1. iGEM 2008 Tutorial Modeling

2. What? Model A model in science is a physical, mathematical, or logical representation of a system of entities, phenomena, or processes. Simulation A simulation is the implementation of a model over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. It is useful for testing, analysis or training where real-world systems or concepts can be represented by a model. Modeling Modeling refers to the process of generating a model as a conceptual representation of some phenomenon.

3. Kind of models Black box: purpose is to summarize the system compactly, not to explain how the system works White box: purpose is to account for physics, statistics,... of the system

4. Example: Chemical Reactor White boxBlack box Model based on reaction equations Model based on input- output data (e.g. ARX, neural network, …) We will work with white box models Example:

5. What we get are systems of ordinary differential equations (ODE’s), which can be solved using MATLAB.

7. Usefulness of modeling Costs of experiments reduce Simulations are much faster  number of experiments increase No danger! BUT simulations represent only part of the real world!

8. Matlab How to model and simulate in Matlab: –Basic: m-files –Advanced: Simulink –Specific: SimBiology toolbox (*) Alternative: CellDesigner (*) Useful link: http://www.mathworks.com/company/events/webinars/webinarconf.html?id=30351 &language=en or google: matlab webminar simbiology (2nd hit) http://www.mathworks.com/company/events/webinars/webinarconf.html?id=30351 &language=en

9. SimBiology What? SimBiology® extends MATLAB® with tools for modeling, simulating, and analyzing biochemical pathways. You can create your own block diagram model using predefined blocks. You can manually enter in compartments, species, parameters, reactions, events, rules, kinetic laws, and units, or read in Systems Biology Mark-Up Language (SBML) models.

12. iGEM-modeling Most interesting information about iGEM- modeling: ETH Zürich 2006/2007 We’ll summarize some crucial aspects and discuss the modeling process of ETH Zürich 2007

13. Parameter estimation & sensitivity analysis –Most difficult and laborious part of modeling –Most parameters not known Solution: sensitivity analysis  Which parameters have effect on which states ? Role of modeling Important is the interaction between modeling and experiments: modeling is not a precursor phase of experiment and synthesis, it is part of the design cycle.

14. ETH Zürich 2007 Project: Top-down aproach: started with black box and recursively refined the system/model

15. Model Overview: Define inputs/outputs/system state variables Note: pay attention to noise sensitivity

16. Detailed Model detailed model of all interactions in the system: define desired behaviour + formalized descripetion of system  identify necessary biological components & interactions Result: three basic subparts: –Sensors –Memory –reporters

17. Final Design Bring the information about the sensors, memory and reporters together:

18. Mathematical Model Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is given by sets of coupled ordinary differential equations Simulation & Sensitivity Analysis