ERC starting grant BIOLEAP Biotechnological optimization of light use efficiency in algae photobioreactors Tomas Morosinotto Madrid, 15 th December 2016
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ERC starting grant
BIOLEAP
Biotechnological optimization of light use efficiency in algae
photobioreactors
Tomas Morosinotto Madrid, 15th December 2016
The main mission of the European Research Council (ERC) is to
promote wholly investigator-driven, or 'bottom-up' frontier research
DiBio
Dipartimento di Biologia
MICROALGAE for biofuels and biofarming
Two objectives: optimization of biomass productivity
Optimization of yield in the desired product
CO2
ALGAE
BIOMASS
O2 CO2
FUELS
CHEMICALS
HIGH ADDED VALUE PRODUCTS
ALGAE
BIOMASS
O2
Why light use efficiency is important?
Photosynthetic efficiency has a huge influence on the area required to
reach the target production
Simulation of a target production of 1 t biomass / year
Light use efficiency in algae
Sunlight is abundant but diluted in large surfaces
Large scale systems for growing algae are expensive!
0 2 4 6 8 10 12
0
200
400
600
800
4000
4200
4400
4600
are
a n
eeded (
m2
)
photosynthetic efficiency (%)
area needed (m2)
Simionato et al., Biophys Chem 2013
Algae in large scale are cultivated in photobioreactors
Biotechnological improvements
Natural Environment
vs.
Artificial Environment
Conditions are different from the environment where these organisms evolved
Nannochloropsis gaditana
DiBio
Dipartimento di Biologia
• Nannochloropsis species is a good natural producer of lipids (TAGs and omega-3)
• Fully sequenced genome
• Possibility to transform its nuclear genome
• Biological interest 0,5 mm
Simionato et al, Euk Cell 2013 Killian et al, Nature
Communications 2011 Corteggiani Carpinelli et al,
Molecular Plant 2014
Forward genetics approach
b. Chemical mutagenesis
Screening 12000 mutant strains
a. Insertional mutagenesis
Improving algae Light use efficiency
97 strains
With altered
photosynthetic properties
Improving algae Light use efficiency
Looking for mutants showing higher Photosynthetic electron transport rate
0 500 1000 1500 2000
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100
ET
R
Light intensity
WT
#2
Improving algae Light use efficiency
Mutants selected shows higher Biomass productivity
0.0
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0.8
Pro
du
cti
vit
y (
g /
L /
d)
*
**
*
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0.8
WT
E2
I48
ALL diferrent from each other except for E2 between 400 uE and 1200 uEwith 250 mil
+ 15%
Perin et al., , Biotechnology for Biofuels 2016
Proof of concept: it is possible to improve biomass productivity in photobioreactors
Conclusions (1)
It is possible to improve biomass productivity
Is it possible to include environment in genetic modifications design?
There is a strong interaction between Genotype and Environment
Artificial environment :
Algae in photobioreactors
Mathematical Models describing growth rate of each cell
depending on its position in the photobioreactor
(light, CO2, nutrient availability)
Biological data
with Fabrizio Bezzo (UniPD), Benoit Chachuat (Imperial College)
Models need to be accurate but yet simple enough to be mathematically treatable (identifiable)
Improving algae Light use efficiency
The multiscale model: Building blocks
photoproduction
photoregulation
photoinhibition
LIGHT
EXPLOITATION
FOR METABOLISM
photoacclimation ADAPTATION TO
LIGHT INTENSITY
metabolism
&
nutrient intake
GROWTH AND
CHEMICAL
PRODUCTION
temperature effects METABOLIC
KINETICS
GROWTH
Modelling algae growth
Bernardi et al., IECR 2014; Nikolaou et al., J Biotechnol. 2015; Bernardi et al., PLoS ONE 2014
Providing guidelines for genetic engineering efforts
Assumption – Growth in fed-batch cultures @ 400 µmol photons m-2 s-1, 150 106 cells /ml
Model assisted genetic engineering
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5
10
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35
40
45
50
p
rod
uct
ivit
y (
%)
NPQmut
/NPQWT
[]
Reduction of NPQ
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10
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45
50
p
roduct
ivit
y (
%)
chlmut
/chlWT
[]
Chl reduction Reduction of antenna size
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40
45
50
p
rod
uct
ivit
y (
%)
ASIImut
/ASIIWT
[]
Identification of the mutation with the largest impact on productivity
Go back to the mutants collection looking for strains with the largest reduction in Chl content per cells
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50
chl = 0.85 chlWT
chl = chlWT
p
rod
uct
ivit
y (
%)
NPQmut
/NPQWT
[]
Model can be used to guide genetic engineering,
identifying the properties of the ideal strain
Model assisted genetic engineering
Model still needs important improvements
(Growth in dynamic conditions)
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5
10
15
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25
30
35
40
45
50
ASII = 0.85 ASIIWT
ASII = ASIIWT
p
rod
uct
ivit
y (
%)
chlmut
/chlWT
[]
Consistency with experimental results
Model assisted genetic engineering
Models can identify the conditions where selected mutants have maximal productivity
Selection of mutants tailored for specific growing conditions
Diana Simionato Stefania Basso Andrea Meneghesso Giorgio Perin Caterina Gerotto Anna Segalla Nicoletta La Rocca Alessandro Alboresi Alessandra Bellan Mattia Storti
Acknowledgments
Dipartimento di Ingegneria Industriale Università di Padova
Dipartimento di Biologia, Università di Padova
Alberto Bertucco, Fabrizio Bezzo
ERC Starting Grant BioLEAP
PAR Lab
Padua Algae Research
Laboratory
Giovanni Finazzi Eric Marechal
IRTSV - CEA Grenoble (France)
Giorgio Valle Università di Padova (Italy)
Benoit Chachuat Imperial College (London, UK)
Giovanni Giuliano ENEA Roma (Italy)
Roberta Croce
University of Groningen (The Netherlands)
University of Münster (Germany)
VU University Amsterdam (The Netherlands)
Egbert J. Boekema
Michael Hippler
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