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SUMMARY:Technologies for scalably modeling and simulating whole cells - Pr
 ofessor Jonathan Karr
DTSTART:20210216T180000Z
DTEND:20210216T193000Z
UID:TALK157453@talks.cam.ac.uk
CONTACT:92260
DESCRIPTION:This talk is open to all regardless of membership.\nRegister h
 ere: https://forms.gle/anKeqjpGUg4Rdrow6\n\nAbstract:\nWhole-cell computat
 ional models that predict phenotype from genotype have great potential to 
 help physicians make precise medical decisions and help engineers design s
 ynthetic cells. Despite their potential\, it remains challenging to assemb
 le a complete model of a cell. We still cannot completely characterize a c
 ell\, the data that is available is scattered across many disparate databa
 ses and articles\, we have limited methods for understanding the combinato
 rial complexity and multiple scales involved in cellular biochemistry\, an
 d we have limited tools for building models collaboratively. Nevertheless\
 , we believe that advances in genomics are making models of whole cells fe
 asible. Over the past few years\, we have leveraged these advances to deve
 lop tools that make it easier to find the data needed to model whole cells
 \, model the complexity of biochemistry\, co-simulate the multiple scales 
 involved in biochemistry\, and share models and simulations with collabora
 tors. We anticipate that these technologies will accelerate the developmen
 t of whole-cell models. Already\, we are using these technologies to pilot
  more comprehensive models of Mycoplasmas and human stem cells.\n\nSpeaker
  profile:\nJonathan Karr is a Fellow at the Icahn School of Medicine at Mo
 unt Sinai. The long-term goal of his research group is to develop comprehe
 nsive computational models of single-cells that help physicians make preci
 se medical decisions and help engineers design synthetic cells. Toward thi
 s goal\, the Karr Lab is focused on pioneering increasingly comprehensive 
 models of bacteria and human cells. In support of this goal\, the Karr Lab
  is also developing the computational methods and resources needed to buil
 d and simulate more comprehensive and more predictive models\, including m
 ethods for integrating heterogeneous data\, data structures for describing
  the combinatorial complexity of biochemistry\, algorithms for simulating 
 multiple scales\, and platforms for building and analyzing models collabor
 atively. Jonathan earned his PhD in Biophysics from Stanford University an
 d his SB in Physics and SB in Brain & Cognitive Sciences from MIT.
LOCATION:Google Meets
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