NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify expe...

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Bibliographic Details
Main Author: Nathan Braniff (14149834) (author)
Other Authors: Taylor Pearce (14228533) (author), Zixuan Lu (7441793) (author), Michael Astwood (14228536) (author), William S. R. Forrest (14228539) (author), Cody Receno (14228542) (author), Brian Ingalls (8698257) (author)
Published: 2022
Subjects:
Biotechnology
Cancer
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
synthetic biology relies
accurate parameter estimates
given modeling objective
comprehensive modeling workflow
experimental scenarios encountered
art numerical tools
particular modeling task
support oed investigations
systems biology modeling
data collection procedures
bacterial optogenetic system
existing oed tools
systems biology research
nloed software package
optimal experimental design
demonstrate nloed ’
optogenetic system
facilitates modeling
experimental design
diagnostic tools
python package
particular emphasis
biological research
oed methods
nloed supports
nloed offers
wide variety
well suited
vivo data
inefficient computation
escherichia coli
efficiently contribute
dynamic models
data types
available data
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