JBEI Research Highlights - June 2014

1. Cellulosic Biomass Pretreatment and Sugar Yields
as a Function of Biomass Particle Size
Outcomes
• Ionic liquid (IL) pretreatment results in
 greater cell wall disruption
 reduced crystallinity
 increase accessible surface area
 Higher saccharification efficiencies
Background
• Lignocellulosic biomass has enormous
potential as feedstock for fuels and
chemical products.
• Efficient pretreatment processes are
needed for optimal enzymatic
saccharification.
Approach
• Compared four feedstock pretreatments
as a function of particle size and sugar
yields
• Determined and compared:
Composition
Crystallinity
Structural Differences
Accessible Surface Area
Enzymatic Saccharification
Significance
• Ionic liquid (IL) pretreatment gives higher saccharification efficiencies than other methods,
greatest at larger particle sizes above 75m; thus, less energy input to size reduction of starting
materials.
Compositions of untreated vs. pretreated switchgrass
Crystallinity Index
Surface area of the biomass samples (m2 / g)
SEM images of untreated vs. pretreated swichgrass Yield of reducing sugars
Dougherty, M. J., et al. PLOS ONE, 9(6), e100836 (2014). DOI: 0.1371/journal.pone.0100836

2. Synthetic Biology Open Language (SBOL)
design communication standard
Outcomes
• Implemented SBOL as an XML/RDF data serialization and developed software libraries and specification documentation.
• Demonstrated utility of SBOL for design exchange between different software tools and between academia and industry.
SBOL-enabled design collaboration across research and commercial institutions
Galdzicki, et al., Nature Biotechnology 32:545–550 (2014).
Background
• Re-using previously validated
designs is critical to the progression
of synthetic biology from a research
discipline to an engineering practice.
• SBOL represents designs in a
community-driven, formalized format
for exchange between software tools,
research groups, and commercial
service providers.
Approach
• Standardize SBOL as a design
exchange data format, and provide
software libraries and specification
documentation to help scientists
benefit from SBOL in their own
research.
Significance
• SBOL contributes to the principled engineering of biological organisms through the
standardization of design information exchange.

3. The plant glycosyltransferase clone
collection for functional genomics
Outcomes
• The clone collection contains 403 Arabidopsis GTs and 96 rice GTs all sequence verified
• The creation of particle bombardment plasmids (pBullet) that are optimized for transient co-
localization studies1
Robotics used for high-
throughput cloning
Lao, et al., Plant Journal. DOI: 10.1111/tpj.12577 (2014).
Background
• Glycosyltransferases (GTs) are
involved in plant cell wall
biosynthesis
• Plant biomass, composed of cell
walls, can be used for production
of advanced biofuels
• The specific functions of these
GTs remain largely unknown
Approach
• Use of robotics system for high-
throughput cloning
• Manual cloning of difficult and
complicated genes that cannot
be done with robotics
• Creation of tools to allow for
easy localization studies
Significance
• This collection can help accelerate our understanding of lignocellulosic material in plants
and development of improved bioenergy crops
• This allows the scientific community to study the GTs without going through the mundane
task of manually cloning interesting candidates
Bombardment tool for co-localization studies in plants

4. Economic impact of advances in ionic
liquid pretreatment on cellulosic biorefinery
Background
• A novel One-Pot (OP) ionic liquid (IL)
pretreatment process developed at JBEI was
shown to reduce water consumption compared
to standard Water-Wash (WW) IL pretreatment
process1.
• Economic viability of both WW and OP routes
was investigated in this study2.
Approach
• A technoeconomic analysis (TEA) of the entire
biorefinery (Fig. 1) with WW- and OP-based
pretreatments was carried out to benchmark
the Minimum Ethanol Selling Price (MESP)
and to identify important cost drivers.
Outcomes
• From an economic perspective, both the WW
and the OP processes require high biomass
loading (50%) (Fig 2a).
• While at 50% biomass loading both WW and
OP processes exhibited comparable
economics (MESP of $6.3/gal), the OP route
was found to be more sustainable as it
requires significantly less water (Fig 2b).
Significance
• TEA has successfully identified cost drivers - i.e., water usage in WW route and acid/base
consumption in OP route (Fig 2a and 2c)
• Lignin valorization was found to be essential for economic feasibility (Fig 2d)
1Shi, Jian, et al. Green Chem. 15.9:2579-2589 (2014).
2Konda, NVSN Murthy, et al. Biotechnology for Biofuels 7.1:86 (2014).
Figure 1. Simplified representation of
biorefinery with WW (blue) and OP (red)
IL pretreatment processes
Figure 2. Comparison of
economic and water usage
impacts of WW and OP routes.

5. High-throughput prediction of eucalypt lignin syringyl/guaiacyl content
using multivariate analysis: a comparison between mid-infrared, near-
infrared, and Raman spectroscopies for model development
Outcomes
• MIR and Raman spectroscopy led to the most
accurate predictive models.2
• High-throughput Raman modelling of lignin
S/G ratios in Acacias and eucalypts resulted in
the ability to predict the S/G ratio in 269
unknown samples with an accuracy equivalent
to the standard data used to construct the model.
Background
• The standard methods for evaluating the important
traits of biomass are laborious, may require toxic
chemicals, and can destroy the sample.1
• The use of spectroscopy coupled with a standard
method can alleviate these shortcomings, and allow
the rapid screening of thousands of different plant
species to assess which may be best suited for
further biofuel research.1,2
Approach
• Develop robust models capable of predicting the trait of
interest (i.e. lignin syringyl/guaiacyl (S/G) ratio) from the
standard method and spectral data using of mid-infrared,
near-infrared, and Raman spectroscopy.2
Significance
• Enables researchers to rapidly evaluate more
plant samples much faster than the standard
methods, thereby reducing experimental time
and expense.
1Lupoi et al., Bioenergy Research, Vol.7: 1-23 (2014).
2Lupoi et al., Biotechnology for Biofuels, Vol.7: 93 (2014).
Reference vs. predicted lignin S/G ratios
using Raman spectra and pyrolysis data
Step 1. Collect Spectral Data
Step 2. Collect Reference Data
Step 3. Construct & Evaluate
Predictive Models
Step 4. Validate Models
& Calculate Predictions
Raman Spectrometer
Pyrolysis Molecular Beam
Mass Spectrum (pyMBMS) @ NREL
Specific mass fragments
correspond to S and G molecules,
enabling calculation of ratio
Spectral Transformation
Combine calibration and validation
data sets; assess accuracy of
pyMBMS reference means to those
predicted using model