Difference between revisions of "Team:SCUT-ChinaA/Model"
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| + | 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. | ||
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| + | 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. | ||
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Revision as of 04:58, 5 October 2018
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 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.
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.
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.
$$S= \left[ \begin{matrix} & v1 & v2 & v3 & v4 & v5 & v6 & v7 & v8 & v9 & b1 & b2 \\ Acetyl-CoA & -1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ Acetoacetyl-CoA & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ HMG-CoA & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate-5-phosphate & 0 & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 \\ Mevalonate-diphosphate & 0 & 0 & 0 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 0 \\ IPP & 0 & 0 & 0 & 0 & 0 & 1 & 1 & -1 & -1 & 0 & 0 \\ DMAPP & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 0 & 0 \\ NPP & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & -1 \\ \end{matrix} \right]\tag{001} $$
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 ) 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.
| enzyme | Substrate | Turnover Number [1/s] | KM Value [mM] |
|---|---|---|---|
| ERG10 | acetyl-CoA | 2.1 | 0.33 |
| ERG13 | acetoacetyl-CoA, acetyl-CoA | 4.6 | acetoacetyl-CoA:0.0014, acetyl-CoA:0.05 |
| HMG1 | hydroxymethylglutaryl-CoA | 0.023 | 0.045 |
| ERG12 | mevalonate | 2.36 | 0.012 |
| ERG8 | phosphomevalonate | 3.4 | 0.0042 |
| ERG19 | (R,S)-5-diphosphomevalonate | 5.9 | 0.0091 |
| NDPS1 | isopentenyl diphosphate | 0.14 | 0.047 |