Introduction / Objective
The general objective of our experimental system is to perform the following functions:
 Differentiate protein production between the top and bottom layer of the biofilm based on a diffusiongradient inducer.
 Respond to varying Repressor production levels
 Respond to varying IPTG Induction levels
 Respond to varying Promoters
 Respond to varying Ribosome Binding Sites
 Respond to varying Copy Number Plasmids
The objective of our model is to explore and characterize the system, and the effect of parameter modulation on the system.
In terms of our iGEM project, this model was employed to explore the effect of IPTG concentration and diffusion, lacI concentration (determined by the combination part of constitutive promoter, ribosome binding site, lacI gene, double terminator, lac promoter/operon), and the ribosome binding site (of the final productproducing enzyme) on the concentrations of all species involved  described in the following sections  and especially on Chitin Synthase and Chitin concentration and the corresponding rates.
Model Development
The following schematic summarizes the mathematical model we formulated to describe our system.
Variables
 Iex: External Inducer, determined by diffusion through Fick's law (IPTG in our experiment)
 Iin: Internal Inducer (IPTG)
 Ii: Inducer bound to Repressor (IPTG bound to lacI)
 i: Repressor (lacI)
 Db: Repressorbound DNA (lacIbound DNA(CHS3) region in plasmid)
 Dunb: transcribeable or Repressorunbound DNA (lacIunbound DNA(CHS3))
 Re: mRNA for Enzyme (CHS3 mRNA)
 E: Enzyme (CHS3)
 S: Substrate (NAcetyl Glucosamine)
 C: Enzyme Substrate Complex (CHS3(NAcetylGlucosamine)Chitin or (NAG)n Complex)
 P: Protein Product (Chitin or (NAG)n+1)
Constants
Rate Constants:
 kIin  Inducer diffusion into cell from surrounding environment
 kIout  Inducer diffusion out of cell to surrounding environment
 kIif  Inducer and Repressor binding to form InducerRepressor Complex
 kIir  InducerRepressor Complex unbinding to form Inducer and Repressor
 kDbf  Repressor binding to DNA to form DNARepressor Complex
 kDbr  DNARepressor Complex unbinding to form free unbound DNA
 ktcribe  transcription
 ktlate  translation
 ksin  endogenous substrate production
 kCf  SubstrateEnzyme Complex Formation
 kCr  SubstrateEnzyme Complex unbinding to form Substrate and Enzyme
 kP  SubstrateEnzyme Complex forming Product and Enzyme
 kEdeg  Enzyme Degredation
 kRdeg  RNA Degredation
 kPdeg  Protein Degredation
Equations
The differential of the variables were found as follows:
 dIin/dt = kIin*Iex  kIex*Iin + kIir*Ii  kIif*Iin*i
 dIi/dt = kIif*Iin*i  kIir*Ii
 di/dt = kIir*Ii  kIif*Iin*i + kDbr*Db  kDbf*i*Dunb
 dDb/dt = kDbf*i*Dunb  kDbr*Db
 dDunb/dt = kDbr*Db  kDbf*i*Dunb
 dRe/dt = ktscribe*Dunb  kRdeg*Re
 dE/dt = ktslate*Re  kEdeg*E + kCr*CC + kP*CC  kCf*E*Si
 dSi/dt = ksin*So  kCf*E*Si + kCr*CC
 dCC/dt = kCf*E*Si  kCr*CC  kP*CC
 dP/dt = kP*CC  kPdeg*P
Assumptions
In order to determine the initial or steady state concentrations of the involved species and to determine the rate constants, the following assumptions were made:
IPTG Diffusion
First, Fick's Law of Diffusion was modeled through MATLAB. The diffusion constant used was 220um^2/s.[4]
It was assumed that IPTG was not consumed nor degraded
We also assumed that IPTG uptake was minor was compared to the concentration in the biofilm, and so that the external IPTG was determined solely by Fick's Law, not by internalization.
