Difference between revisions of "Team:Freiburg/Results/Modeling"
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Mathematical modeling of biological networks therefore is a powerful tool to support experimental research, predicting the behaviour of networks both concerning time and space. These predictions can then be validated experimentally. | Mathematical modeling of biological networks therefore is a powerful tool to support experimental research, predicting the behaviour of networks both concerning time and space. These predictions can then be validated experimentally. | ||
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Two crucial biological processes within the DIA Chip are cell-free expression and the diffusion of proteins to the chemical surface. Cell-free expression limits the DIA chip regarding time, protein diffusion regarding space, determinig the maximum protein spot density one can still resolve. Therefore, we decided to model both cell-free expression and protein diffusion to predict the behaviour of protein concentration in time and space. | Two crucial biological processes within the DIA Chip are cell-free expression and the diffusion of proteins to the chemical surface. Cell-free expression limits the DIA chip regarding time, protein diffusion regarding space, determinig the maximum protein spot density one can still resolve. Therefore, we decided to model both cell-free expression and protein diffusion to predict the behaviour of protein concentration in time and space. | ||
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With a model like this, it is possible to find the minimum time needed to get a sufficiently high concentration of protein for antibody detection. This reduces the time needed for diagnosis using the DIA Chip. Furthermore, not only protein, but also other molecules' concentrations can be simulated. Therefore, bottlenecks within the biochemical system as well as limiting substrates can be predicted. Specifically increasing the concentration of such limiting substrates as well as substances involved in bottleneck processes may increase not only the overall protein yield but also speed up synthesis to account for an even faster diagnosis.<br> | With a model like this, it is possible to find the minimum time needed to get a sufficiently high concentration of protein for antibody detection. This reduces the time needed for diagnosis using the DIA Chip. Furthermore, not only protein, but also other molecules' concentrations can be simulated. Therefore, bottlenecks within the biochemical system as well as limiting substrates can be predicted. Specifically increasing the concentration of such limiting substrates as well as substances involved in bottleneck processes may increase not only the overall protein yield but also speed up synthesis to account for an even faster diagnosis.<br> | ||
Moreover, a cell-free expression model has huge potential for other applications. Hier fehlen noch Beispiele. | Moreover, a cell-free expression model has huge potential for other applications. Hier fehlen noch Beispiele. |
Revision as of 13:22, 12 September 2015
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Modeling
Note
Overview / Introduction
More and more information on biological networks, and a lot is already known yet.Understanding signaling pathways important for medical health issues, but also for building new networks from scratch using synthetic biology.However, the more components a network involves, the harder it gets to estimate how a whole network reacts to changes from both the inside and the outside. This is especially a problem, if entities change both in time and space. Experimentally studying all different components of a network for different possible conditions often is not only time-consumptive but also very expensive and therefore not viable.
