Multi-scale modelling of cellular signalling, from protein structure to control of cell state transitions: a QBI Online Seminar with Oleksii Rukhlenko

Wednesday, January 12, 2022 - 10:00 am to 11:00 am
Event sponsor
Quantitative Biosciences Institute (QBI) & Systems Biology Ireland (SBI)
students, staff, faculty, alumni, local science community

The QBI/SBI Seminar Series on molecular networks of cancer and other diseases aims to facilitate collaborative relationships between scientists from both the US and Ireland. QBI and UCD recently signed a 5-year MOU to reinforce the links between scientists in San Francisco and Dublin, and enhance their collaborative ability to strengthen scientific research and innovation. Through this agreement, scientists will work together to identify opportunities to promote cooperative biosciences research and training activities.

On January 12th, the series will present Oleksii Rukhlenko, a Research Scientist at Systems Biology Ireland, University College Dublin. Dr. Rukhlenko received his Masters in Applied Mathematics & Physics and his Ph.D in Mathematical Modeling from the Moscow Institute of Physics and Technology. During these programs, he was involved in mathematical modeling of blood flow and coagulation. Under guidance of Professor Guria, he developed the model which described formation of wide spectra of fibrin polymer structures in intensive blood flow; ranging from clouds of dust-like microthrombi up to solid polymer structures, including loose structures of different density. Since 2015, Dr. Rukhlenko has investigated intracellular Signalling Transduction Networks (STN) at Systems Biology Ireland. His current research is concentrated on structure-based dynamic modeling of signaling cascades, data-driven reconstruction of STNs, investigating mechanistic dynamical models of STNs to predict cellular responses, and experimental validation of theoretical predictions.

Talk Title Multi-scale modelling of cellular signalling: from protein structure to control of cell state transitions
Webinar ID 91479345244
Passcode 678572

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