Systems biology intends to portray as well as comprehend biology around
the globe, where biological processes are acknowledged as the outcome of complex
mechanisms which occur on multiple dimensions beginning with the molecular level
and reaching to ecosystem level. Biological information in systems biology comes
from overlying but distinct scientific areas, each with its own style of expressing the
events under research. Simulation and modeling are computer-aided methods that are
precious for the quantitative and integrative description, prediction, and exploration of
these mechanisms. In addition, Multi-level and hybrid models have been developed to
meet both improved accuracy and capability of making good knowledge bases, which
turned out to be a valuable tool in computational systems biology. Various methods,
including the silicon model, have been developed in many scientific disciplines for
solving multi-scale problems, which is appropriate to continuum-based modeling
strategies. The association between system properties is depicted using continuous
mathematical equations in which heterogeneous microscopic elements, such as persons,
are modelled using individual units. We summarized multi-scale methodologies and
their application in biotechnology and drug development applications in view of
emphasizing the importance of studying systems as a whole with the role of artificial
intelligence and biostatistical aspects in this review.
Keywords: Artificial intelligence, Biostatistical aspects, Multi-scale modeling, System biology, Silicon model.
