(562c) Modeling Metabolic Variation In Mutant Library Selections for Sugar Utilization | AIChE

(562c) Modeling Metabolic Variation In Mutant Library Selections for Sugar Utilization

Authors 

Groot, J. - Presenter, University of Colorado


Selections are a powerful tool to obtain microbial strains
with superior traits like improved cellulosic sugar utilization.
However, the genetic mechanisms behind selections are complex and only partially
understood at best. Better understanding of how genetic variation confers
growth, fitness effects will allow for improved design of selections. Here the
applications and limitations of Elementary Flux Mode (EFM) modeling of genomic
mutant library growth phenotypes will be discussed for E.coli selections
on cellulosic sugars.  

Trackable Multiplex Recombineering (TRMR) creates a
collection of mutants each with either a specific gene overexpressed or knocked-down1.
This genetically engineered variance speeds up the discovery and identification
of mutants with high growth/fitness thereby indicating mutations/genetic mechanisms that dominate a selection.    

Elementary Flux Mode (EFM) Analysis allows for an evaluation
of the metabolic pathways of a cell. A recent EFM method2 allows for
modeling changes in fluxes in mutants with altered expression of metabolic
genes. The method is adapted to assess variability in growth phenotypes in a mutant
library. An evaluation of growth (yield) variability is given for different
selection conditions (e.g. glucose, xylose growth) and an extension to model
mutant fitness of a TRMR library is proposed.  

Sugars like glucose and xylose are metabolized via different
pathways. Quantifying the variation in mutant selections based on metabolic
pathway utilization can help assess its contribution to divergence in selections
and could aid in relating its effect to variation in regulation and (often unanswered)
fundamental issues like rate versus yield strategies, the relation between
growth and fitness.

References

1 Warner, J.R., Gill, R.T. et al (2010) Nature Biotechnology
28: 856?862

2 Zhao, Q. and Kurata, H. (2009) Bioinformatics
25(13):1702-1708