Constant Determination
Initially, the following initial concentration values were assumed:
 Iex: Described in the previous section
 Iin: Internal IPTG initially assumed to be 0
 Ii: 0, because no IPTG means IPTGrepressor complex
 i: Value acquired from [5] as between 10^5 and 10^4 and adjusted to 9.973*10^5
 Db: Calculated, knowing the expression vector is a 200 copy number plasmid; of the DNA, approximated that 90.08% is bound
 Dunb: Calculated, knowing the expression vector is a 200 copy number plasmid; of the DNA, approximated that 9.92% is bound
 Re: Determined to be 1.82735*10^4 by achieving steady state with the model
 E: Determined to be 6.08*10^4 by achieving steady state with the model
 So: External Substrate calculated by using amount added in enriched media as 33.9
 Si: Determined from [3] to be 10^1
 C: Determined to be 3*10^5by achieving steady state with the model
 P: Determined to be 1*10^4 by achieving steady state with the model
The rate constant values were assumed to be the following:
 kIin: .05 yielded reasonable diffusion times
 kIex: .05 since simple diffusion, the internal rate constant must be similar if not identical to the external rate constant
 kIif: .01 yielded reasonable repressor/Inducer interactions
 kIir: .01 yielded reasonable repressor/Inducer interactions
 kDbf: 100 yielded reasonable DNA/repressor interactions
 kDbr: .0011 yielded reasonable DNA/repressor interactions
 ktscribe: .01 yielded reasonable production levels
 ktslate: .01 yielded reasonable production levels
 kRdeg: .003 yielded reasonable degredation
 kEdeg: .003 yielded reasonable degredation
 kPdeg: .003 yielded reasonable degredation
 ksin: 0.000000009 yielded reasonable internalization of substrate
 kCf: .01 yielded reasonable production levels
 kCr: .01 yielded reasonable production levels
 kP: .01 yielded reasonable production levels
Results
1. IPTG Diffusion
First, in order to determine the diffusion of IPTG when applied (sprayed) at the top layer throughout the biofilm, Fick's Diffusion equation was used to create a finitetimedifferential mathematical model, which is graphically displayed below.
Once sprayed, the model predicts that the IPTG diffuses down the biofilm and stabilizes throughout the biofilm layer.
2. Effect of Inducer on Product Generation
As inducer levels are easily experimentally adjustable, we wanted to know the effect of the adjustment.
In the figure, the blue line represents product levels when induced and the green line represents product levels when not induced.
As shown above, the model predicts that induction leads to higher product levels.
3. Effect of Repressor on Product Generation
Repressor levels are also experimentally adjustable by changing the repressor producing segment of the part, and so we wanted to know the effect of the adjustment.
In the figure above, the green line represents product levels with the assumed repressor level and the blue line represents product levels when the repressor level is doubled.
The figure is a bit misleading, as a system with different repressor levels will have a different initial steady state. Regardless, the model predicts that more repressor leads to significantly less product.
4. Effect of Promoter on Product Generation
The promoter preceding the gene can be changed to different promoters, and we wanted to know the effect of this adjustment.
In the figure above, the blue line represents product with the assumed rate constant for transcription (which would be affected by the promoter) and the green line represents product when that value is doubled.
As shown above, the model predicts that a more efficient promoter would increase product production.
5. Effect of Ribosome Binding Site on Product Generation
The Ribosome Binding Site preceding the gene can be changed to different Ribosome Binding Sites, and we wanted to know the effect of this adjustment.
In the figure above, the green line represents product with the assumed rate constant for translation (which would be affected by the ribosome binding site) and the blue line represents product when that value is doubled.
As shown above, the model predicts that a more efficient ribosome binding site would increase product production.
6. Effect of DNA Copy Number on Product Generation
The DNA part could be ligated into a different plasmids with varying copy numbers; we wanted to know the effect of this adjustment.
In the figure above, the blue line represents product with the assumed DNA concentration value (which would be affected by the plasmid copy number) and the green line represents product when that value is doubled.
As shown above, the model predicts that varying copy numbers would vary product production.
Future Considerations
Although the this model, as shown, captures the topology of our engineered network, its predictive prowess can be improved by obtaining constants and parameters from empirical observations.
In acquiring data, using the actual Chitin vector would be most helpful, but if not viable, then an alternative method is to characterize the system using GFP instead of CHS3 and fluorescence as an indicator of product generation. The resulting data could be used to fit the parameters of the model.
Possible future work:
 Currently the model predicts the final product expression level between the top layer and bottom layer of a gradientinduced system as only about 0.00001%. Determine if this is primarily due to model inaccuracy or the inefficacy of diffusionbased layer differential induction method.
 Currently the model only predicts only a 10% increase in product when induced by what is suspected to be a significant concentration of inducer.
 The model assumes constant substrate production rate; this assumption may or may not be accurate.
