{"id":1576,"date":"2023-10-18T17:31:02","date_gmt":"2023-10-18T17:31:02","guid":{"rendered":"https:\/\/units.cals.ncsu.edu\/microbiology-graduate-program\/?post_type=person&p=1576"},"modified":"2026-07-07T13:53:27","modified_gmt":"2026-07-07T13:53:27","slug":"orlando-arguello-miranda","status":"publish","type":"person","link":"https:\/\/units.cals.ncsu.edu\/microbiology-graduate-program\/people\/orlando-arguello-miranda\/","title":{"rendered":"Orlando Arguello-Miranda"},"content":{"rendered":"

Cell Biology Through Data Science<\/strong><\/em><\/h3>\n

Our laboratory wants to understand how cells divide. We want to discover new biochemical mechanisms that help cells divide when needed. In the same manner, we want to learn how cells stop whenever cell division is too dangerous and could result in irreversible cellular damage. In humans, problems in the control of cell division cause diseases such as cancer and can interfere with wound healing or the maintenance of adult stem cells. In addition, for many bacteria, parasitic organisms, and agricultural pests, the capacity to stop cell division and enter dormant or quiescent states is critical for becoming resistant to antibiotics and pesticides. Thus, understanding how cells activate or stop their machinery for cell division promises to advance both biomedical and agricultural knowledge.<\/p>\n

To analyze dividing and non-dividing cells, we use a unique combination of biochemical methods with machine learning approaches. We track individual cells as they enter or exit from cell division using custom-made algorithms for image analysis. The information derived from monitoring single cells is processed using machine learning algorithms to cluster data sets, identify correlations, and infer causality in intracellular biological networks. Machine learning-inspired hypotheses are then tested using biochemical and genetic tools in model organisms such as the yeast\u00a0Saccharomyces cerevisiae<\/em>.<\/p>\n

Our current projects aim at:<\/strong><\/p>\n