Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background: One of the most ll described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent  can dissect the relationship beten model parameters and system dynamics. Here  combine two strategies to enable an exploration of parameter space in relation to model output. A simplified, piecewise linear approximation of the original model is combined with a sensitivity analysis of the same system, to obtain and validate analytical expressions describing the dynamical role of different model parameters. Results:  considered two different output responses to parameter perturbations. One was qualitative and described whether the system was still working, i.e. whether there re oscillations.  call parameters that correspond to such qualitative change in system response essential. The other response pattern was quantitative and measured changes in cell size, corresponding to perturbations of modulatory parameters. Analytical predictions from the simplified model concerning the impact of different parameters re compared to a sensitivity analysis of the original model, thus evaluating the predictions from the simplified model. The comparison shod that the predictions on essential and modulatory parameters re satisfactory for small perturbations, but more discrepancies re seen for larger perturbations. Furthermore, for this particular cell cycle model,  found that most parameters re either essential or modulatory. Essential parameters required large perturbations for identification, whereas modulatory parameters re more easily identified with small perturbations. Finally,  used the simplified model to make predictions on critical combinations of parameter perturbations. Conclusions: The parameter characterizations of the simplified model are in large consistent with the original model and the simplified model can give predictions on critical combinations of parameter perturbations.  believe that the distinction beten essential and modulatory perturbation responses will be of use for sensitivity analysis, and in discussions of robustness and during the model simplification process.
['Olivia Eriksson', 'Tom Andersson', 'Yishao Zhou', 'Jesper Tegnér']
I am writing a research paper on a idea Decoding complex biological networks - tracing essential and modulatory parameters in complex and simplified models of the cell cycle
[{"from": "human", "value": "I am writing a research paper on a idea Decoding complex biological networks - tracing essential and modulatory parameters in complex and simplified models of the cell cycle"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background: One of the most ll described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent  can dissect the relationship beten model parameters and system dynamics. Here  combine two strategies to enable an exploration of parameter space in relation to model output. A simplified, piecewise linear approximation of the original model is combined with a sensitivity analysis of the same system, to obtain and validate analytical expressions describing the dynamical role of different model parameters. Results:  considered two different output responses to parameter perturbations. One was qualitative and described whether the system was still working, i.e. whether there re oscillations.  call parameters that correspond to such qualitative change in system response essential. The other response pattern was quantitative and measured changes in cell size, corresponding to perturbations of modulatory parameters. Analytical predictions from the simplified model concerning the impact of different parameters re compared to a sensitivity analysis of the original model, thus evaluating the predictions from the simplified model. The comparison shod that the predictions on essential and modulatory parameters re satisfactory for small perturbations, but more discrepancies re seen for larger perturbations. Furthermore, for this particular cell cycle model,  found that most parameters re either essential or modulatory. Essential parameters required large perturbations for identification, whereas modulatory parameters re more easily identified with small perturbations. Finally,  used the simplified model to make predictions on critical combinations of parameter perturbations. Conclusions: The parameter characterizations of the simplified model are in large consistent with the original model and the simplified model can give predictions on critical combinations of parameter perturbations.  believe that the distinction beten essential and modulatory perturbation responses will be of use for sensitivity analysis, and in discussions of robustness and during the model simplification process."}]