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SUMMARY:Chemical Engineering Seminar-The Confluence of Kinetic Modeling an
 d Data Science
DTSTART:20190927T160000
DTEND:20190927T173000
DTSTAMP:20260427T234001Z
UID:30e970eb5a1b5419bace3f07022a49dd1f0e9e9d91dfaa9955cceeef
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Linda Broadbelt\, Department of Chemical and Biological 
 Engineering\, Northwestern University\, USA.\nNote: We had to advance the
  seminar to 16:00 (instead of 16:15) due to the ISIC council taking place 
 at 17:00.\n\nReaction pathway analysis and kinetic modeling are powerful t
 ools to design novel routes to chemicals\, identify optimal processing con
 ditions\, and suggest catalyst design strategies. We have developed method
 s for the assembly of kinetic models of substantive detail that link the a
 tomic and process scales. We have applied our methodology to seemingly ver
 y disparate chemistries\, yet applying a common methodology reveals that t
 here are many ubiquitous features of complex reaction networks for chemica
 l and biological systems. The first part of this talk will focus on mechan
 istic modeling of the conversion of hydrocarbons from renewable sources\, 
 from quantitative analysis of chemical catalysis by native inorganic const
 ituents to mechanistic understanding of how enzymes achieve exquisite sele
 ctivity for similar conversion processes\, leading to the potential for th
 e design of novel (bio)chemical pathways. However\, the design of novel pa
 thways was carried out in the absence of any quantitative kinetic modeling
 \, raising the intriguing question of whether data science approaches alon
 e are sufficient to understand complex reaction networks. We demonstrate t
 he application of data science methods to identify potential biopriviledge
 d molecules\, that is\, molecules that are accessible from biological feed
 stocks and processes and serve as candidates for conversion to a full rang
 e of attractive products via selective chemical catalytic chemistries. Int
 riguingly\, the ranking of potential biopriviledged molecules relies on un
 furling potential reaction pathways from the candidate molecule to product
 s of interest and calls for quantitative comparisons of kinetics\, which d
 emonstrates the confluence of kinetic modeling and data science and their 
 symbiotic relationship.
LOCATION:BCH 2201 https://plan.epfl.ch/?room==BCH%202201
STATUS:CONFIRMED
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