In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is
a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental
unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any
network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights
some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and
analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper
images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by
an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks
have been developed as a platform for integrating information from high to low-throughput experiments for the
analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks
in such a way that they can become easily understandable for researchers with both biological and mathematical
backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise
that researchers have turned to computer simulation and the development of more theory-based approaches to augment
and assist in the development of a fully quantitative understanding of cellular dynamics.
Systems biology, Modeling algorithms, Genome-scale modeling, Biological network.
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.