Mathematical modeling of biological networks therefore is a powerful tool to support experimental research, predicting the behaviour of networks both concerning time and space. These predictions can then be validated experimentally.
Two crucial biological processes within the DIA Chip are cell-free expression and the diffusion of proteins to the chemical surface. Cell-free expression limits the DIA chip regarding time, protein diffusion regarding space, determinig the maximum protein spot density one can still resolve. Therefore, we decided to model both cell-free expression and protein diffusion to predict the behaviour of protein concentration in time and space.
With a model like this, it is possible to find the minimum time needed to get a sufficiently high concentration of protein for antibody detection. This reduces the time needed for diagnosis using the DIA Chip. Furthermore, not only protein, but also other molecules' concentrations can be simulated. Therefore, bottlenecks within the biochemical system as well as limiting substrates can be predicted. Specifically increasing the concentration of such limiting substrates as well as substances involved in bottleneck processes may increase not only the overall protein yield but also speed up synthesis to account for an even faster diagnosis.
Moreover, a cell-free expression model has huge potential for other applications. Hier fehlen noch Beispiele.
Detailed System
Transcription
ODE System
\[ tc0(1): \;\;\; \frac{dc^{RNAP}_{free}[t]}{dt}\: =\: c^{RNAP}_{bound}[t] \cdot k^{RNAP}_{gain} - c^{RNAP}_{free}[t] \cdot k^{RNAP}_{loss} \] \[ \hphantom{tc0(1): \;\;\; \frac{dc^{RNAP}_{free}[t]}{dt}\: =\: } + c^{RNAP}_{sigma}[t] \cdot k^{sigma}_{off} - c^{RNAP}_{free}[t] \cdot c^{sigma}[t] \cdot k^{sigma}_{on} \] \[ \hphantom{tc0(1): \;\;\; \frac{dc^{RNAP}_{free}[t]}{dt}\: =\: } + c^{RNAP}_{elongter}[-1][t] \cdot k^{RNAP}_{diss} \]
\[ tc0(2): \;\;\; \frac{dc^{sigma}[t]}{dt}\: =\: c^{sigma}_{bound}[t] \cdot k^{sigma}_{gain} - c^{sigma}[t] \cdot k^{sigma}_{loss} + c^{RNAPsigma}_{bound}[t] \cdot k^{sigma}_{off} \] \[ \hphantom{tc0(2): \;\;\; \frac{dc^{sigma}[t]}{dt}\: =\: } + c^{RNAP}_{sigma}[t] \cdot k^{sigma}_{off} - c^{RNAP}_{free}[t] \cdot c^{sigma}[t] \cdot k^{sigma}_{on} \] \[ \hphantom{tc0(2): \;\;\; \frac{dc^{sigma}[t]}{dt}\: =\: } + c^{RNAP}_{ini}[-1][t] \cdot k^{tc}_{prel} \]
\[ tc0(3.1): \;\;\; \frac{dc^{RNAP}_{sigmaint}[t]}{dt}\: =\: c^{RNAP}_{free}[t] \cdot c^{sigma}[t] \cdot k^{sigma}_{on} - c^{RNAP}_{sigmaint}[t] \cdot k^{sigma}_{off} \] \[ \hphantom{tc0(3.1): \;\;\; \frac{dc^{RNAP}_{sigmaint}[t]}{dt}\: =\: } + c^{RNAP}_{sigma}[t] \cdot k^{RNAPsigma}_{isore} - c^{RNAP}_{sigmaint}[t] \cdot k^{RNAPsigma}_{iso} \] \[ tc0(3.2): \;\;\; \frac{dc^{RNAP}_{sigma}[t]}{dt}\: =\: c^{RNAPsigma}_{bound}[t] \cdot k^{RNAP}_{gain} \cdot c^{RNAP}_{sigma}[t] \cdot k^{RNAP}_{loss} \] \[ \hphantom{tc0(3.2): \;\;\; \frac{dc^{RNAP}_{sigma}[t]}{dt}\: =\: } + \sum \limits_{i=0}^n c^{RNAP}_{on}[i][t] \cdot k^{RNAP}_{off} - c^{RNAP}_{sigma}[t] \cdot p^{DNA} \cdot l^{DNA} \cdot k^{RNAP}_{on} \] \[ \hphantom {tc0(3.2): \;\;\; \frac{dc^{RNAP}_{sigma}[t]}{dt}\: =\: } + c^{RNAP}_{sigmaint}[t] \cdot k^{sigma}_{iso} - c^{RNAP}_{sigma}[t] \cdot k^{RNAPsigma}_{isore} \]
