BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:AmirHosein Sadeghimanesh (Coventry University) DTSTART:20250410T150000Z DTEND:20250410T153000Z DTSTAMP:20260421T124344Z UID:MoRN/121 DESCRIPTION:Title: D esigning Machine Learning Tools to Characterize Multistationarity of Fully Open Reaction Networks\nby AmirHosein Sadeghimanesh (Coventry Univers ity) as part of Seminar on the Mathematics of Reaction Networks\n\n\nAbstr act\nChemical Reaction Networks (CRNs) are the mathematical formulation of how the quantities associated to a set of species (molecules\, proteins\, cells\, or animals) vary as time passes with respect to their interaction s with each other. Their mathematics does not describe just chemical react ions but many other areas of the life sciences such as ecology\, epidemiol ogy\, and population dynamics. We say a CRN is at a steady state when the concentration (or number) of species do not vary anymore. Some CRNs do not attain a steady state while some others may have more than one possible s teady state. The CRNs in the later group are called multistationary. Multi stationarity is an important property\, e.g. switch-like behaviour in cell s needs multistationarity to occur. Existing algorithms to detect whether a CRN is multistationary or not are either extremely expensive or restrict ed in the type of CRNs they can be used on\, motivating a new machine lear ning approach. This talk is about a recent attempt to design machine learn ing tools to predict multistationarity of reaction networks.\n LOCATION:https://researchseminars.org/talk/MoRN/121/ END:VEVENT END:VCALENDAR