Abstract: A set of conditions is defined that to be simulated via execution of a machine-learning model. For each condition, a set of learnable condition-specific parameters is identified to configure a model architecture. A first learnable condition-specific parameter associated with a first condition of the set of conditions can be identified a shared or global parameter that is to have a same value as at least another learnable condition-specific parameter (associated with another condition). One or more parameter data structures can be configured with parameter values for the sets of condition-specific parameters for the sets of conditions, where the configuration imposes a constraint that a value for the first condition-specific parameter and the at least one value for the at least one other condition-specific parameter are the same. The machine-learning model can be trained using the configured parameter data structure(s).

Abstract: The present disclosure relates to a scalable experimental workflow that uses a culture system to maintain a steady state in a biological system, and techniques for identifying values for parameters in a in silico model based on experimental data obtained from the biological system. Particularly, aspects of the present disclosure are directed to obtaining measurement data for one or more characteristics of a biological system developed in a culture system, where the measurement data is indicative of each of the one or more characteristics at a physiological steady state where growth of the biological system is occurring at a substantially constant growth rate, determining a value for a parameter of a model of the biological system based on an growth formula, the measurement data, and the substantially constant growth rate, and parametrizing the model with at least the value determined for the parameter.

Abstract: A method for allocating resources among multiple sub-models in a simulation of a biological cell is described herein. The method receives an initial state dataset including initial aggregate resources in a plurality of sub-models, which make up a whole cell model. The method calculates, at a first time step, an outcome of each of the sub-models, which includes a local production, a local consumption and local net value of at least one resource shared between at least two sub-models. The method calculates a subsequent state dataset based on the outcome of each of the sub-models. The subsequent state dataset includes subsequent aggregate resources, the local production, the local consumption and local net value. The method determines, at a second time step, a second outcome of each of the plurality of sub-models based on the subsequent aggregate resources and the local production, local consumption and local net value.

Abstract: The present disclosure relates to a scalable experimental workflow that uses a culture system to maintain a steady state in a biological system, and techniques for identifying values for parameters in a in silico model based on experimental data obtained from the biological system. Particularly, aspects of the present disclosure are directed to obtaining measurement data for one or more characteristics of a biological system developed in a culture system, where the measurement data is indicative of each of the one or more characteristics at a physiological steady state where growth of the biological system is occurring at a substantially constant growth rate, determining a value for a parameter of a model of the biological system based on an growth formula, the measurement data, and the substantially constant growth rate, and parametrizing the model with at least the value determined for the parameter.