Computational Systems Biology
Sauro Lab
University of Washington
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Software Downloads:

1. SBW
2. JDesigner
3. Jarnac
4. WinSCAMP
5. Matlab Interface
6. Optimization
6. Bifurcation
 
maintained by Frank Bergmann
 
 
 
 

Workshop 2011, August 8th-12th

We are pleased to announce Joint User-Training Workshop “Developing Multi-Scale,Multi-Cell Developmental and Biomedical Simulations with CompuCell3D and SBW” that will be held at the Indiana University at Bloomington.

The course will focus on teaching the basics of multi-cell, multi-scale modeling using the open-source packages CompuCell3D and SBW. The workshop will be taught by many of the CompuCell3D and SBW developers. In addition to participating in lectures and hands-on exercises, each participant should prepare a 30 min presentation covering her/his area of research. Based on our previous experience such presentations lead to many future collaborations as well as make the workshop more scientifically stimulating event.

Event Dates: August 8th - August 12th, 2011.

Location: Biocomplexity Institute, Indiana University

CompuCell3D Workshop page

Course Schedule for Network Modeling

Date for this half of the course: 8th August to 12th August

On Day 1 the course starts at 9.30 am, from Day 2 onwards the course starts at 9 am.

See the PDF file for details

Schedule

An e-book copy of the text book “Enzyme Kinetics for Systems Biology” will be provided for free to workshop participants.

An option to purchase paper copies at a considerable discount will also be made available..

Download the e-book here. The copy is stored in a hidden web page. To obtain a copy take the current url:

http://www.sys-bio.org/sbwWiki/tutorials/bloomington2011/

and add to it the name of the person after which the workshop building is named, followed an underscore, followed by the room number where the workshop is held, i.e Building_RoomNumber

For paper back copies see: www.analogmachine.org or at Amazon: http://tinyurl.com/3gag92t

Software Notes

Jarnac Syntax Summary

See section at end for note on special event syntax for JarnacLite


Schedule and Course Notes

Day 1:

Topics

Notes

Day 2:

Topics

Notes

Day 3:

Topics

Negative Feedback (slides)

Oscillators (slides)

Cascades (slides)

Structural Analysis, Moiety Conservation (slides)

Large Network Project

Identify all the motifs in this network and describe the overall function of the network.

Large Network


Day 4:

Topics

Notes

Day 5:

Project - work in team or individually.

Download a modeling paper.

Extract the model and enter it into the software.

Reproduce one or more of the simulations described in the paper.


Select a model from the following list or one of your own choice:

Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells Nature Reviews Cancer 11, 523-532 (July 2011) John J. Tyson, William T. Baumann, Chun Chen, Anael Verdugo1, Iman Tavassoly, Yue Wang, Louis M. Weiner & Robert Clarke

Rattanakul C, Lenbury Y, Krishnamara N, Wollkind DJ. Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy. Biosystems 2003 Jun;70(1):55-72.

Eur J Biochem. 2000 Mar;267(6):1583-8. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Kholodenko BN.

Gardner TS, Dolnik M, Collins JJ. A theory for controlling cell cycle dynamics using a reversibly binding inhibitor. Proc Natl Acad Sci U S A 1998 Nov;95(24):14190-5. Center for BioDynamics and Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA

Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature 2000 Jan;403(6767):335-8. Department of Molecular Biology and Physics, Princeton University, New Jersey 08544, USA.

Poolman MG, Assmus HE, Fell DA. Applications of metabolic modelling to plant metabolism. J Exp Bot. 2004 May;55(400):1177-86. Epub 2004 Apr 8.

Goldbeter A. A model for circadian oscillations in the Drosophila period protein (PER). Proc Biol Sci 1995 Sep;261(1362):319-24.

Rohwer JM, Botha FC. Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data. Biochem J 2001 Sep;358(Pt 2):437-45.

Markevich NI, Hoek JB, Kholodenko BN. Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J Cell Biol 2004 Feb;164(3):353-9.

Smolen P, Hardin PE, Lo BS, Baxter DA, Byrne JH. Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK. Biophys J 2004 May;86(5):2786-802.

Tyson JJ, Hong CI, Thron CD, Novak B. A simple model of circadian rhythms based on dimerization and proteolysis of PER and TIM. Biophys J 1999 Nov;77(5):2411-7.

Nielsen K, Sørensen PG, Hynne F, Busse HG. Sustained oscillations in glycolysis: an experimental and theoretical study of chaotic and complex periodic behavior and of quenching of simple oscillations. Biophys Chem 1998 May;72(1-2):49-62.

