Boot Camp: How to Simulate and Analyze Your Cancer Models with COPASI

(September 29,2014 - October 1,2014 )

Organizers


Stefan Hoops
VBI Research Facility, Virginia Polytechnic Institute and State University
Pedro Mendes
Center for Quantitative Medicine, University of Connecticut Health Center
Kathy O Hara
Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University

Mathematical models typically start out in simple form. One writes down a few differential equations, estimates the parameters, explores the output, and checks to see if it can predict behavior reasonably well. After that, the process begins to take on a life of its own. Since the model is greatly abstracted and simplified, it captures some aspects of the system, but fails in others, so new variables and more inputs are added. Alternative mechanisms are investigated. At some point, the question arises: How can one tell if this is a good model? The aim of this bootcamp is to provide tools to answer that question. We will frame the question in a way that respects both the biology and the underlying mathematics.Two organizers of the bootcamp, Pedro Mendes and Stefan Hoops, have spent the last twenty years creating a bridge between these paradigms, in the form of a software package called COPASI (COmplex PAthway SImulator)..  COPASI is a simulation software that allows one to translate the biochemical interactions between species into dynamical systems represented by sets of either stochastic or deterministic equations. The boot camp will consist of short lectures introducing concepts followed by simulation and analysis of various cancer models using the software. These models focus on a wide array of cancers (colon, lung) and different levels of representation (signaling pathways, cell cycle, cell populations). The first day is dedicated to basic simulation and model analysis, mainly steady state and dynamic simulation. The second day will focus on methods, like optimization, to interrogate models about specific properties of interest, such as which drug targets are the more effective. The third day will be focused on calibrating models against experimental data.

Preparation
Participants are encouraged to familiarize themselves with the COPASI software before arriving to the Boot Camp by:
• Viewing the short online videos on COPASI: www.copasi.org/VideoTutorials
• Reading a tutorial paper: “Computational Modeling of Biochemical Networks Using COPASI.” in Methods in Molecular Biology , Humana Press. 500:17-59.
• Downloading and installing the software on their computers (help will be available at the Boot Camp for those who have not done so): www.copasi.org

During the Boot Camp participants will use the software to analyze models that have been previously published. Getting familiar with these publications beforehand will be very helpful. If some of these papers are too technical/mathematical for you, read the introduction and discussion so that you have an overview of the work and the biological aspects, rather than trying to understand it completely – leave the technical aspects to the Boot Camp. The following papers will be discussed:

• Huang CY, Ferrell JE Jr (1996) Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc. Natl. Acad. Sci. USA 93(19):10078-10083. doi:10.1073/pnas.93.19.10078 PMID:8816754
• Kholodenko BN (2000) Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 267(6):1583-8 doi:10.1046/j.1432-1327.2000.01197.x PMID:10712587
• Bianconi F, Baldelli E, Ludovini V, Crinò L, Flacco A, Valigi P (2012) Computational model of EGFR and IGF1R pathways in lung cancer: a systems biology approach for translational oncology. Biotechnol Adv. 30(1):142-53. doi:10.1016/j.biotechadv.2011.05.010 PMID:21620944
• Kim D, Rath O, Kolch W, Cho KH (2007) A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Oncogene 26(31):4571-9. doi:10.1038/sj.onc.1210230 PMID:17237813
• Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G. (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnol. 20(4):370-5. doi:10.1038/nbt0402-370 PMID:11923843
• Conradie R, Bruggeman FJ, Ciliberto A, Csikász-Nagy A, Novák B, Westerhoff HV, Snoep JL (2009) Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex. FEBS J. 277(2):357-67. doi:10.1111/j.1742-4658.2009.07473.x PMID:20015233
• Smallbone K, Corfe BM (2014) A mathematical model of the colon crypt capturing compositional dynamic interactions between cell types. Int. J. Exp. Pathol. 95(1):1-7. doi:10.1111/iep.12062 PMID:24354351

Monday, September 29, 2014
Time Session
08:00 AM
08:30 AM

Breakfast

08:30 AM
09:30 AM

Introduction to COPASI (lecture) of the basic functionality of the software) - Overview

09:30 AM
10:30 AM

Defining models (tutorial) - Participants will be guided to enter a simple model of the MAPK cascade (Huang & Ferrel, 1996) in COPASI.

10:30 AM
11:00 AM

Break

11:00 AM
12:00 PM

Time course simulation (hands-on) - Participants will simulate time courses of the Huang & Ferrel model.

12:00 PM
01:30 PM

Lunch Break

01:30 PM
02:00 PM

Steady state analysis (hands-on) - Participants will find steady state(s) for the Huang & Ferrel model.

02:00 PM
02:30 PM

Ultrasensitivity (hands-on) - Participants will use the parameter scan function of the software to trace the ultrasensitivity property of the MAPK cascade (Huang & Ferrel model).

02:30 PM
02:45 PM

Break

02:45 PM
03:00 PM

SBML & BioModels database (lecture) - A quick (10 min) overview of the SBML file format and the BioModels database.

03:00 PM
04:00 PM

Oscillations in MAPK (hands-on) - Participants will download the model of Kholodenko (2000) and will carry out time course simulations to reveal oscillations, and will use the software to investigate how the oscillations can be suppressed.