\[ tc0(4): \;\;\; \frac{dc^{RNAP}_{on}[i][t]}{dt}\: =\: c^{RNAP}_{sigma}[t] \cdot p^{DNA} \cdot k^{RNAP}_{on} \] \[ \hphantom{tc0(4): \;\;\; \frac{dc^{RNAP}_{on}[i][t]}{dt}\: =\: } + c^{RNAP}_{on}[i\:-\:v^{RNAP}_{move} \cdot dt][t] \cdot (1 - k^{RNAP}_{off}) - c^{RNAP}_{on}[i][t] \]
\[ tc0(5): \;\;\; \frac{dc^{RNAP}_{prom}[t]}{dt}\: =\: \sum \limits_{i\:=\:n - v^{RNAP}_{move} \cdot dt}^n c^{RNAP}_{on}[i][t] \cdot (1 - k^{RNAP}_{off}) \] \[ \hphantom{tc0(5): \;\;\; \frac{dc^{RNAP}_{prom}[t]}{dt}\: =\: } + c^{RNAP}_{open}[t] \cdot k^{tc}_{closed} - c^{RNAP}_{prom}[t] \cdot k^{tc}_{open} \]
\[ tc0(6): \;\;\; \frac{dc^{RNAP}_{open}[t]}{dt}\: =\: c^{RNAP}_{prom}[t] \cdot k^{tc}_{open} - c^{RNAP}_{open}[t] \cdot k^{tc}_{closed} \] \[ \hphantom{tc0(6): \;\;\; \frac{dc^{RNAP}_{open}[t]}{dt}\: =\: } + c^{RNAP}_{ini}[-1][t] \cdot k^{tc}_{iniab} - c^{RNAP}_{open}[t] \cdot c^{ATP}[t] \cdot c^{X_1 TP}[t] \cdot k^{tc}_{ini1} \]
\[ tc0(7): \;\;\; \frac{dc^{RNAP}_{ini1}[t]}{dt}\: =\: c^{RNAP}_{open}[t] \cdot c^{ATP}[t] \cdot c^{X_1 TP}[t] \cdot k^{tc}_{ini1} - c^{RNAP}_{ini1}[t] \cdot c^{X_2 TP}[t] \cdot k^{tc}_{inix} \]
\[ tc0(8.1): \;\;\; \frac{dc^{RNAP}_{ini}[i][t]}{dt}\: =\: c^{RNAP}_{ini}[i-1][t] \cdot c^{X_i TP}[t] \cdot k^{tc}_{inix} - c^{RNAP}_{ini}[i][t] \cdot c^{X_i+1 TP}[t] \cdot k^{tc}_{inix}, \] \[ \hphantom{tc0(8.1): \;\;\; \frac{dc^{RNAP}_{ini}[i][t]}{dt}\: =\: } (i = 2, ..., l^{ini-1}) \] \[ tc0(8.2): \;\;\; \frac{dc^{RNAP}_{ini}[1][t]}{dt}\: =\: \frac{dc^{RNAP}_{ini1}[t]}{dt} \] \[ tc0(8.3): \;\;\; \frac{dc^{RNAP}_{ini}[-1][t]}{dt}\: =\: c^{RNAP}_{ini}[-2][t] \cdot c^{X_-1 TP}[t] \cdot k^{tc}_{inix} - c^{RNAP}_{ini}[-1][t] \cdot (k^{tc}_{iniab} + k^{tc}_{prel}) \]
\[ tc0(9): \;\;\; \frac{dc^{RNAP}_{prel}[t]}{dt}\: =\: c^{RNAP}_{ini}[-1][t] \cdot k^{tc}_{prel} - c^{RNAP}_{prel}[t] \cdot c^{X_1 TP}[t] \cdot k^{tc}_{elong} \]
\[ tc0(10.1): \;\;\; \frac{dc^{RNAP}_{elong}[i][t]}{dt}\: =\: c^{RNAP}_{elong}[i-1][t] \cdot (1 - prob^{tc}_{mm}) \cdot c^{X_i TP}[t] \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(10.1): \;\;\; \frac{dc^{RNAP}_{elong}[i][t]}{dt}\: =\: } - c^{RNAP}_{elong}[i][t] \cdot ((1 - prob^{tc}_{mm}) \cdot c^{X_1 TP}[t] \cdot k^{tc}_{elong} + prob^{tc}_{mm} \cdot (c^{NTPs}[t] - c^{X_1 TP}[t]) \cdot k^{tc}_{elong}) \] \[ \hphantom{tc0(10.1): \;\;\; \frac{dc^{RNAP}_{elong}[i][t]}{dt}\: =\: } + c^{RNAP}_{elongGreAB}[j + l^{mRNA}_{cl}][t] \cdot k^{GreAB}_{cat}, \] \[ \hphantom{tc0(10.1): \;\;\; \frac{dc^{RNAP}_{elong}[i][t]}{dt}\: =\: } (i = 2, ..., l^{elong-1}),\: (j = i\: and\: j = 2, ..., l^{elong} - l^{mRNA}_{cl}) \] \[ tc0(10.2): \;\;\; \frac{dc^{RNAP}_{elong}[1][t]}{dt}\: =\: c^{RNAP}_{prel}[t] \cdot (1 - prob^{tc}_{mm}) \cdot c^{X_1 TP}[t] \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(10.2): \;\;\; \frac{dc^{RNAP}_{elong}[1][t]}{dt}\: =\: } - c^{RNAP}_{elong}[1][t] \cdot ((1 - prob^{tc}_{mm}) \cdot c^{X_1 TP}[t] \cdot k^{tc}_{elong} + prob^{tc}_{mm} \cdot (c^{NTPs}[t] - c^{X_1 TP}[t]) \cdot k^{tc}_{elong}) \] \[ \hphantom{tc0(10.2): \;\;\; \frac{dc^{RNAP}_{elong}[1][t]}{dt}\: =\: } + c^{RNAP}_{elongGreAB}[l^{mRNA}_{cl}][t] \cdot k^{GreAB}_{cat} \] \[ tc0(10.3): \;\;\; \frac{dc^{RNAP}_{elong}[-1][t]}{dt}\: =\: c^{RNAP}_{elong}[-2][t] \cdot (1 - prob^{tc}_{mm}) \cdot c^{X_{-1} TP}[t] \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(10.3): \;\;\; \frac{dc^{RNAP}_{elong}[-1][t]}{dt}\: =\: } - c^{RNAP}_{elong}[-1][t] \cdot l^{mRNA} \cdot c^{pprot} * k^{pprot}_{on} \]