Chassagnole C, Noisommit-Rizzi N, Schmid JW, Mauch K, Reuss M Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. (ISSN: 0006-3592) (ESSN: 1097-0290)

Chassagnole C, Fell DA, Raïs B, Kudla B, Mazat JP. Control of the threonine-synthesis pathway in Escherichia coli: a theoretical and experimental approach. Biochem J 2001 Jun;356(Pt 2):433-44.

Holzhütter HG. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur J Biochem 2004 Jul;271(14):2905-22.

Bornheimer SJ, Maurya MR, Farquhar MG, Subramaniam S. Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction. Proc Natl Acad Sci U S A 2004 Nov;101(45):15899-904.

Goldbeter A, Dupont G, Berridge MJ. Minimal model for signal-induced Ca2+ oscillations and for their frequency encoding through protein phosphorylation. Proc Natl Acad Sci U S A 1990 Feb;87(4):1461-5.

Pritchard L, Kell DB. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem 2002 Aug;269(16):3894-904.

Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses Mol Syst Biol. 2007;3:144

Bruggeman FJ, Boogerd FC, Westerhoff HV. The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica. FEBS J 2005 Apr;272(8):1965-85.

Albeck JG, Burke JM, Aldridge BB, Zhang M, Lauffenburger DA, Sorger PK. Quantitative analysis of pathways controlling extrinsic apoptosis in single cells. Mol Cell 2008 Apr;30(1):11-25.

Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB. Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha. Syst Biol (Stevenage) 2004 Jun;1(1):93-103.

Ortega F, Garcés JL, Mas F, Kholodenko BN, Cascante M. Bistability from double phosphorylation in signal transduction. Kinetic and structural requirements. FEBS J 2006 Sep;273(17):3915-26.

Rovers W, Giersch C. Photosynthetic oscillations and the interdependence of photophosphorylation and electron transport as studied by a mathematical model. Biosystems 1995;35(1):63-73.


Videos

Some Basic Concepts

Introduction to Stoichiometric Networks

Sample JarnacLite Script Models

p = defn pathway

J0: $X0 -> S1; (VM1/Km1)*(X0-S1/Keq1)/(1+X0/Km1+S1/Km2);
J1: S1 -> S2;  (VM2/Km3)*(S1-S2/Keq2)/(1+S1/Km3+S2/Km4);
J2: S2 -> S3;  (VM3/Km5)*(S2-S3/Keq3)/(1+S2/Km5+S3/Km6);
J3: S3 -> $X1; VM4*S3/(Km7 + S3);
end;

p.X0 = 10;p.X1 = 0;
p.S1 = 0; p.S2 = 0; p.S3 = 0;
p.VM1 = 2; p.VM2 = 2; p.VM3 = 4; p.VM4 = 2.5;
p.Keq1 = 10; p.Keq2 = 2.3; p.Keq3 = 1.2;
p.Km1 = 0.67; p.Km2 = 1.3;
p.Km3 = 0.27; p.Km4 = 5.6;
p.Km5 = 0.77; p.Km6 = 2.1;
p.Km7 = 0.37; 

p.ss.eval;
println p.J0;
tmp = p.Vm1;
Jss = p.J0;

p.Vm1 = p.Vm1 * 1.05;
p.ss.eval;
println "C1 = ", ((p.J0 - Jss)/Jss)/0.05;
p.Vm1 = tmp;

tmp = p.Vm2;
p.Vm2 = p.Vm2 * 1.05;
p.ss.eval;
println "C2 = ", ((p.J0 - Jss)/Jss)/0.05;
p.Vm2 = tmp;

tmp = p.Vm3;
p.Vm3 = p.Vm3 * 1.05;
p.ss.eval;
println "C3 = ", ((p.J0 - Jss)/Jss)/0.05;
p.Vm3 = tmp;

tmp = p.Vm4;
p.Vm4 = p.Vm4 * 1.05;
p.ss.eval;
println "C4 = ", ((p.J0 - Jss)/Jss)/0.05;
p.Vm4 = tmp;

println p.cc (<p.J0>, p.Vm1);
println p.cc (<p.J0>, p.Vm2);
println p.cc (<p.J0>, p.Vm3);
println p.cc (<p.J0>, p.Vm4);

// Metabolic pathway

p = defn pathway

J0: $X0 -> S1; (VM1/Km1)*(X0-S1/Keq1)/(1+X0/Km1+S1/Km2);
J1: S1 -> S2;  (VM2/Km3)*(S1-S2/Keq2)/(1+S1/Km3+S2/Km4);
J2: S2 -> S3;  (VM3/Km5)*(S2-S3/Keq3)/(1+S2/Km5+S3/Km6);
J3: S3 -> $X1; VM4*S3/(Km7 + S3);
end;

p.X0 = 10;p.X1 = 0;
p.S1 = 0; p.S2 = 0; p.S3 = 0;
p.VM1 = 10; p.VM2 = 5; p.VM3 = 4; p.VM4 = 2.5;
p.Keq1 = 10; p.Keq2 = 2.3; p.Keq3 = 1.2;
p.Km1 = 0.67; p.Km2 = 1.3;
p.Km3 = 0.27; p.Km4 = 5.6;
p.Km5 = 0.77; p.Km6 = 2.1;
p.Km7 = 0.37; 

