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Edward Vitkin and Zohar Yakhini
Department of Computer Science, Technion - Israel Institute of Technology
Background
Efficient and sustainable conversion of biomass
into valuable products is a major challenge for
bioengineering.
The composition of the available feedstock
biomass and the ability of microorganisms to
efficiently ferment it are two most critical factors
influencing the process efficiency.
Even in the two-organism fermentation system, an
analysis of many promising scenarios may require
solution of millions of optimization tasks.
Simulations and computer-assisted optimization
are valuable tools for fermentation processes
designers.
We present BioLEGO, a Microsoft Azure Cloud-
based framework, delivering these heavy
calculations to unskilled users.
Modeling Scalability
Experimental Validation
Feedstock Biomass Fermentation Process Received Product
Class Metabolite Name Biomass A (g/100gDW) Biomass B …
Insoluble fibres (NDF)
Ulvan 23.68 23.68Hemicellulose 20.60 20.60Cellulose 9.13 9.13Lignin (ADL) 1.56 1.56
Monosaccharides
Glucose 17.24 17.24Rhamnose 7.40 7.40Xylose 1.93 1.93Galactose 0.35 0.35Mannose 0.29 0.29Arabinose 0.08 0.08
Amino acids
Aspartic acid 1.09 1.09Threonine 0.53 0.53Serine 0.59 0.59Glutamic acid 1.09 1.09Proline 0.35 0.35Glycine 0.56 0.56Alanine 0.77 0.77Valine 0.77 0.77Methionine 0.20 0.20Cystine 0.16 0.16Isoleucine 0.40 0.40Leucine 0.70 0.70Tyrosine 0.51 0.51Phenylalanine 0.23 0.23Histidine 0.12 0.12Lysine 0.55 0.55Arginine 0.52 0.52
Fatty acids
Myristic 0.19 0.19Palmitic 4.67 4.67Palmitoleic 0.54 0.54Stearic 0.15 0.15Oleic 1.25 1.25Linoleic 0.19 0.19a-Linolenic 0.25 0.25Arachidic 0.09 0.09Eicosenoic 0.12 0.12Behenic 0.33 0.33Docosahexaenoic 0.09 0.09
Compositions of Expected Feedstock Biomasses Potential products
Potential fermentation setups
• Fermentation Efficiency
• Per target product
• Per feedstock
• Per fermentation setup
• Beneficial adjustments
• Of feedstock
• Of fermenting organisms
Product NameEthanolButanolAcetone
Organisms, organism order and growth conditions
E. coli; no O2S. cerevisiae; with O2 S. cerevisiae followed by E.coli; with O2
Flux Balance Analysis (FBA) framework
Modular Approach – Single Module
Modular Approach – Combining Modules
Formulation of Optimization problem
The reaction stoichiometry in a metabolic
model is represented by matrix S, wherein
Sm,r corresponds to stoichiometric
coefficient of metabolite m in the reaction r.
The vector of metabolic fluxes that are
carried by the model reactions, normally
denoted as v, is constrained both by mass-
balance and by maximal/minimal feasible
fluxes vUB and vLB
Module interaction
rules for two-step
serial fermentation
process
Feedstock Media →
Media Org1
Waste Org1 →
Media Org2
Growth Org1 →
Media Org2
Product Org1 →
Total product
Waste Org2 →
Total waste
Growth Org2 →
Total waste
Product Org2 →
Total product
Runtime Statistics
Research Setup Number of Tasks Computational Setup Runtime
Exploratory search for optimal
biomass utilization setups for
distinct types of corn biomasses:
1. Corn Cobs
2. Corn Fiber
3. Corn Stover
• 240 model
configurations
• 480 single
fermentation task
simulations
• 10 X Model Setup Constructors • Standard A1v2 nodes (1 core, 2048MB)• 2 threads
• 10 X Single Fermentation Task Estimators• Standard A1v2 nodes (1 core, 2048MB) • 3 threads
21min
(1) Sensitivity and (2) Gradient
analyses for anaerobic two-step
ethanol production from
Kappaphycus alvarezzi
• 2 X 37 single
fermentation task
simulations
• 5 X Single Fermentation Task Estimators• Standard A1v2 nodes (1 core, 2048MB) • 3 threads 2 X
4min
Analysis of two-step
fermentation with knock-outs in
each organism:
1. S. cerevisiae (2,280 reactions)
2. E. coli (2,914 reactions)
• 6,649,115 single
fermentation task
simulations
• 45 X Single Fermentation Task Estimators• Standard D3v2 nodes (4 core, 14336MB) • 10 threads
LOCAL SERVER• 25 X Task calculation request threads• 10 X Task result processing threads
126hr*
* Stronger
Local Server
can improve
up to 50%
Heatmap of knockout performances
Analyzing 6.6M knockouts in two-step process
Use Case
Simulation Results
Reaction
pairs of
interest
GOrilla analysis (Enriched Functionality in results of S. cerevisiae)