TITLE:
Novel Quantitative Approach for Predicting mRNA/Protein Counts in Living Cells
AUTHORS:
Henri C. Jimbo, Seraphin I. Ngongo, Achille Mbassi, Nicolas G. Andjiga
KEYWORDS:
Applied Mathematics, Embedded Control System, Genetic Algorithm, Optimization, Biodynamics, Stochastic Modelling, Simulations, Stochasticity, Bioengineering and Medicine
JOURNAL NAME:
Applied Mathematics,
Vol.8 No.8,
August
21,
2017
ABSTRACT: One of the most complex questions in quantitative biology is how to manage
noise sources and the subsequent consequences for cell functions. Noise in
genetic networks is inevitable, as chemical reactions are probabilistic and often,
genes, mRNAs and proteins are present in variable numbers per cell. Previous
research has focused on counting these numbers using experimental
methods such as complex fluorescent techniques or theoretical methods by
characterizing the probability distribution of mRNAs and proteins numbers
in cells. In this work, we propose a modeling based approach; we build a mathematical
model that is used to predict the number of mRNAs and proteins
over time, and develop a computational method to extract the noise-related
information in such a biological system. Our approach contributes to answering
the question of how the number of mRNA and proteins change in living
cells over time and how these changes induce noise. Moreover, we calculate
the entropy of the system; this turns out to be important information for
prediction which could allow us to understand how noise information is generated
and expanded.