// Feedback model
// Negative feedback in a metabolic pathway

p = defn feedback


J0: $X0 -> S1; VM1*(X0-S1/Keq1)/(1+X0+S1+S4^h);
J1: S1 -> S2;  VM2*(10*S1-2*S2)/(1+S1+S2);
J2: S2 -> S3;  VM3*(10*S2-2*S3)/(1+S2+S3);
J3: S3 -> S4;  VM4*(10*S3-2*S4)/(1+S3+S4);
J4: S4 -> $X1; VM5*S4/(KS4+S4);


end;

p.X0 = 10;
p.X1 = 0;
p.S1 = 0;
p.S2 = 0;
p.S3 = 0;
p.S4 = 0;
p.VM1 = 10;
p.VM2 = 5;
p.VM3 = 4;
p.VM4 = 2.5;
p.VM5 = 2.5;
p.Keq1 = 10;
p.h = 2;
p.KS4 = 0.5;

p = defn cell
     S1 -> S2; k1*S1;
     S2 -> S3; k2*S2;
end;

p.S1 = 10;
p.S2 = 0;
p.S3 = 0;

p.k1 = 0.2;
p.k2 = 0.6;

p = defn cell
     
     ext S1, S3;
     
     // Sinusoidal input
     S1 = sin (time*alpha);
     
     S1 -> S2; k1*S1;
     S2 -> S3; k2*S2;
end;

p.S1 = 10;
p.S2 = 0;
p.S3 = 0;

p.k1 = 0.2;
p.k2 = 0.6;
p.alpha = 1.2;

// JarnacLite additional syntax (event control)
p = defn newModel
      $Xo -> S1;  v;
       S1 -> $X1; k*S1;
  
       at(gt(time,10)): v = 2;
       at(gt(time,20)): v = 1;
end;

p.v = 1;
p.k = 0.5;
p.Xo = 0.5; 

p = defn NewModel

$alpha -> $S2; 
   Vmax*alpha*(1 + alpha)^(n-1)/((1 + alpha)^n + L*(1 + beta)^n);

end;

p.alpha = 1;
p.beta = 0.1;
p.S2 = 0.1;
p.Inh = 0.1;
p.Vmax = 1;
//p.Km1 = 0.56;
p.n = 4;
p.KI = 0.56;
p.L = 10;

Elasticity calculation, at low S e = 4

p = defn cell
   $S1 -> $S2; Vmax*S1^n/(Km + S1^n);
end;

p.S1 = 0.10495;
p.S2 = 0;
p.Vmax = 1;
p.Km = 0.5;
p.n = 4;

p = defn cell 
   $AA -> P2; Vmax1*k1*P1/(1 + k1*P1); 
   P2 -> $w; k5*P2; 
   
   $AA -> P3; Vmax2*k2*P1/(1 + k2*P1 + k3*P2 + k4*P1*P2^8); 
   P3 -> $w; k5*P3; 
end; 

p.P2 = 0; 
p.P3 = 0; 
p.P1 = 0.01; 

p.AA = 0; 
p.Vmax1 = 5; 
p.k1 = 0.1;
 
p.Vmax2 = 1; 
p.k2 = 1; 
p.k3 = 0.1; 
p.k4 = 10; 
p.k5 = 0.1; 

// Possible chaotic system
// Uses to positive feedback loops in sequence

p = defn cell

var So, S1, S2;
ext Xo, X1;

$Xo -> So; k0*Xo;
So -> S1; k1*So+Vmax*So*pow(S1,n)/(Km1+pow(S1,n));
S1 -> S2; k2*S1+Vmax*S1*pow(S2,n)/(Km2+pow(S2,n));
S2 -> $X1; k3*S2;

end;

p.Xo = 1;
p.X1 = 0;
p.So = 0;
p.S1 = 1;
p.S2 = 0;
p.k0 = 0.04898571;
p.k1 = 0.01000333;
p.Vmax = 10.72801298;
p.n = 4.05601338;
p.Km1 = 15.78044574676;
p.k2 = 0.10411347;
p.Km2 = 5.66570501904;
p.k3 = 0.1;

CC3DSOSlibPy Installer

 
tutorials/bloomington2011.txt · Last modified: 2011/08/12 06:41 by hsauro
 

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