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As in any model-based fitting procedure, more data usually means more accurate and precise flux estimates. The minimal number of
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measurements depend on the model used for flux calculation. For instance, for steady-state built-in models provided with PhysioFit, we recommend using
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at least 6 to 8 time points, which should provide reliable and meaningful estimates in most situations.
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As in any model-based fitting procedure, more data usually means more
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accurate and precise flux estimates. The minimal number of measurements
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depend on the model used for flux calculation. For instance, for
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steady-state built-in models provided with PhysioFit, we recommend using at
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least 6 to 8 time points, which should provide reliable and meaningful
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estimates in most situations.
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Still, the exact answer to this question strongly depends on the uptake/production/growth rates of your (micro)organism
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in the conditions you are investigating, on the sampling time interval, on the questions you are addressing, on the model used for flux calculation, and on
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many other parameters! You can make some tests by calculating fluxes from (published or theoretical) datasets similar
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to those you have in mind.
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Still, the exact answer to this question strongly depends on the
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uptake/production/growth rates of your (micro)organism in the conditions you
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are investigating, on the sampling time interval, on the questions you are
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addressing, on the model used for flux calculation, and on many other
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parameters! You can make some tests by calculating fluxes from (published or
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theoretical) datasets similar to those you have in mind.
A χ² test describes how well a model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model used in PhysioFit (see the :doc:`method` section). It is calculated as the sum of differences between measured and simulated values, each squared and divided by the simulated value.
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A good fit corresponds to small differences between measured and simulated values, thereby the χ² value is low. In contrast, a bad fit corresponds to large differences between simulations and measurements, and the χ² value is high.
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The quality of the fit can be evaluated based on:
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* the plots of experimental vs simulated data for the best fit, which should be as close as possible,
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* the χ² statistical test results given in the stat ouput file (see below
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for help on interpreting the results).
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.. seealso:: :ref:`chi2 test` and :ref:`bad fit`
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The resulting χ² value can then be compared with a χ² distribution to determine the goodness of fit. The p-value of one-tail χ² test is calculated by PhysioFit from the best fit and is given in the log file (have a look to the :doc:`usage` section). A p-value close to 0 means poor fitting, and a p-value close to 1 means good fitting (keeping in mind that a p-value very close to 1 can be an evidence that standard deviations might be overestimated). A
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p-value between 0.95 and 1 means the model fits the data good enough with respect to the standard deviations provided (at a 95% confidence level). PhysioFit provides an explicit meassage stating wether the flux data are satisfactorily fitted or not (at a 95% confidence interval).
A possible reason to explain a bad fit is that standard deviations on measurements (concentration biomass and metabolites) is under-estimated, thereby making the χ² test too stringent. In this case, plots of measured and fitted data should be in agreement. Reliable estimated of standard deviation on measurements must be provided to PhysioFit (have a look to the :doc:`usage` section to see how to check and adjust this parameter).
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Another possible reason to explain a bad fit is that a key asumption of the flux calculation method is not respected. For instance,
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if you use a steady-state model shipped with PhysioFit, cells might not be strictly in metabolic steady-state, i.e. with
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constant fluxes during the whole experiment. If this key asumption does not occur (e.g. cells are continuously adapting
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to their environment and fluxes change over time), PhysioFit will not be able to fit the data satisfactorily. In this case,
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evaluate wether the deviation is significant or not (e.g. based on the detailed χ² statistics or on the plot of fitted vs
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measured data), and evaluate the potential biases that would be introduced by interpreting (or not) these flux values.
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In rare situations, it may also be because some parameters have to be tweaked to
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help PhysioFit fitting the measurements, which results in obviously aberrant fits (e.g. with flat
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time-course profiles for all metabolites). This might happen for instance if some measurements are
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provided in units far from unity (e.g. 1.10\ :sup:`-5` M instead of 10 µM). If this situation happens, we suggest modifying the initial values of fluxes, or changing the units of input data, and re-run the flux calculation. For more info on the run parameters and how they may affect the fitting process,
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A possible reason to explain a bad fit is that standard deviations on
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measurements (concentration biomass and metabolites) is under-estimated,
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thereby making the χ² test too stringent. In this case, plots of measured and
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fitted data should be in agreement. Reliable estimated of standard deviation
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on measurements must be provided to PhysioFit (have a look to the :doc:`usage`
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section to see how to check and adjust this parameter).
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Another possible reason to explain a bad fit is that a key asumption of the
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flux calculation method is not respected. For instance, if you use a
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steady-state model shipped with PhysioFit, cells might not be strictly in
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metabolic steady-state, i.e. with constant fluxes during the whole
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experiment. If this key asumption does not occur (e.g. cells are continuously
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adapting to their environment and fluxes change over time), PhysioFit will
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not be able to fit the data satisfactorily. In this case, evaluate wether
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the deviation is significant or not (e.g. based on the detailed χ²
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statistics or on the plot of fitted vs measured data), and evaluate the
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potential biases that would be introduced by interpreting (or not) these
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flux values.
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In rare situations, it may also be because some parameters have to be
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tweaked to help PhysioFit fit the measurements, which results in
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obviously aberrant fits (e.g. with flat time-course profiles for all
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metabolites). This might happen for instance if some measurements are
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provided in units far from unity (e.g. 1.10\ :sup:`-5` M instead of 10 µM). If
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this situation happens, we suggest modifying the initial values of fluxes,
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or changing the units of input data, and re-run the flux calculation. For
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more info on the run parameters and how they may affect the fitting process,
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please refer to section :ref:`physiofit parameters`.
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If you believe the problem is in PhysioFit, we would greatly appreciate
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if you could open a new issue on our `issue tracker <https://github.com/MetaSys-LISBP/PhysioFit/issues>`_.
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If you think the problem is in PhysioFit, we would greatly appreciate
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if you could open a new issue on our `issue tracker <https://github
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.com/MetaSys-LISBP/PhysioFit/issues>`_.
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I cannot start PhysioFit graphical user interface, can you help me?
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