Difference between revisions of "Team:Fudan/Model"

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       inherent randomness and the phenotypical properties of biochemical systems. As a result, we provide a more fundamental
 
       inherent randomness and the phenotypical properties of biochemical systems. As a result, we provide a more fundamental
 
       explanation for the traditional Hill Equation while achieving
 
       explanation for the traditional Hill Equation while achieving
       higher accuracy and flexibility.</br> <a href="2017.igem.org/Team:Fudan/Model/HE">Click me if you dare!</a></br></p>
+
       higher accuracy and flexibility.</br>  
 +
      <h class="notation dark-orange">◆ [ Click the button! ] Click me if you dare!</h></br></p>
 
       </td>
 
       </td>
 
     </tr>
 
     </tr>
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       network and how to make stochastic simulation based on it. You would
 
       network and how to make stochastic simulation based on it. You would
 
       see how life creates order from chaos and gain mush more insights into
 
       see how life creates order from chaos and gain mush more insights into
       the vast and numerous biochemical reactions in cell from our model.</br> <a href="2017.igem.org/Team:Fudan/Model/GET">Cannot wait to explore!</a></p>
+
       the vast and numerous biochemical reactions in cell from our model.</br>  
 +
      <h class="notation dark-orange">◆ [ Click the button! ] Cannot wait to explore!</h></p>
 
       </td>
 
       </td>
 
       <td>
 
       <td>

Revision as of 17:53, 1 November 2017

  • Theoretical Basis

    Build up the Probabilistic Model to analyze biochemical reactions in cell and achieve higher accuracy and flexibility.

  • Network Modelling

    Apply the Probablistic Model to the analysis of our gene transcription network.



BACKGROUND...


For SwordS to generate an antigen density-dependent, triple HCC therapeutic response pattern, design of the gene transcription network (GTN) plays a central role in our project. Traditional method to analyze a GTN is using the Hill Equation to describe the relation between each pair of transcription factor and its receptor. However, we found it incomprehensible to apply the Hill Equation while studying specific biochemical reactions in cell like TF-Receptor binding process.

The Hill Equation concerns all about concentrations of substances in a system, and is itself founded on the theory of chemical equilibrium. However, biochemical reactions in a single cell cannot directly influence the reactions in another cell. Meanwhile, the receptors we are interested in usually exit in a small amount in a single cell. Specially, we are often concerned about a single gene site in the nucleus. Thus, there will be neither chemical equilibrium nor the concept of concentration. Thus, the Hill Equation appears to be fundamentally flawed in such situations. Furthermore, the Hill Equation is incapable of studying the behaviours of a half-bound complex, which we believe will be rather important in future researches. Since its parameters are substantially statistical, it cannot provide enough accuracy and flexibility either, especially in field like disease treatment where extreme fine tuning is critical.

...To create a more rigorous method to analyze gene transcription networks, and more basically, biochemical reactions in cell, we take a probabilistic perspective towards the issue...



In the Theoretical Basis part, we build up a Probabilistic Model by introducing some key concepts from Probability Theory and the theory of Markov Chain. We apply our model to the analysis of multi-binding process in cell, and further construct a concise method of simulation. Our model reveals the relationship between the inherent randomness and the phenotypical properties of biochemical systems. As a result, we provide a more fundamental explanation for the traditional Hill Equation while achieving higher accuracy and flexibility.
◆ [ Click the button! ] Click me if you dare!

In the Network Modelling part, we apply the Probabilistic Model built up previously to analyze our gene transcription network. We demonstrate how to extend our model from a single reaction to a network and how to make stochastic simulation based on it. You would see how life creates order from chaos and gain mush more insights into the vast and numerous biochemical reactions in cell from our model.
◆ [ Click the button! ] Cannot wait to explore!

◆ Want to better understand our model, or want to analyze your own gene transcription network in a convenient and elegant way, see our [Software]!