Team:Fudan/Software/Netro Fudan

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Nεtro_Fudan
iGEM 2017 FUDAN

This page is designed to help you better understand our model and make your own explorations. It is powered by our software, Nεtro, which enables you to design and explore your own gene transcripton network. In this page, we will illustrate four essential parts of our modelling work. The first two correspond to the Theoretical Basis part, and the last two the Network Modelling part.

(Note that all the parameters bellow are shown in natural logarithm.)

Probabilistic Model

λ1:   

λ2:   

τ1:   

τ2:   

kon:   

This is the result of using our probabilistic model to study a multi-binding process, which is corresponding to Figure 2 in 'Stochastic Modelling Of The Hill Equation' (Theoretical Basis). You may manipulate the parameters in the model and see how they will affect the probability distribution of the states of the receptor with three binding sites.

The diagram of the process is shown bellow (Figure 1 in Theoretical Basis) for review.

Probabilistic Model & Hill Equation

λ1:   

λ2:   

τ1:   

τ2:   

kon:   

konh:   

n:   

This is the result of our probabilistic model with that of the Hill Equation, which is corresponding to Figure 3 in 'Stochastic Modelling Of The Hill Equation' (Theoretical Basis). You may manipulate the parameters in the model and see how the two models are related and how they differ from each other.

Hill Equation Model

konN:   

konX:   

konY:   

n1:   

n2:   

n3:   

This is the result of using Hill Equation to analyze the property of our network, which is corresponding to Figure 5 in 'Stochastic Modelling Of The Gene Transcription Network' (Network Modelling). You may manipulate the parameters in the model and see how they will affect the response of the network.

The diagram of the network is shown bellow (Figure 2 in Network Modelling) for review.

Probabilistic Model

konN:   

konX:   

konY:   

λ1:   

λ2:   

τ1:   

τ2:   

This is the result of using the Probabilistic Model to analyze the property of our network, which is corresponding to Figure 9 in 'Stochastic Modelling Of The Gene Transcription Network' (Network Modelling). You may manipulate the parameters in the model and see how they will affect the response of the network. Note the difference from the Hill Equation Model.

Acknowledgement

We give our deepest thanks to Tian Huang, the leader of another team from Fudan University, Fudan_China. This web will never come to exist without his tremendous help on programming. We wish him good fortune in the wars to come.