13th International Conference on Membrane Computing - MTA Sztaki

<strong>13th</strong> <strong>International</strong> <strong>Conference</strong> on Membrane Computing, CMC13,
Budapest, Hungary, August 28 - 31, 2012. Proceedings, pages 69 - 85.
A Case-study on the Inuence of Noise to
Log-Gain Principles for Flux Dynamic Discovery
Tanvir Ahmed 1 , Garrett DeLancy 1 and Andrei Paun 1,2
1: Department of Computer Science, Louisiana Tech University, Ruston
PO Box 10348, Louisiana, LA-71272 USA {tah025, rgd006, apaun}@latech.edu
2: Bioinformatics Department, National Institute of Research and Development for
Biological Sciences, Splaiul Independenµei, Nr. 296, Sector 6, Bucharest, Romania
apaun@fmi.unibuc.ro
Abstract. In this paper we show problems associated with the log-gain
procedure [20] for determining ux-dynamics from time series by means
of applying noise to the data sets. We illustrate this by rst creating a
set of ux functions and using these ux functions to derive a time series
which we then apply Gaussian noise to [5], [6] This noisy time series
is then used in the log-gain procedure to determine ux-dynamics. The
error from the two sets of ux functions is found to be extremely large
for signal-to-noise ratios of less than about 25. To further illustrate the
disparity in the results, we use these derived ux functions to discover
new time series. We show that the log-gain procedure is very susceptible
to noise, and that for it to be of practical use with data collect in vivo
it must be made much more robust.
1 Introduction
One of many goals in the eld of bioinformatics is the creation of tools [1]
used to interpret raw biological data and generate models based on this data.
A problem that may be encountered is that the amount of data these accurate
models require sometimes far exceeds the amount of data we can feasibly collect
from the biological source. One method that some have used in an attempt
to bypass this problem is the extrapolation of predicted data from the gathered
data. Care must be taken in this process as it becomes increasingly easy to create
an accurate model that can only make predictions based on extrapolated data,
perfect data gathered under ideal conditions, or data that contains no noise. Such
a model obviously has little use, and as our techniques for gathering biological
data evolve and become more ecient and rened, we will nd ourselves needing
to create ller data less and less often.
The word noise is commonly used to refer to unwanted sound; however, in
many scientic areas the term is extended to cover any unwanted signal or data
that interferes with the collection of wanted data. This is very much like sound
noise interfering with one's ability to listen to a conversation, and as such the
term ts. The vast majority of data gathered from scientic experiments in
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