Tuesday, September 30, 2014
Time Session
08:00 AM
08:30 AM

Breakfast

08:30 AM
09:30 AM

Sensitivity analysis (lecture) - An introduction to sensitivity analysis and metabolic control analysis.

09:30 AM
10:30 AM

Other MAPK models (hands-on) - Participants will download the models of Bianconi et al. (2012), Schoeberl et al. (2002) and Kim et al. (2007) and will simulate and analyze them applying the methods learned during the day.

10:30 AM
11:00 AM

Break

11:00 AM
12:00 PM

Optimization (lecture) - An introduction to optimization and some optimization algorithms.

12:00 PM
01:30 PM

Lunch Break

01:30 PM
03:00 PM

Optimization applied to MAPK models (hands-on) - Participants will apply optimization algorithms to the various MAPK models already downloaded, discovering the power of this methodology to find particular properties of interest in the models.

Wednesday, October 1, 2014
Time Session
08:00 AM
08:30 AM

Breakfast

08:30 AM
09:00 AM

Hybrid models with discrete events (lecture) - A short lecture on continuous (ODE) models containing discrete events.

09:00 AM
10:00 AM

Cell cycle (hands-on) - Participants will download the model of Conradie et al. (2009) of cell cycle. Participants will familiarize with Events and use these to measure the cell cycle period.

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM

Using events as probes (hands-on) - Applying discrete events to the Kholodenko (2000) model to act as probes measuring oscillation period.

11:00 AM
12:00 PM

Parameter estimation (lecture) - An introduction to parameter estimation.

12:00 PM
01:30 PM

Lunch Break

01:30 PM
02:30 PM

Parameter estimation practice (hands-on) - Participants will carry out parameter estimation to the Kholodenko (2000) model.

02:30 PM
03:00 PM

Break

03:00 PM
04:00 PM

Colon crypt model (hands-on) - Participants will download a model of colon crypt by Smallbone & Corfe (2014) and apply all the techniques learned to this model. In particular they will carry out parameter estimation like in the publication.

Name Email Affiliation
Albasini Mourao, Marcio Duarte albasinimourao.1@osu.edu College of Arts and Sciences, Mathematical Biosciences Institute
Arango, Daniel arango-tamayo.1@osu.edu Internal Medicine and Molecular Genetics, The Ohio State University
Bates, Dan bates@math.colostate.edu Department of Mathematics, Colorado State University
Blachly, James james.blachly@osumc.edu Internal Medicine, The Ohio State University
Branson, Owen branson.103@osu.edu Ohio State Biochemistry Program, Ohio State University
Cebulla, Colleen colleen.cebulla@osumc.edu Ophthalmology and Visual Science, The Ohio State University
Chen, James james.chen@osumc.edu Biomedical Informatics & Medical Oncology, Ohio State Wexner Medical Center
Devine, Raymond devine.82@osu.edu Nursing, The Ohio State University
Duarte Sanmiguel, Silvia sanmiguelsilvia84@gmail.com Mollecular Genetics, The Ohio State University
Feldges, Robert feldges.1@osu.edu Mathematics, The Ohio State University
Fessel, Kimberly fessel.6@mbi.osu.edu Mathematical Biosceinces Institute, The Ohio State University
Flores Castillo, Nicolas castillo.173@osu.edu Department of Statistics, Rice University
Hoops, Stefan shoops@vbi.vt.edu VBI Research Facility, Virginia Polytechnic Institute and State University
Iyiola, Olaniyi samuel@kfupm.edu.sa Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals
Kanji, Suman suman.kanji@osumc.edu Radiation Oncology, The Ohio State University
Kim, Jae Kyoung kim.5052@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Li, Ximing xl285608@ohio.edu Biological Sciences, Ohio University
Lu, Rong lu.550@buckeyemail.osu.edu Biostatistics, The Ohio State University
Madrasi, Kumpal madrasi_k@mercer.edu Pharmacy Practice, Mercer University
Mendes, Pedro mendes@vt.edu Center for Quantitative Medicine, University of Connecticut Health Center
Meyring-Wosten, Anna anna.meyring-wosten@rriny.com Renal Research Institute, Renal Research Institute
O Hara, Kathy kohara1@vbi.vt.edu Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University
Palanichamy, Kamalakannan pk@osumc.edu Radiation Oncology, The Ohio State University
Patterson, Catherine catherine-patterson@uiowa.edu Applied Mathematical and Computational Sciences, University of Iowa
Rambani, Komal komal.rambani@osumc.edu IBGP, OSU
Rezaei, Mohammad rezaei.4@osu.edu Biophysics Graduate Program, Ohio State University
Rezaei Yousefi, Mohammadmahdi rezaeiyousefi.1@osu.edu Electrical and Computer Engineering, The Ohio State University
Taslim, Cenny taslim.2@osu.edu Statistics/Comprehensive Cancer Center, The Ohio State University
Toupo, Danielle Dpt35@cornell.edu Applied mathematics, Cornell University
Walk, Julia julia-walk@uiowa.edu Applied Mathematical and Computational Sciences, University of Iowa
Yilmaz, Selen AyseSelen.yilmaz@osumc.edu Biomedical Informatics, OSU Wexner medical center
Zhang, Tongli tongli.zhang@bioch.ox.ac.uk Biochemistry, University of Oxford