13th International Conference on Membrane Computing - MTA Sztaki

A case-study on the influence of noise to log-gain principles for flux dynamic
discovery
Using the log-gain theory requires that we know the initial values of all uxes
and at least 1,000 steps of each time-series. [23] With data collected from the
noisy time series we constructed, we are easily able to supply this information.
For our study, we used both the initial values of the uxes and 10,000 steps of
our noisy time series data for the log-gain MetaPlab plugin.
Our next step was to take the noisy time series data and import it into
a model which contained no ux functions. The above model has had it's ux
functions removed and the relevant data replaced with our noisy time series data.
Such a model is what would typically be used with the log-gain theory, as the
log-gain process should generate ux-dynamics by utilizing the time series data
gathered from experimentation and without making use of known ux functions.
This model is a prime candidate for being used with the MetaPlab log-gain
plugin. With noiseless data this process would be quickly veried; however, for
our noisy data additional tests will be required. After importing all noisy data
and the initial values for the ux functions, we were ready to begin the log-gain
process.
We made use of the following set of tuners in using our model with the loggain
process. Tuner selection was a simple task as we knew the ux functions
before hand (recall that we simply assumed some ux functions). As such, tuner
selection was a simple process of determining which reactions were governed by
which substances.
R 0 = A, B, C
R 1 = B,C
R 2 = C, A
R 3 = B
R 4 = C
As mentioned above, we made use of the MetaPlab log-gain plugin to apply
log-gain theory to our model. This plugin requires the initial values of all uxes
and at least 1,000 steps of each time-series. The time series data used is the
10,000 step noisy data we generated. The reaction set was input into the plugin
with a good Covering Oset Log Gain Property [10], and a polynomial was then
generated through the plugin by selecting an appropriate substance. Descriptions
for the polynomials were given at the time of generation. The reactions were set
up in the following manner: R0 covered substance A, R3 covered substance B,
and R2covered substance C. After all tuners and coverings were input into
MetaPlab, we ran the plugin. We also used a linear regression plugin to apply
curve tting to the clean noise eect and retrieve the ux functions. This only
required that we make use of the ux functions and the initial values of the
various time series. The time series can either be imported from external les by
the log-gain plugin or computed by using the dynamic computation plugin.
The log-gain plugin applied log-gain theory to the noisy time series data, but
to better understand our results, we took one last step. We noticed that our
75