\[ tc0(11.1): \;\;\; \frac{dc^{RNAP}_{elongter}[i][t]}{dt}\: =\: (c^{RNAP}_{elongter}[i-1][t] - c^{RNAP}_{elongter}[i][t]) \cdot c^{ATP}[t] \cdot k^{pprot}_{cat} \] \[ \hphantom{tc0(11.1): \;\;\; \frac{dc^{RNAP}_{elongter}[i][t]}{dt}\: =\: } + c^{RNAP}_{elong}[i] \cdot c^{pprot} \cdot k^{pprot}_{on}, \] \[ \hphantom{tc0(11.1): \;\;\; \frac{dc^{RNAP}_{elongter}[i][t]}{dt}\: =\: } (i = 2, ..., l^{mRNA}-1) \] \[ tc0(11.2): \;\;\; \frac{dc^{RNAP}_{elongter}[-1][t]}{dt}\: =\: c^{RNAP}_{elongter}[-2][t] \cdot c^{ATP}[t] \cdot k^{pprot}_{cat} - c^{RNAP}_{elongter}[-1][t] * k^{RNAP}_{diss} \] \[ \hphantom{tc0(11.2): \;\;\; \frac{dc^{RNAP}_{elongter}[-1][t]}{dt}\: =\: } + c^{RNAP}_{elong}[-1][t] \cdot c^{pprot}[t] \cdot k^{pprot}_{on} \]
\[ tc0(12): \;\;\; \frac{dc^{mRNA}[t]}{dt}\: =\: c^{RNAP}_{elongter}[-1][t] \cdot k^{RNAP}_{diss} - c^{RNAse}_{onmRNA}[t] \cdot k^{RNAse}_{cat} \]
\[ tc0(13): \;\;\; \frac{dc^{RNAP}_{elongmm}[i][t]}{dt}\: =\: c^{RNAP}_{elong}[i-1][t] \cdot prob^{tc}_{mm} \cdot (c^{NTPs}[t] - c^{X_1 TP}[t]) \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(13): \;\;\; \frac{dc^{RNAP}_{elongmm}[i][t]}{dt}\: =\: } - c^{RNAP}_{elongmm}[i][t] \cdot c^{GreAB}[t] \cdot k^{GreAB}_{on} \]
\[ tc0(14): \;\;\; \frac{dc^{RNAP}_{elongGreAB}[i][t]}{dt}\: =\: c^{RNAP}_{elongmm}[i][t] \cdot c^{GreAB}[t] \cdot k^{GreAB}_{on} - c^{RNAP}_{elongGreAB}[i][t] \cdot k^{GreAB}_{cat} \]
\[ tc0(15): \;\;\; \frac{dc^{RNAse}[t]}{dt}\: =\: c^{RNAse}_{bound}[t] \cdot k^{RNAse}_{gain} - c^{RNAse}[t] \cdot k^{RNAse}_{loss} \] \[ \hphantom{tc0(15): \;\;\; \frac{dc^{RNAse}[t]}{dt}\: =\: } + (c^{RNAse}_{onmRNAcl}[t] + c^{RNAse}_{onmRNAab}[t] + c^{RNAse}_{onmRNA}[t]) \cdot k^{RNAse}_{cat} \] \[ \hphantom{tc0(15): \;\;\; \frac{dc^{RNAse}[t]}{dt}\: =\: } - (c^{mRNAcl}[t] + c^{mRNAab}[t] + c^{mRNA}[t]) \cdot c^{RNAse}[t] \cdot k^{RNAse}_{on} \]
\[ tc0(16): \;\;\; \left( \frac{dc^{RNAse}_{onmRNA}[t]}{dt} ,\: \frac{dc^{RNAse}_{onmRNAab}[t]}{dt} ,\: \frac{dc^{RNAse}_{onmRNAcl}[t]}{dt} \right) \] \[ \hphantom{tc0(16): \;\;\; } = c^{RNAse}[t] \cdot (c^{mRNA}[t],\: c^{mRNAab}[t],\: c^{mRNAcl}[t]) \cdot k^{RNAse}_{on} - \left( c^{RNAse}_{onmRNA}[t],\: c^{RNAse}_{onmRNAab}[t],\: c^{RNAse}_{onmRNAcl}[t] \right) \cdot k^{RNAse}_{cat} \]
\[ tc0(17): \;\;\; \frac{dc^{mRNAab}[t]}{dt}\: =\: c^{RNAP}_{ini}[-1][t] \cdot k^{tc}_{iniab} - c^{RNAse}_{onmRNAab}[t] \cdot k^{RNAse}_{cat} \]
\[ tc0(18): \;\;\; \frac{dc^{mRNAcl}[t]}{dt}\: =\: \sum \limits_{i=1}^n c^{RNAP}_{elongGreAB}[i][t] \cdot k^{GreAB}_{cat} + 2 \cdot c^{RNAse}_{onmRNA}[t] \cdot k^{RNAse}_{cat} - c^{RNAse}_{onmRNAcl}[t] \cdot k^{RNAse}_{cat} \]
\[ tc0(19.1): \;\;\; \frac{dc^{entity}_{bound}[t]}{dt}\: =\: c^{entity}[t] \cdot k^{entity}_{loss} - c^{entity}_{bound}[t] \cdot k^{entity}_{gain}, \] \[ \hphantom{tc0(19.1): \;\;\; \frac{dc^{entity}_{bound}[t]}{dt}\: =\: } (entity \notin \{RNAP, RNAPsigma\}) \]
\[ tc0(19.2): \;\;\; \left( \frac{dc^{RNAP}_{bound}[t]}{dt},\: \frac{dc^{RNAPsigma}_{bound}[t]}{dt} \right) \] \[ \hphantom{tc0(19.2): \;\;\; } = c^{RNAP}[t] \cdot k^{RNAP}_{loss} - c^{RNAP}_{bound}[t] \cdot k^{RNAP}_{gain} + \left( c^{RNAPsigma}_{bound}[t],\: - c^{RNAPsigma}_{bound}[t] \right) \cdot k^{sigma}_{off} \]
\[ tc0(20): \;\;\; \frac{dc^{pprot}[t]}{dt}\: =\: c^{pprot}_{bound}[t] \cdot k^{pprot}_{gain} - c^{pprot}[t] \cdot k^{pprot}_{loss} \] \[ \hphantom{tc0(20): \;\;\; \frac{dc^{pprot}[t]}{dt}\: =\: } + c^{RNAP}_{elongter}[-1][t] \cdot k^{RNAP}_{diss} - c^{RNAP}_{elong}[-1][t] \cdot l^{mRNA} \cdot c^{pprot}[t] \cdot k^{pprot}_{on} \]
