecting foam on the surface of a liquid medium contained in a vessel is described. The method including the steps of receiving a sample image of at least a portion of the vessel comprising the liquid-gas interface and classifying the sample image between a first class and at least one second class, associated with different amounts of foam on the surface of the liquid. The classifying is performed by a deep neural network classifier that has been trained using a plurality of training images of at least a portion of a vessel comprising a liquid-gas interface. The plurality of training images may comprise at least some images that differ from each other by one or more of: the location of the liquid-gas interface on the image, the polar and/or azimuthal angle at which the liquid-gas interface is viewed on the image, and the light intensity or colour temperature of the one or more light sources that illuminated the imaged portion of the vessel when the image was acquired.

Abstract: Aspects relate to a computer-implemented method, a computer program and a system for storing a heterogeneous sequence of discrete-time data determined from a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises receiving the discrete-time data, the discrete-time data comprising data from one or more first scientific instruments and including data comprising one or more timestamps corresponding to one or more digital signals.

Abstract: A method for optimizing protein expression comprises obtaining a plurality of amino acid sequences and corresponding known efficiency values, each known efficiency value indicating efficiency of expressing a protein having a corresponding amino acid sequence; for the plurality of prediction algorithms, obtaining a prediction function, wherein the prediction function outputs a predicted efficiency value for expressing a protein having an amino acid sequence corresponding to an input numerical vector; evaluating the prediction function by comparing outputted predicted efficiency values with the known efficiency values; selecting a prediction algorithm based on said evaluating; predicting, using the prediction algorithm and the prediction function, efficiency values for expressing proteins respectively having specified amino acid sequences; and outputting the specified amino acid sequences and the efficiency values predicted for the specified amino acid sequences.

Abstract: A computer-implemented method is provided for controlling a chromatography system that is configured to physically perform and/or simulate a chromatography process. The method comprises obtaining, from the chromatography system, a current state of the chromatography system, the current state including one or more values of one or more state parameters, the one or more state parameters including one or more quantities of one or more substances present in the chromatography system, and determining one or more values of one or more control parameters for the chromatography system according to a policy that is configured to map the current state to a corresponding action representing the one or more values of the one or more control parameters.

Abstract: Aspects of the application relate to methods, a computer program and a process control device. According to one aspect, a computer-implemented method for determining a multivariate process chart is provided. The multivariate process chart is to be used to control a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The multivariate process chart includes a first trajectory, an upper limit for the first trajectory and a lower limit for the first trajectory.

Abstract: Aspects of the application relate to computer-implemented methods, process control devices, and a computer program. According to one aspect, a computer-implemented method for controlling a process in a plurality of first scale vessels via a first process control device is provided. Each of the first scale vessels contains fluid and the process is for producing a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method includes controlling, by the first process control device and at least partially in parallel, the process in each of the first scale vessels. The method can include periodically determining, prior to an assigning decision and at a first frequency, first sets of process parameter values for each of the process parameters from each of the first scale vessels.

Abstract: Aspects of the application relate to methods, a computer program and a process control device. According to one aspect, a computer-implemented method for determining a multivariate process chart is provided. The multivariate process chart is to be used to control a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The multivariate process chart includes a first trajectory, an upper limit for the first trajectory and a lower limit for the first trajectory.

Abstract: A computer-implemented method for data analysis comprises obtaining a plurality of first observations, the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins; and outputting the second histograms constructed for the plurality of groups.

Abstract: A computer-implemented method for data analysis comprises obtaining a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting the second histograms.

Abstract: A computer-implemented method for analyzing data obtained for a chemical and/or biological process comprises: obtaining a result of statistical data analysis on the data obtained with respect to the chemical and/or biological process; calculating, for values of process parameters obtained at groups of time points during batch processes of the chemical and/or biological process, a ratio of a correlation value to a confidence value of the correlation value, the correlation value indicating a correlation between the values of the process parameter and at a process output value; calculating, for process parameters, an average of absolute values of the ratios calculated for the values of the process parameter obtained at different groups of time points during the batch processes; excluding the values of one of the process parameters having a smallest average; and iterating, until at least one specified condition is met.

Abstract: An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier.

Abstract: A method of predicting a parameter of a medium to be observed in a bioprocess based on Raman spectroscopy including the steps of acquiring a first series of preparatory Raman spectra of an aqueous medium using a first measuring assembly; normalizing the first series of preparatory Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the first measuring assembly; building a multivariate model for the parameter based on the normalized preparatory Raman spectra; acquiring predictive Raman spectra of the medium to be observed during the bioprocess with another measuring assembly; normalizing the predictive Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the other measuring assembly; and applying the built model to the predictive Raman spectra for predicting the parameter.

Abstract: A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.

Abstract: A computer-implemented method for data analysis is provided.

Abstract: Computer-implemented monitoring of monoclonal quality of cell growth is specifically applicable to development of cell lines for the manufacturing of biopharmaceuticals. In one aspect, a computer-implemented method comprises: acquiring a sequence of images of a cell culture taken at different times during cell growth; processing each image in the sequence of images to identify cell locations of cells in the cell culture; determining for at least some of the images in the sequence of images the number of cells from the identified cell locations; determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations; evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth; and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions.

Abstract: Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.

Abstract: A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method can include obtaining a base dataset including one or more videos captured with microscopic imaging with at least one of the one or more videos including a cellular event, and cropping out, from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event. An artificial neural network (ANN) model can be trained using the plurality of selected sub-videos as training data, to perform unsupervised video alignment, a query sub-video can be aligned using the trained ANN model, and a determination can be made whether or not the query sub-video includes the cellular event.

Abstract: A computer-implemented method for data analysis comprises obtaining a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting the second histograms.

Abstract: Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.