MicroRNAs and Robustness in Biological Regulatory Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic, and Genetic - Systems Biology of Metabolic and Signaling Networks - page 60

involvement of microRNAs in the ubiquitous p53 regulatory function of cell cycle
control, then their global role in cell respiration homeostasis, in carcinogenesis, and
finally we discuss the influence of microRNAs on the increase of robustness of
genetic networks during evolution.

4.1 Introduction

The whole body physiology as well as cell metabolism are regulated by interaction
networks that bring together as elementary nodes, cells or macromolecules like
genes and their expression products, proteins, or other metabolites. This complex
organization is made out of numerous weak interactions due to physicochemical
forces like electrostatic or van der Waals forces, or to electrical and/or mechanical
forces. The aim of this chapter is to show how mathematical theories like graph
theory, discrete network theory, and dynamical systems theory are necessary to
give a mechanistic description of how a cell works, a tissue or an organ grows,
from the emergent properties of their constituents interacting at different levels of
complexity. The corresponding regulatory networks made of elements (e.g., genes,
proteins, cells, ) in interaction control important tissue functions like proliferation

and differentiation and cellular functions like respiration or glycolysis. The
dynamics of these networks depends highly on the relationships and delays between
the kinetics of creation and/or transformation of their elements and then they need
to be described in the framework of Systems Biology.
A system is a set of elements in interaction and the cell (resp. tissue) organiza-
tion is a biological system, considered as a pyramid of components made of
interacting macromolecules (resp. cells). Their observed spatio-temporal behavior
(phenotype) can be explained through several loops of complexity from data
acquisition to reconstruction of regulatory interaction networks (inverse problem)
at different levels, allowing direct predictions by modeling and simulating them in
silico. This complexity deals with kinetic rules (Henri-Michaelis-Menten, Hill,
Monod-Wyman-Changeux, Thomas, ... ) prescribing how macromolecules, cells,
and tissues are connected into integrated regulatory networks with architectural
similarities both inside the cell and at tissue level. The dynamics allowed by these
rules and the corresponding discrete or differential equations allow simulating
trajectories to be compared with the temporal evolutions observed in experiments.
These trajectories can be stable in different mathematical senses that we present in
Sect. 4.2 ; a system being stable in all senses will be declared robust. In Sect. 4.3 ,we
consider simple examples such as robust and non-robust systems. In Sect. 4.4 ,
we examine the consequences of the role of microRNAs in the energetic system of
the cell, at the physiologic vegetative level. In Sect. 4.5 , we study the global role
of microRNAs at the level of mitochondrial or chloroplast respiration, in the
genesis of cancer and in the control of the defenses against infectious agents. In
Sect. 4.6 , we present a brief perspective about the role of microRNAs in maintaining
robustness in genetic networks.

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