\[ tc0(21): \;\;\; \frac{dc^{GreAB}[t]}{dt}\: =\: c^{GreAB}_{bound}[t] \cdot k^{GreAB}_{gain} - c^{GreAB}[t] \cdot k^{GreAB}_{loss} \] \[ \hphantom{tc0(21): \;\;\; \frac{dc^{GreAB}[t]}{dt}\: =\: } + \sum \limits_{i=1}^n c^{RNAP}_{elongGreAB}[i][t] \cdot k^{GreAB}_{cat} - \sum \limits_{i=1}^n c^{RNAP}_{elongmm}[i][t] \cdot c^{GreAB}[t] \cdot k^{GreAB}_{on} \]
\[ tc0(22): \;\;\; \frac{dc^{NTP}[t]}{dt}\: =\: - \sum \limits_{i=2, X_i=N}^n c^{RNAP}_{ini}[i-1][t] \cdot c^{X_i TP}[t] \cdot k^{tc}_{inix} \] \[ \hphantom{tc0(22): \;\;\; \frac{dc^{NTP}[t]}{dt}\: =\: } - c^{RNAP}_{prel}[t] \cdot (1 - prob^{tc}_{mm}) \cdot c^{X_1 TP}[t] \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(22): \;\;\; \frac{dc^{NTP}[t]}{dt}\: =\: } - \sum \limits_{i=2, X_i=N}^{n-1} c^{RNAP}_{elong}[i-1][t] \cdot (1 - prob^{tc}_{mm}) \cdot c^{X_i TP}[t] \cdot k^{tc}_{elong} \] \[ \hphantom{tc0(22): \;\;\; \frac{dc^{NTP}[t]}{dt}\: =\: } \left[ - c^{RNAP}_{open}[t] \cdot c^{ATP}[t] \cdot c^{X_1 TP}[t] \cdot k^{tc}_{ini1}\right]_{for X_1 = N} \] \[ \hphantom{tc0(22): \;\;\; \frac{dc^{NTP}[t]}{dt}\: =\: } \left[ - c^{RNAP}_{open}[t] \cdot c^{ATP}[t] \cdot c^{NTP}[t] \cdot k^{tc}_{ini1} - \sum \limits_{i=1}^{n-1} c^{RNAP}_{elongter}[i][t] \cdot c^{ATP}[t] \cdot k^{pprot}_{cat}\right]_{for N = A} \]
\[ tc0(23): \;\;\; \frac{dc^{NTPs}[t]}{dt}\: =\: \frac{dc^{ATP}[t]}{dt} + \frac{dc^{TTP}[t]}{dt} + \frac{dc^{GTP}[t]}{dt} + \frac{dc^{CTP}[t]}{dt} \]
Translation
ODE System
Amino Acid Activation
\[ tl0(1): \;\;\; \frac{dc^{AaxtRNASyn}[t]}{dt}\: =\: c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{AaxtRNASyn}_{cat2} \] \[ \hphantom{tl0(1): \;\;\; \frac{dc^{AaxtRNASyn}[t]}{dt}\: =\: } + c^{AaxtRNASyn}_{Aax}[t] \cdot k^{Aax}_{off} - c^{AaxtRNASyn}_{Aax}[t] \cdot c^{Aax}[t] \cdot k^{Aax}_{on} \]
\[ tl0(2): \;\;\; \frac{dc^{AaxtRNASyn}_{Aax}[t]}{dt}\: =\: c^{AaxtRNASyn}[t] \cdot c^{Aax}[t] \cdot k^{Aax}_{on} - c^{AaxtRNASyn}_{Aax}[t] \cdot k^{Aax}_{off} \] \[ \hphantom{tl0(2): \;\;\; \frac{dc^{AaxtRNASyn}_{Aax}[t]}{dt}\: =\: } - c^{AaxtRNASyn}_{Aax}[t] \cdot c^{ATP}[t] \cdot k^{AaxtRNASyn}_{cat1} \]
\[ tl0(3): \;\;\; \frac{dc^{AaxtRNASyn}_{Aaa}[t]}{dt}\: =\: c^{AaxtRNASyn}_{Aax}[t] \cdot c^{ATP}[t] \cdot k^{AaxtRNASyn}_{cat1} \] \[ \hphantom{tl0(3): \;\;\; \frac{dc^{AaxtRNASyn}_{Aaa}[t]}{dt}\: =\: } + c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{tRNA}_{off} - c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{AaxtRNASyn}_{cat2} \]
\[ tl0(4): \;\;\; \frac{dc^{AaxtRNASyn}_{AaatRNA}[t]}{dt}\: =\: + c^{AaxtRNASyn}_{Aaa}[t] \cdot c^{tRNA}[t] \cdot k^{tRNA}_{on} - c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{tRNA}_{off} \] \[ \hphantom{tl0(4): \;\;\; \frac{dc^{AaxtRNASyn}_{AaatRNA}[t]}{dt}\: =\: } - c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{AaxtRNASyn}_{cat2} \]
\[ tl0(5): \;\;\; \frac{dc^{AaxtRNA}[t]}{dt}\: =\: c^{AaxtRNASyn}_{AaatRNA}[t] \cdot k^{AaxtRNASyn}_{cat2} \] \[ \hphantom{tl0(5): \;\;\; \frac{dc^{AaxtRNA}[t]}{dt}\: =\: } + c^{AaxtRNA}_{EFTu}[t] \cdot k^{EFTu}_{off} - c^{AaxtRNA}[t] \cdot c^{EFTu}[t] \cdot k^{EFTu}_{on} \] \[ \hphantom{tl0(5): \;\;\; \frac{dc^{AaxtRNA}[t]}{dt}\: =\: } + c^{tl}_{el1mm}[t] \cdot k^{AaxtRNA}_{reject} \]
\[ tl0(6): \;\;\; \frac{dc^{ftrans}[t]}{dt}\: =\: c^{ftrans}_{MettRNAfMet}[t] \cdot k^{MettRNAfMet}_{off} - c^{ftrans}[t] \cdot c^{MettRNAfMet}[t] \cdot k^{MettRNAfMet}_{on} \] \[ \hphantom{tl0(6): \;\;\; \frac{dc^{ftrans}[t]}{dt}\: =\: } + c^{ftrans}_{MettRNAfMet}[t] \cdot c^{f}[t] \cdot k^{ftrans}_{cat} - c^{ftrans}[t] \cdot c^{fMettRNAfMet}[t] \cdot c^{t}[t] \cdot k^{ftrans}_{deg} \]
