Computing MDR-Game with Applications to Real Data

Abstract

Originally developed for probability and statistic purposes, Multiple Dice Rolling (MDR) game turns out to be useful for many other applications such as complex biological systems and medical research. It has been mainly used to estimate system states that cannot be observed directly. MDR game is affected by noise and fluctuations. It is widely agreed that noise in dynamic systems is captured by using the available computational and experimental technics, which provide information on the dynamics, size and entropy. Recently, computational advances have used algorithmic strategies and focused on stochastic accuracy to predict noise level in a given systems. It is known that experimental techniques can fully characterized the noise pattern and in some cases predicted relatively good outcomes for the MDR game. In this research, we developed a stochastic theory based on the MDR game and applied real data from to the Ciona and Beer-Tavazoie datasets. We found that the Ciona data (cell biometry) was relatively stable and the Beer-Tavazoie data (gene expression) was noisy. These facts support the well-known biologists’ theories that “cells are stable, but genes which are components of cells are unstable”. Furthermore, we have, for the first time, demonstrated quantitatively the molecular biology dogma. This result can be further developed in a novel direction to further understand the disease prediction, control and aging monitoring at the molecular and genetic levels.