Modeling Guide

Introduction

The first step in modeling is always to choose wether you want to model your system in a stochastic or deterministic and time/state-discrete or time/state-continuous manner.

The most convenient way to select between a discrete and continuous model is to look at the behaviour of the variables in the real world, since it makes no sense to model single molecule interactions with quantities that include fractions of molecules. A model where the state variable is discrete in most cases limits you to a stochastical model. If you pick a continuous model the choice to model the system with a deterministic or stochastic ansatz is highly dependent on what characteristics of the system you want to analyse in your model. For quantitative data, like what amount of Proten XY is produced, a deterministic model fits your needs. For qualitative data, like the variance in the production of Protein XY.

The decision between a stochastic and deterministic models is sometimes also dependant on which parameters for the system are available or rather attainable.

The next step is to pick a level of detail. Here your decision should be based on what level of detail is feasible and what level of detail is needed and you should always choose the lower level of those two.The feasability again depends on the available parameters and how well the considered system is known.

In your implementation the the level of detail should be reflected by the tolerances that you pass to your solver and the amount of simplifications that you apply to your equations.

In our case we decided on a continuous deterministic model.

Mass Action Law

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Transcription

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Translation

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MATLAB Implementation

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