\[ tl0(7): \;\;\; \frac{dc^{ftrans}_{MettRNAfMet}[t]}{dt}\: =\: c^{ftrans}[t] \cdot c^{MettRNAfMet}[t] \cdot k^{MettRNAfMet}_{on} - c^{ftrans}_{MettRNAfMet}[t] \cdot k^{MettRNAfMet}_{off} \] \[ \hphantom{tl0(7): \;\;\; \frac{dc^{ftrans}_{MettRNAfMet}[t]}{dt}\: =\: } + c^{ftrans}[t] \cdot c^{fMettRNAfMet}[t] \dot c^{t}[t] \cdot k^{ftrans}_{deg} - c^{ftrans}_{MettRNAfMet}[t] \cdot c^{f}[t] \cdot k^{ftrans}_{cat} \]
\[ tl0(8): \;\;\; \frac{dc^{fMettRNAfMet}[t]}{dt}\: =\: c^{ftrans}_{MettRNAfMet}[t] \cdot c^{f}[t] \cdot k^{ftrans}_{cat} - c^{ftrans}[t] \cdot c^{fMettRNAfMet}[t] \cdot c^{t}[t] \cdot k^{ftrans}_{deg} \] \[ \hphantom{tl0(8): \;\;\; \frac{dc^{fMettRNAfMet}[t]}{dt}\: =\: } + c^{tl}_{ini2}[t] \cdot k^{fMettRNAfMet}_{off} - c^{tl}_{ini1}[t] \cdot c^{fMettRNAfMet}[t] \cdot k^{fMettRNAfMet}_{on} \]
Initialization
\[ tl0(9): \;\;\; \frac{dc^{70s}[t]}{dt}\: =\: c^{30s}[t] \cdot c^{50s}[t] \cdot k^{50s}_{bind} - c^{70s}[t] \cdot c^{IF3}[t] \cdot k^{IF3}_{on} \]
\[ tl0(10): \;\;\; \frac{dc^{70sIF3}[t]}{dt}\: =\: c^{70s}[t] \cdot c^{IF3}[t] \cdot k^{IF3}_{on} - c^{70sIF3}[t] \cdot k^{IF3}_{off} \] \[ \hphantom{tl0(10): \;\;\; \frac{dc^{70sIF3}[t]}{dt}\: =\: } - c^{70sIF3}[t] \cdot k^{50s}_{diss} \]
\[ tl0(11): \;\;\; \frac{dc^{30sIF3}[t]}{dt}\: =\: c^{70sIF3}[t] \cdot k^{50s}_{diss} - c^{30sIF3}[t] \cdot k^{IF3}_{off} \] \[ \hphantom{tl0(11): \;\;\; \frac{dc^{30sIF3}[t]}{dt}\: =\: } + c^{30sIF3IF1}[t] \cdot k^{IF1}_{off} - c^{30sIF3}[t] \cdot c^{IF1}[t] \cdot k^{IF1}_{on} \]
\[ tl0(12): \;\;\; \frac{dc^{30sIF3IF1}[t]}{dt}\: =\: c^{30sIF3}[t] \cdot c^{IF1}[t] \cdot k^{IF1}_{on} - c^{30sIF3IF1}[t] \cdot k^{IF1}_{off} \] \[ \hphantom{tl0(12): \;\;\; \frac{dc^{30sIF3IF1}[t]}{dt}\: =\: } + c^{tl}_{ini0}[t] \cdot k^{mRNA}_{off} - c^{30sIF3IF1}[t] \cdot c^{mRNA}[t] \cdot k^{mRNA}_{on} \]
\[ tl0(13): \;\;\; \frac{dc^{tl}_{ini0}[t]}{dt}\: =\: c^{30sIF3IF1}[t] \cdot c^{mRNA}[t] \cdot k^{mRNA}_{on} - c^{tl}_{ini0}[t] \cdot k^{mRNA}_{off} \] \[ \hphantom{tl0(13): \;\;\; \frac{dc^{tl}_{ini0}[t]}{dt}\: =\: } + c^{tl}_{ini1}[t] \cdot k^{IF2}_{off} - c^{tl}_{ini0}[t] \cdot c^{IF2}[t] \cdot k^{IF2}_{on} \]
\[ tl0(14): \;\;\; \frac{dc^{tl}_{ini1}[t]}{dt}\: =\: c^tl_ini0[t] * c^IF2[t] * k^IF2_on - c^tl_ini1[t] * k^IF2_off + c^tl_ini2[t] * k^fMettRNAfMet_off - c^tl_ini1[t] * c^fMettRNAfMet[t] * k^fMettRNAfMet_on tl0(15): dc^tl_ini2[t]/dt = c^tl_ini1[t] * c^fMettRNAfMet[t] * k^fMettRNAfMet_on - c^tl_ini2[t] * k^fMettRNAfMet_off c^tl_inidissIF3[t] * c^IF3[t] * k^IF3_bind - c^tl_ini2[t] * k^IF3_diss tl0(16): dc^tl_inidissIF3[t]/dt = c^tl_ini2[t] * k^IF3_diss - c^tl_inidissIF3[t] * c^IF3[t] * k^IF3_bind + c^tl_inion50s[t] * k^50s_off - c^tl_inidissIF3[t] * c^50s[t] * k^50s_on tl0(17): dc^tl_inion50s[t]/dt = c^tl_inidissIF3[t] * c^50s[t] * k^50s_on - c^tl_inion50s[t] * k^50s_off + c^tl_inidissIF1[t] * c^IF1[t] * k^IF1_bind - c^tl_inion50s[t] * k^IF1_diss tl0(18): dc^tl_inidissIF1[t]/dt = c^tl_inion50s[t] * k^IF1_diss - c^tl_inidissIF1[t] * c^IF1[t] * k^IF1_bind - c^tl_inidissIF1[t] * k^IF2_cat tl0(19): dc^tl_inicat[t]/dt = c^tl_inidissIF1[t] * k^IF2_cat - c^tl_inicat[t] * k^IF2_diss tl0(20): dc^IF1[t]/dt = c^30sIF3IF1[t] * k^IF1_off - c^30sIF3[t] * c^IF1[t] * k^IF1_on + c^tl_inion50s[t] * k^IF1_diss - c^tl_inidissIF1[t] * c^IF1[t] * k^IF1_bind tl0(21): dc^IF3[t]/dt = (c^70sIF3[t] + c^30sIF3[t]) * k^IF3_off - (c^70s + c^30s) * k^IF3_on + c^tl_ini2[t] * k^IF3_diss - c^tl_inidissIF3[t] * c^IF3[t] * k^IF3_bind tl0(22): dc^IF2[t]/dt = c^tl_ini1[t] * k^IF2_off - c^tl_ini0[t] * c^IF2[t] * k^IF2_on + c^IF2_GDP[t] * c^GTP[t] * k^IF2_reg - c^IF2[t] * c^GDP[t] * k^IF2_deg tl0(23): dc^IF2_GDP[t]/dt = c^tl_inicat[t] * k^IF2_diss + c^IF2[t] * c^GDP[t] * k^IF2_deg - c^IF2_GDP[t] * c^GTP[t] * k^IF2_reg
Elongation.
