Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can produce enormous quantities of data from a given cellular or multicellular system.
Massachusetts Institute of TechnologyNov 5 2024
We've landed on a computational modeling framework that allows prediction of effects of perturbations in a highly complex system, including multiple scales and many different types of components." Using computational approaches such as machine learning, scientists can use this data to train models to predict a specific output based on a given set of inputs -; for example, whether a vaccine will generate a robust immune response. However, that type of modeling doesn't reveal anything about the steps that happen in between the input and the output.
Lauffenburger's lab has previously used this type of model to analyze intracellular signaling pathways, which required analyzing just one kind of data. To adapt this approach to analyze many datasets at once, the researchers applied a mathematical technique that can filter out any correlations between variables that are not directly affecting each other.
The data that the researchers examined in this study included measurements of about 200 variables, including levels of cytokines, antibodies, and different types of immune cells, from about 30 animals.
Cell Computational Modeling Cytometry Genomics Immune Response Immune System Machine Learning Pathogen Proteomics Research Transcriptomics Tuberculosis Vaccine
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