Microbial Community Modeling (2)
Jingwei Ma
Feb 14th , 2013
Bioprocessing and Bioproduct Engineering Laboratory
Department of Biological Systems Engineering
Outline
 Molecular methods used for microbial
community characterization
molecular methods
 Mathematic modeling used for microbial
community evaluation
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Outline
 Mathematic modeling used for microbial
community evaluation
 The development of ADM1
 Application of ADM1 for microbial
community evaluation
 BBEL contribution on ADM1
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Progress of AD Modeling
Simple
substrate characterization model
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Hydrolysis limiting step model
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Eastman and Ferguson model
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Pavlosathis and Gosset model
Intermediate
•
substrate characterization model
Shimizu model (1993)
Advanced
substrate characterization model
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Angelidaki model
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Siegrist model
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ADM1 model (2002)
Development of ADM1
 IWA task group
• Created on 8th World Congress on AD (Sendai, 1998)
• Includes 12 specialists from Australia, Denmark, Greece, Italy,
Netherlands, Russia, Spain, Switzerland, USA
• Report published on 2002
 Main goal
• To propose the first generic model of AD (ADM1)
 Specific goals
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To be limited to the main relevant processes occurring in order to
have a model as simple and applicable as possible;
To create a common starting point for further model development
and validation.
Reaction System
Biochemical reactions
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Disintegration (extracellular)
Hydrolysis (extracellular)
Acidogenesis (intracellular)
Acetogenesis (intracellular)
Methanogenesis (intracellular)
Physico-chemical reactions
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Ion association/dissociation
Gas-liquid mass transfer
Liquid-solid transfer
Ionic solubilization
Biochemical processes
Composite particulate
Inert particulate
Disintegration
Carbohydrates
Proteins
Fats
Inert soluble
Hydrolysis
Sugars
Acidogenesis
Amino acids
LCFA
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3
1
Propionate
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Acetogenesis
Hva, HBu
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Acetate
H2
Death
Methanogenesis
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CH4 ; CO2
(1) Acidogenesis of sugars
(3) Acetogenesis of LCFA
(5) Acetogenesis of butyrate and valerate
(7) Hydrogenotrophic methanogenesis
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(2) Acidogenesis of amino acids
(4) Acetogenesis of propionate
(6) Acetotrophic methanogenesis
Kinetics/Rate equations
First order equations
• Disintegration
rdis = kdisXc
• Hydrolysis
rhyd = khydXpol
• Bacterial death
rdea = kd,iXi,biom
Substrate uptake Monod equations
• Acidogenesis
ri = [km,iXiSj/(Ks,j+Sj)]Ii
• Acetogenesis
dXi/dt = Yiri
• Methanogenesis
Yi = const
Biochemical kinetic processes
Biochemical kinetic processes
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Physico-chemical processes
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Mixing pattern
CSTR
PF
Intraliquid interactions
Charge balance
Cat++NH4++H+=HCO3-+V(LC)FA-+OH-+An-+pH
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Liquid-gas interactions
ri-gas = kLa(Si-lig – KHpi-as)
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Liquid-solid interactions
Precipitation
Sedimentation
Adsorption
Model Implementation
Schematic of a typical single-tank digester
q = flow
V = volume
Sstream,i = concentration of liquid components
Xstream,i = concentration of particulate components
Mass Balance Equations
Accumulation = Input - Output + Reaction
(1)
(2)
(3)
(4)
Model in matrix format
The overall volume specific action term (ri) for each component i
can be formulated by summing the products of the
stoichiometric coefficients in column i and process rates:
Example: the overall rate of reaction for monosaccharides (r1) is:
Hydrolysis of
carbohydrates
Hydrolysis of lipids
Uptake of sugars
Model presentation in Peterson matrix format
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Inhibition/Toxicity
• Biocidal inhibition (reactive toxicity, normally irreversible):antibiotics,
detergents, strong oxidisers, electrophiles etc.
• Biostatic inhibition (non-reactive toxicity, normally reversible): pH,
products, weak acids/bases, cations and anything else that disrupts
homeostasis
Summary

Differential and algebraic equation (DAE) set
•26 dynamic state concentration variables
•19 biochemical kinetic processes
•3 gas-liquid transfer kinetic processes
•8 implicit algebraic variables
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Differential equation (DE) set
•32 dynamic state concentration variables
•6 acid-base kinetic processes
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Modification of ADM1 for microbial
community simulation
• In the ADM1 model, one microbial population is
associated to each reaction and seven main groups of
bacteria are represented: sugar, amino acids, LCFA,
valerate and butyrate, propionate, acetate and hydrogen
degraders, each of them having specific kinetic
parameters.
