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13th International Conference on Membrane Computing - MTA Sztaki

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T. Ahmed, G. DeLancy, A. Paun<br />

almost all of the sciences c<strong>on</strong>tains at least a small amount of noise; biology is no<br />

excepti<strong>on</strong>. Noise is actually extremely prevalent in data gathered from the area<br />

of molecular biology; many of the tools we use to gather in vivo biological data<br />

are capable of (and often do) also inadvertently gather data produced by other<br />

biological functi<strong>on</strong>s. Because of this, overcoming the noise problem is <strong>on</strong>e of the<br />

many challenges in that eld.<br />

P systems [13], [11] are computati<strong>on</strong>al models that preform computati<strong>on</strong>s<br />

based up<strong>on</strong> an abstracti<strong>on</strong> in the way that chemicals interact with and move<br />

across various cellular membranes. [11], [12], [13], [21] Metabolic P systems (MP<br />

systems) can be dened as deterministic P systems proposed to compute the<br />

dynamics of various biological processes (like metabolism) in cells.<br />

Key to our research are two very important tools: Matlab and MetaPlab.<br />

For the uninformed, Matlab is a programming envir<strong>on</strong>ment with a wide range of<br />

uses including but not limited to algorithm development and data analysis. For<br />

the purposes of of our research we made use of Matlab to apply generated noise<br />

to noiseless data. MetaPlab is a modeling tool used by researchers to better<br />

understand and predict the internal mechanisms of biological systems [8], [9]<br />

and their resp<strong>on</strong>ses to external stimuli, envir<strong>on</strong>mental c<strong>on</strong>diti<strong>on</strong>s, and structural<br />

changes. The MetaPlab framework is based <strong>on</strong> a core module which enables the<br />

design and management of biological models and an extensive set of plugins.<br />

[23] MetaPlab is used extensively in our research for both model creati<strong>on</strong> and<br />

data collecti<strong>on</strong>. Without MetaPlab much of the work d<strong>on</strong>e here would have been<br />

much more dicult.<br />

The log-gain principle states that we can compute future behavior of a given<br />

system within an acceptable approximati<strong>on</strong>. Furthermore, it is believed that in<br />

some situati<strong>on</strong>s we can determine an MP system that is a good model of a few<br />

discovered metabolic dynamics. Using current methods it is dicult to measure a<br />

reacti<strong>on</strong>'s uxes. This is due to the microscopic nature of chemical elements, the<br />

complex interacti<strong>on</strong>s am<strong>on</strong>g these elements and the sheer number of elements.<br />

Log-gain principles allow us to calculate the approximate values of uxes if we<br />

know the time series of the system. With an appropriate model, this would allow<br />

us to quickly and easily retrieve the reacti<strong>on</strong> uxes for every instant. The process<br />

is believed to be such that a ratio should hold between change of substances and<br />

related change of ux units. [20]<br />

Here we hope to show the eects that noise may have <strong>on</strong> MP systems and<br />

explore the underlying mechanisms of ux-dynamics discovery in log-gain principles.<br />

As such, the additi<strong>on</strong> of noise to the data used in the log-gain procedure<br />

will act as a sort of stress test to determine how well the whole process can handle<br />

noisy data. The importance of such an expiriment is almost self-relevant. By<br />

showing how well the log-gain procedure can handle noise we will hopefully be<br />

able to determine how well the procedure could handle data gathered completely<br />

from an in vivo source.<br />

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