tl0(24.1): dc^tl_el0[i][t]/dt = c^tl_el0AaxtRNA[i][t] * k^AaxtRNAEFTu_off - c^tl_el0[i] * c^AaxtRNA_EFTu[t] * k^AaxtRNAEFTu_on + c^tl_el1mm[i][t] * k^tRNA_reject + c^tl_el3[i-1] * k^tRNA_dissel, (i = 2, ..., l^mRNAcodons) tl0(24.2): dc^tl_el0[1][t]/dt = c^tl_inicat[t] * k^IF2_diss + c^tl_el0AaxtRNA[0][t] * k^AaxtRNAEFTu_off - c^tl_el0[0] * c^AaxtRNA_EFTu[t] * k^AaxtRNAEFTu_on + c^tl_el1mm[0][t] * k^tRNA_reject tl0(25): dc^tl_el0AaxtRNA[i][t]/dt = c^tl_el0[i] * c^AaxtRNA_EFTu[t] * k^AaxtRNAEFTu_on - c^tl_el0AaxtRNA[i][t] * k^AaxtRNAEFTu_off - c^tl_el0AaxtRNA[i][t] * k^EFTu_cat, (i = 1, ..., l^mRNAcodons) tl0(26): dc^tl_el0cat[i][t]/dt = c^tl_el0AaxtRNA[i][t] * k^EFTu_cat - c^tl_el0cat[i][t] * k^EFTu_diss, (i = 1, ..., l^mRNAcodons) tl0(27): dc^tl_el1raw[i][t]/dt = c^tl_el0cat[i][t] * k^EFTu_diss - c^tl_el1raw[i][t] * k^tl_proof, (i = 1, ..., l^mRNAcodons) tl0(28): dc^tl_el1proof[i][t]/dt = c^tl_el1raw[i][t] * k^tl_proof - c^tl_el1proof[i][t] * (prob^tl_mm + (1 - prob^tl_mm)), (i = 1, ..., l^mRNAcodons) tl0(29): dc^tl_el1correct[i][t]/dt = c^tl_el1proof[i][t] * (1 - prob^tl_mm) - c^tl_el1correct[i][t] * k^tl_elong, (i = 1, ..., l^mRNAcodons) tl0(30): dc^tl_el2[i][t]/dt = c^tl_el1correct[i][t] * k^tl_elong + c^tl_el2EFG[i][t] * k^EFG_off - c^tl_el2[i][t] * c^EFG[t] * k^EFG_on, (i = 1, ..., l^mRNAcodons) tl0(31.1): dc^tl_el2EFG[i][t]/dt = c^tl_el2[i][t] * c^EFG[t] * k^EFG_on - c^tl_el2EFG[i][t] * k^EFG_off - c^tl_el2EFG[i][t] * k^EFG_cat, (i = 1, ..., l^mRNAcodons) tl0(31.2): dc^tl_el2EFGcat[i][t]/dt = c^tl_el2EFG[i][t] * k^EFG_cat - c^tl_el2EFGcat[i][t] * k^tl_trans, (i = 1, ..., l^mRNAcodons) tl0(31.3): dc^tl_el2EFGtrans[i][t]/dt = c^tl_el2EFGcat[i][t] * k^tl_trans - c^tl_el2EFGtrans[i][t] * k^EFG_diss (i = 1, ..., l^mRNAcodons) tl0(32): dc^tl_el3[i][t]/dt = c^tl_el2EFGtrans[i][t] * k^EFG_diss - c^tl_el3[i][t] * k^tRNA_dissel (i = 1, ..., l^mRNAcodons) tl0(33): dc^AaxtRNA_EFTu[t]/dt = c^AaxtRNA[t] * c^EFTu[t] * k^EFTu_on - c^AaxtRNA_EFTu[t] * k^EFTu_off + \sum \limits_{i=1}^n c^tl_el0AaxtRNA[i][t] * k^AaxtRNAEFTu_off - \sum \limits_{i=1}^n c^tl_el0[i][t] * c^AaxtRNA_EFTu[t] * k^AaxtRNAEFTu_on tl0(34): dc^tl_el1mm[i][t]/dt = c^tl_el1raw[i][t] * prob^tl_mm - c^tl_el1mm[i][t] * k^AaxtRNA_reject (i = 1, ..., l^mRNAcodons) tl0(35): dc^EFTu_GDP[t]/dt = \sum \limits_{i=1}^n c^tl_el0cat[i][t] * k^EFTu_diss + c^EFTu_GDPEFTS[t] * k^EFTS_off - c^EFTu_GDP[t] * c^EFTS[t] * k^EFTS_on tl0(36): dc^EFTu_GDPEFTS[t]/dt = c^EFTu_GDP[t] * c^EFTS[t] * k^EFTS_on - c^EFTu_GDPEFTS[t] * k^EFTS_off + c^EFTu_EFTS[t] * c^GDP[t] * k^GDP_bindTu - c^EFTu_GDPEFTS[t] * k^GDP_dissTu tl0(37): dc^EFTu_EFTS[t]/dt = c^EFTu_GDPEFTS[t] * k^GDP_dissTu - c^EFTu_EFTS[t] * c^GDP[t] * k^GDP_bindTu + c^EFTu_GTPEFTS[t] * k^GTP_offTu - c^EFTu_EFTS[t] * c^GTP[t] * k^GTP_onTu tl0(38): dc^EFTu_GTPEFTS[t]/dt = c^EFTu_EFTS[t] * c^GTP[t] * k^GTP_onTu - c^EFTu_GTPEFTS[t] * k^GTP_offTu + c^EFTu[t] * c^EFTS[t] * k^EFTS_bind - c^EFTu_GTPEFTS[t] * k^EFTS_diss