References
1. A novel structured kinetic modeling approach for the analysis of plasmid instability in recombinant bacterial cultures
William E. Bentley, Dhinakar S. Kompala
Article first published online: 18 FEB 2004
DOI: 10.1002/bit.260330108
http://onlinelibrary.wiley.com/doi/10.1002/bit.260330108/pdf
2. Mathematical modeling of induced foreign protein production by recombinant bacteria
Jongdae Lee, W. Fred Ramirez
Article first published online: 19 FEB 2004
DOI: 10.1002/bit.260390608
http://onlinelibrary.wiley.com/doi/10.1002/bit.260390608/pdf
3. Pool Levels of UDP NAcetylglucosamine and UDP NAcetylglucosamineEnolpyruvate in Escherichia coli and Correlation with Peptidoglycan Synthesis
DOMINIQUE MENGINLECREULX, BERNARD FLOURET, AND JEAN VAN HEIJENOORT*
E.R. 245 du C.N.R.S., Institut de Biochimie, Universit' ParisSud, Orsay, 91405, France
Received 9 February 1983/Accepted 15 March 1983
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC217602/pdf/jbacter002470262.pdf
4. Diffusion in Bioﬁlms
Philip S. Stewart
Center for Bioﬁlm Engineering and Department of Chemical Engineering, Montana
State University–Bozeman, Bozeman, Montana, 597173980
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC148055/pdf/0965.pdf
5. Regulation of the Synthesis of the Lactose Repressor
PATRICIA L. EDELMANN' AND GORDON EDLIN
Department of Genetics, University of California, Davis, California 95616
Received for publication 21 March 1974
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC245824/pdf/jbacter003350105.pdf
MATLAB mfile
%IPTG PREDETERMINATION
%finite difference method
%diffusion equation (Fick's 2nd Law)
%c=c0*(erfc*x/sqrt(2*D*t))
%D*(Ci+12C+Ci1)/dx^2 = Ci/dt
%x goes from 0 (top) to 100 micrometers
%D=IPTG is a modified monosaccharide, so we can estimate from Table 1 that
%its diffusion coefficient in water at 25°C will be ca. 6.5 × 10?6 cm2 s?1.
%Scaling to 37°C and taking De/Daq to be 0.25, De is found to be 2.2 × 10?6 cm2 s?1
%http://www.ncbi.nlm.nih.gov/pmc/articles/PMC148055/
%%
clear
clc
%INITIALIZE
D=220; %um^2/s
c0=14.298; %mg so 3ml spray of 20mM iptg
%in comparison 23.83mg of iptg is in 5ml 20mM
dx=1; %um
xmax=100; %um; 100um total
dt=.002; %s
tmax=10; %s; 1 hour total
C=zeros(xmax/dx,tmax/dt); %rows = same time %also, (x,t) x is row, t is column
C_Rate=zeros(xmax/dt,1);
C(1,1)=c0;
%Time Step
for t=1:1:tmax/dt1; %s
for x=1:1:(xmax/dx) %um
if x==1%account for x=1 boundary condition
C_Rate(x)=D*(C(x+1,t)C(x,t))/(dx^2);
elseif x==xmax/dx %account for x=xmax boundary condition
C_Rate(x)=D*(C(x1,t)C(x,t))/(dx^2);
else%BULK
C_Rate(x)=D*(C(x1,t)+C(x+1,t)2*C(x,t))/(dx^2);
end
C(x,t+1)=C(x,t)+C_Rate(x)*dt;
end
t*.002
end
%%
%Compile into 1s parts
%newC=zeros(100,tmax/dt/500);
counts=1;
for t=1:500:tmax/dt1 %.002*x=1;
newC(:,counts)=C(:,t);
counts=counts+1;
end
%%
%visualize newC
for t=1:1:tmax/dt/500
XX=0:dx:xmaxdx;
YY=newC(:,t);
plot(XX,YY,'k.');
axis([0 100 0 2])
text(50,1.8,sprintf('Time is: %g s', t*dt));
text(50,1.7,sprintf('IPTG Mass balance is: %g mg', sum(C(:,t))));
xlabel('Distance from surface (um)')
ylabel('IPTG (mg)')
Mov(t)=getframe();
%pause(1);
end
%%
%VISUALIZE
blah=1;