• The engineering of biological systems would be improved
if one could predict and manipulate the associated
microbial diversity. Mathematical models, in which data on
micro-scale molecular diversity, as gained with modern
molecular tools have been incorporated to more closely
represent biological processes, can provide a useful tool
to reach this goal.
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Example 1
Aceticlastic methanogen population dynamics
• Competitive aceticlastic population structures were
implemented in the ADM1
• Methanosaeta sp. and Methanosarcina sp. were used to
demonstrate the effect of competitive methanogenic
populations.
• In the competitive structure the maximum acetate utilization
rate is the sum of that for each genus.
• The acetate production rate is determined from the sum of
biochemical processes producing acetate.
• A CSTR reactor configuration was used for the simulations
Straub AJ, Conklin AS, Ferguson JF, Stensel HD. Use of the ADM1 to investigate the effects
of acetoclastic methanogen population dynamics on mesophilic digester stability. Water Sci
Technol. 2006;54(4):59-66.
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Acetoclastic methanogen population
growth rates as a function of digester
steady-state acetate concentration
The biokinetics for the two curves show the dominance of Methanosarcina
sp. above a reactor acetate concentration of 0.14 g COD/L
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Variation in methanogen population and
acetate concentrations
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Example 2
Modeling microbial diversity in
anaerobic digestion through an extended
ADM1 model
• The traditional ADM1 model was extended in such a way
that multiple species are associated to each functional
group.
• The number of species per reaction is arbitrary and in this
study has been set to 10, to keep a reasonable
computation speed.
Ivan Ramirez, Eveline I.P. Volcke, Rajagopal Rajinikanth, Jean-Philippe Steyer, Modeling
microbial diversity in anaerobic digestion through an extended ADM1 model, 2009, Water
Research 43(11): 2787-2800
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Determination of kinetic parameters
• Within a functional group, the kinetic parameters km and Ks for
each specie, were randomly chosen from a normal bimodal
distribution, with means of µ1 =0.6*k, µ2 =1.4*k, and standard
deviations of σ1,2=0.125*k where k is the value of the
corresponding standard ADM1 parameter
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Determination of kinetic parameters
• The distribution type and their parameter values were
established following a curve-fitting process using
experimental data.
• In order to obtain similar conditions for simulations, each
initial biomass concentrations in ADM1 will be divided by
10 and distributed equally among each microbial
population in ADM1_10.
• Whereas the original ADM1 possesses 24 state variables,
of which 7 biomass species, the extended model includes
70 different biomass species (7 functional groups, 10
species per functional group), of 87 state variables in
total. The number of associated reactions is extended
from 19 to 154.
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Experimental data versus simulation results
with ADM1 and ADM1_10
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Specific growth rates and simulated
evolution of acetate degrading biomass
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Behavior simulation of a CSTR for three
different TAN concentration feeding
strategies
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Simulated reactor performance for one-step
(R1) and two-step (R2) ammonia increase
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Simulated reactor performance for one-step
ammonia increase (R3) with successive
suppression of the dominant species
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Reactor performance for one-step ammonia
increase (R3) with successive
suppression of the dominant species
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BBEL Contribution
BBEL Contribution
Transformer Model
• Transform practical measurements to ADM1 input;
• Stoichiometry is evaluated by mass and charge balances;
• Transformation step include the ordered maximization procedure.
Practical Measurements
Total COD (CODt)
Soluble COD (CODs)
VFA
Total carbon (TC)
Total inorganic carbon (TIC)
TKN
TAN
Total phosphorous
Orthophosphate (orthoP)
Total alkalinity (Scat)
Total solids (TS)
Total volatile solids (TVS)
ADM1 Input
Organic carbon (kgCOD/m3)
· Soluble component
S1
S2
S3
…
· Particulate component
X1
X2
X3
…
Inorganic carbon (kmoleC/m3)
Nitrogen (kmoleN/m3)
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Co-digestion model
To be repeated for additional
waste streams
HSAD Model
Solid
reactor
Liquid
reactor
Two-stage HSAD system
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Thank You Very Much!
Jingwei Ma
Washington State University
Pullman, WA 99164-6120, USA
Email: mjw@wsu.edu
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