tl0(39): dc^EFTu[t]/dt = c^AaxtRNA_EFTu[t] * k^EFTu_off - c^EFTu[t] * c^AaxtRNA[t] * k^EFTu_on + c^EFTu_GTPEFTS[t] * k^EFTS_diss - c^EFTu[t] * c^EFTS[t] * k^EFTS_bind tl0(40): dc^EFTS[t]/dt = c^EFTu_GDPEFTS[t] * k^EFTS_off - c^EFTu_GDP[t] * c^EFTS[t] * k^EFTS_on + c^EFTu_GTPEFTS[t] * k^EFTS_diss - c^EFTu[t] * c^EFTS[t] * k^EFTS_bind tl0(41): dc^EFG_GDP[t]/dt = \sum \limits_{i=1}^n c^tl_el2EFGtrans[i][t] * k^EFG_diss + c^EFG_raw[t] * c^GDP[t] * k^GDP_bindG - c^EFG_GDP[t] * k^GDP_dissG tl0(42): dc^EFG_raw[t]/dt = c^EFG_GDP[t] * k^GDP_dissG - c^EFG_raw[t] * c^GDP[t] * k^GDP_bindG + c^EFG[t] * k^GTP_offG - c^EFG_raw[t] * c^GTP[t] * k^GTP_onG tl0(43): dc^EFG[t]/dt = c^EFG_raw[t] * c^GTP[t] * k^GTP_onG - c^EFG[t] * k^GTP_offG + \sum \limits_{i=1}^n c^tl_el2_EFG[i][t] * k^EFG_off - \sum \limits_{i=1}^n c^tl_el2[i][t] * c^EFG[t] * k^EFG_on
Termination.
tl0(44): dc^tl_term0[t]/dt = c^tl_el3[-1][t] * k^tRNA_diss + c^tl_term1[t] * k^RF1_off - c^tl_term0[t] * c^RF1[t] * k^RF1_on tl0(45): dc^tl_term1[t]/dt = c^tl_term0[t] * c^RF1[t] * k^RF1_on - c^tl_term1[t] * k^RF1_off + c^tl_term2[t] * k^RF3_off - c^tl_term1[t] * c^RF3[t] * k^RF3_on tl0(46): dc^tl_term2[t]/dt = c^tl_term1[t] * c^RF3[t] * k^RF3_on - c^tl_term2[t] * k^RF3_off - c^tl_term2[t] * k^protein_diss tl0(47): dc^tl_protrel[t]/dt = c^tl_term2[t] * k^protein_diss - c^tl_protrel[t] * k^30s_diss tl0(48): dc^RF1[t]/dt = c^tl_term1[t] * k^RF1_off - c^tl_term0[t] * c^RF1[t] * k^RF1_on + c^tl_protrel * k^30s_diss tl0(49): dc^RF3[t]/dt = c^tl_term2[t] * k^RF3_off - c^tl_term1[t] * c^RF3[t] * k^RF3_on + c^tl_protrel * k^30s_diss tl0(50): dc^protein[t]/dt = c^tl_term2[t] * k^protein_diss
General.
tl0(51): dc^TLATP[t]/dt = - c^AaxtRNASyn_Aaa[t] * c^ATP[t] * k^AaxtRNASyn_cat1 tl0(52): dc^TLGTP[t]/dt = c^IF2[t] * c^GDP[t] * k^IF2_deg - c^IF2_GDP[t] * c^GTP[t] * k^IF2_reg + c^EFTu_GTPEFTS[t] * k^GTP_offTu - c^EFTu_EFTS[t] * c^GTP[t] * k^GTP_onTu + c^EFG[t] * k^GTP_offG - c^EFG_raw[t] * c^GTP[t] * k^GTP_onG tl0(53): dc^TLGDP[t]/dt = c^IF2_GDP[t] * c^GTP[t] * k^IF2_reg - c^IF2[t] * c^GDP[t] * k^IF2_deg + c^EFTu_GDPEFTS[t] * k^GDP_dissTu - c^EFTu_EFTS[t] * c^GDP[t] * k^GDP_bindTu + c^EFG_GDP[t] * k^GDP_dissG - c^EFG_raw[t] * c^GDP[t] * k^GDP_bindG tl0(54): dc^Aax[t]/dt = c^AaxtRNASyn_Aax[t] * k^Aax_off - c^AaxtRNASyn[t] * c^Aax[t] * k^Aax_on tl0(55): dc^tRNA[t]/dt = c^AaxtRNASyn_AaatRNA[t] * k^tRNA_off - c^AaxtRNASyn_Aaa[t] * c^tRNA[t] * k^tRNA_on + \sum \limits_{i=1}^n c^tl_el3[i][t] * k^tRNA_dissel tl0(56): dc^TLmRNA[t]/dt = c^tl_ini0[t] * k^mRNA_off - c^30sIF3IF1[t] * c^mRNA[t] * k^mRNA_on + c^tl_protrel * k^30s_diss tl0(57): dc^50s[t]/dt = c^70sIF3[t] * k^50s_diss - c^30s[t] * c^50s[t] * k^50s_bind + c^tl_inion50s[t] * k^50s_off - c^tl_inidissIF3[t] * c^50s[t] * k^50s_on + c^tl_protrel * k^30s_diss tl0(58): dc^30s[t]/dt = c^30sIF3[t] * k^IF3_off - c^30s[t] * c^IF3[t] * k^IF3_on - c^30s[t] * c^50s[t] * k^50s_bind + c^tl_protrel * k^30s_diss