for t=1:7:tmax/dt
XX=xmaxdx100:dx:0100;
YY=C(:,t);
plot(YY,XX,'k.');
axis([0 2 100 0])
text(1.2,30,sprintf('Time: %g s', t*dt));
%text(50,1.7,sprintf('IPTG Mass balance is: %g mg', sum(C(:,t))));
ylabel('Distance from surface (um)')
xlabel('IPTG (mg)')
VID(blah)=getframe(gcf);
blah=blah+1;
end
% Updated kinetics model
%{
Iex  external inducer or iptg
Iin  internal inducer or iptg
Ii  lac inhibitor / iptg complex
i  lac inhibitor
Db  DNA bound to inhibitor
Dunb  DNA not bound to inhibitor
R  mRNA
E  Enzyme Chitin Synthase
So  External Substrate  NAcetyl Glucosamine
Si  Internal Substrate  NAcetyl Glucosamine
CC  Enzyme Substrate Complex
P  Protein  Chitin
%}
dt=1;
tmax=3600;
dx=1;
xmax=100;
%General Array Structure: (Depth (um), Time (s))
%Depth = 0100 micrometers
%Time = 0 3600 seconds
preset=zeros(xmax,tmax);
Iin=preset;Ii=preset;i=preset;Db=preset;Dunb=preset;Re=preset;E=preset;Si=preset;CC=preset;P=preset;
%initial concentration
a=load('newC.mat'); %Iex is diff  assume cell intake << exist
Iex=zeros(100,3600);%a.newC();%
Iin(:,1)=0; %initially IPTG inside the cell is 0
Ii(:,1)=0; %no iptg, no Ii
i(:,1)=0.9973*10^4; %10^7 to 10^8 M which is 10^4 to 10^5 mM http://www.ncbi.nlm.nih.gov/pmc/articles/PMC245824/pdf/jbacter003350105.pdf
%DNA  partsregistry  200 copy number  200/(6.23*10^23)/(6*10^16
%volume)*1000 = 5.53*10^4mM
dper=0.9008;
Db(:,1)=5.53*dper*10^4; %say 90% is bound no idea
Dunb(:,1)=5.53*(1dper)*10^4; %say 10% unbound no idea
Re(:,1)=1.82735*10^4; %initially, no RNA
E(:,1)=6.08*10^4; %initially no enzyme
So=33.9; %1 pill per plate ~ 750mg/100ml /221.21g/mol /1000*1000*1000 is 33.9mM
Si(:,1)=10^1; %http://www.ncbi.nlm.nih.gov/pmc/articles/PMC217602/pdf/jbacter002470262.pdf
CC(:,1)=3*10^5; %initially 0
P(:,1)=1*10^4; %initially 0
%rate constants
kIin=.05;%safe to say diffuses within minutes
kIex=.05;%permeability must be similar
kIif=.01;
kIir=.01;
kDbf=100;
kDbr=.0011;
ktscribe=.01;
ktslate=.01;
kRdeg=.003;
kEdeg=.003;
kPdeg=.003;
ksin=0.000000009;
kCf=.01;
kCr=.01;
kP=.01;
%Begin Loop
%FILL IN THE BLANKS
%General Array Structure: (Depth (um), Time (s))
for tt=1:1:tmax/dt1
for xx=1:1:xmax/dx
%Predetermine diffusion
%dIex=DIF(xx,tt) + kIin*Iin(xx,tt)  kIex*Iex(xx,tt);
dIin=kIin*Iex(xx,tt) kIex*Iin(xx,tt) +kIir*Ii(xx,tt) kIif*Iin(xx,tt)*i(xx,tt);
dIi=kIif*Iin(xx,tt)*i(xx,tt) kIir*Ii(xx,tt);
di=kIir*Ii(xx,tt) kIif*Iin(xx,tt)*i(xx,tt) +kDbr*Db(xx,tt) kDbf*i(xx,tt)*Dunb(xx,tt);
dDb=kDbf*i(xx,tt)*Dunb(xx,tt) kDbr*Db(xx,tt);
dDunb=kDbr*Db(xx,tt) kDbf*i(xx,tt)*Dunb(xx,tt);
dRe=ktscribe*Dunb(xx,tt) kRdeg*Re(xx,tt);
dE=ktslate*Re(xx,tt) kEdeg*E(xx,tt) +kCr*CC(xx,tt) +kP*CC(xx,tt) kCf*E(xx,tt)*Si(xx,tt);
dSi=ksin*So kCf*E(xx,tt)*Si(xx,tt) +kCr*CC(xx,tt);
dCC=kCf*E(xx,tt)*Si(xx,tt) kCr*CC(xx,tt)  kP*CC(xx,tt);
dP=kP*CC(xx,tt) kPdeg*P(xx,tt);
%Iex(xx,tt+1)=Iex(xx,tt)+dIex*dt;
Iin(xx,tt+1)=Iin(xx,tt)+dIin*dt;
Ii(xx,tt+1)=Ii(xx,tt)+dIi*dt;
i(xx,tt+1)=i(xx,tt)+di*dt;
Db(xx,tt+1)=Db(xx,tt)+dDb*dt;
Dunb(xx,tt+1)=Dunb(xx,tt)+dDunb*dt;
Re(xx,tt+1)=Re(xx,tt)+dRe*dt;
E(xx,tt+1)=E(xx,tt)+dE*dt;
Si(xx,tt+1)=Si(xx,tt)+dSi*dt;
CC(xx,tt+1)=CC(xx,tt)+dCC*dt;
P(xx,tt+1)=P(xx,tt)+dP*dt;
end
tt*dt/tmax*100
end
