Team:SCUT-ChinaA/Model

Abstract

To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to simulate our system, with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. And, inspired of machine learning algorithms, we established an algorithm using gradient descent method to search for the optimal solution of $E_t$. Finally, we got results that were close to the results on some published articles we read, and hence we decided to design our experiment based on the model. Also, while building our model, we have developed a software tool which may be helpful for those who need to optimize a pathway.

Flux Balance Analysis

To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method.

\[maximize \quad f=c*v\] \[s.t. \quad A*v=0\] \[\qquad \qquad 0 \leq v \leq V_{max} \]

To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method.

$$S= \left[ \begin{matrix} & v1 & v2 & v3 & v4 & v5 & v6 & v7 & v8 & v9 & b1 & b2 \\ Acetyl-CoA & -1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ Acetoacetyl-CoA & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ HMG-CoA & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate-5-phosphate & 0 & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate-diphosphate & 0 & 0 & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 \\ IPP & 0 & 0 & 0 & 0 & 0 & 1 & 1 & -1 & -1 & 0 & 0 \\ DMAPP & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 0 & 0 \\ NPP & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & -1 \\ \end{matrix} \right]\tag{001} $$

To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) }\) (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method.

To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method. To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method. To improve the efficiency of producing limonene, we build a model to help us design our genetic machine. We use flux balance analysis to set up a relationship of input ( substrate ) and output (the produce rate of limonene), with the matrix of the pathway and the $V_{max}$ (calculated by $k_{cat}$ and $E_t$ ) of each reactions. After we get the relationship we optimize the output by finding the best solution of $E_t$ , using Newton method.