{"id":13264,"date":"2024-05-22T10:20:00","date_gmt":"2024-05-22T10:20:00","guid":{"rendered":"https:\/\/www.sygnaturediscovery.com\/?post_type=webinar-and-podcast&p=13264"},"modified":"2026-01-23T16:04:40","modified_gmt":"2026-01-23T16:04:40","slug":"how-kinetics-experiments-unveil-organelle-complexity","status":"publish","type":"webinar-and-podcast","link":"https:\/\/www.sygnaturediscovery.com\/fr\/webinar-and-podcast\/how-kinetics-experiments-unveil-organelle-complexity\/","title":{"rendered":"How Kinetics Experiments Unveil Organelle Complexity"},"content":{"rendered":"\n

Mitochondria behave dynamically and kinetic experiments reveal things static measurements can’t. In Episode 3, Tatiana explains how time-resolved data uncovers the complexity of mitochondrial responses and why kinetic readouts are increasingly used in pharmacological evaluations. She discusses how these experiments help identify subtle dysfunction earlier in the discovery process. <\/p>\n\n\n\n

About Tatiana<\/h3>\n\n\n\n
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Tatiana Rosenstock has had an exceptional career and has made significant contributions to the field of Neuroscience for 20+ years. Her work has focused on topics such as mitochondrial deregulation, transcription deregulation, and autophagy in neurodegenerative diseases, such as Huntington\u2019s Disease, and Amyotrophic Lateral Sclerosis, and Schizophrenia.  <\/p>\n\n\n\n

At Sygnature Discovery, Tatiana is a Principal Scientist overseeing various neuroscience-related projects in different sub-areas such as metabolism (mitochondrial function), biomarkers, microglial function and activation, and neuroplasticity. During this period, she has been working with distinct in-vitro models, named iPSC-derived neurons, primary cortical neurons, Alzheimer`s and Parkinson`s disease fibroblasts, neuroblastomas cell lines, tissue and biofluids from transgenics animal models of Alzheimer`s disease. <\/p>\n<\/div>\n<\/div>\n\n\n\n

What You’ll Learn<\/h2>\n\n\n\n
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  • Why kinetic data is crucial for understanding mitochondrial behavior<\/li>\n\n\n\n
  • How time-dependent changes reveal hidden dysfunction<\/li>\n\n\n\n
  • The role of kinetics in building more predictive CNS models<\/li>\n<\/ul>\n\n\n
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