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, we have developed a software which may be helpful for those who need to optimize a pathway while building our model.

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.