13th International Conference on Membrane Computing - MTA Sztaki

T. Ahmed, G. DeLancy, A. Paun
almost all of the sciences contains at least a small amount of noise; biology is no
exception. Noise is actually extremely prevalent in data gathered from the area
of molecular biology; many of the tools we use to gather in vivo biological data
are capable of (and often do) also inadvertently gather data produced by other
biological functions. Because of this, overcoming the noise problem is one of the
many challenges in that eld.
P systems [13], [11] are computational models that preform computations
based upon an abstraction in the way that chemicals interact with and move
across various cellular membranes. [11], [12], [13], [21] Metabolic P systems (MP
systems) can be dened as deterministic P systems proposed to compute the
dynamics of various biological processes (like metabolism) in cells.
Key to our research are two very important tools: Matlab and MetaPlab.
For the uninformed, Matlab is a programming environment with a wide range of
uses including but not limited to algorithm development and data analysis. For
the purposes of of our research we made use of Matlab to apply generated noise
to noiseless data. MetaPlab is a modeling tool used by researchers to better
understand and predict the internal mechanisms of biological systems [8], [9]
and their responses to external stimuli, environmental conditions, and structural
changes. The MetaPlab framework is based on a core module which enables the
design and management of biological models and an extensive set of plugins.
[23] MetaPlab is used extensively in our research for both model creation and
data collection. Without MetaPlab much of the work done here would have been
much more dicult.
The log-gain principle states that we can compute future behavior of a given
system within an acceptable approximation. Furthermore, it is believed that in
some situations we can determine an MP system that is a good model of a few
discovered metabolic dynamics. Using current methods it is dicult to measure a
reaction's uxes. This is due to the microscopic nature of chemical elements, the
complex interactions among these elements and the sheer number of elements.
Log-gain principles allow us to calculate the approximate values of uxes if we
know the time series of the system. With an appropriate model, this would allow
us to quickly and easily retrieve the reaction uxes for every instant. The process
is believed to be such that a ratio should hold between change of substances and
related change of ux units. [20]
Here we hope to show the eects that noise may have on MP systems and
explore the underlying mechanisms of ux-dynamics discovery in log-gain principles.
As such, the addition of noise to the data used in the log-gain procedure
will act as a sort of stress test to determine how well the whole process can handle
noisy data. The importance of such an expiriment is almost self-relevant. By
showing how well the log-gain procedure can handle noise we will hopefully be
able to determine how well the procedure could handle data gathered completely
from an in vivo source.
70