1

Chapter 1

Introduction

approaches have been used for solving biological systems since

on time. Most of the earlier effort has reduced a system to the

levels for the study. Such an effort is referred to as the

ist [Mazzochhi, 2008]. Having recognised that a biological

one of the most complicated systems on the earth, whether a

l system can be easily simplified for a meaningful research has

bated for a few decades [Mazzochhi, 2008]. Moreover,

sing a biological system, such as a single organism or a single

nrelated subparts to reduce the component number for the study

greatly challenged nowadays [Glaeser, 1994; Wood, et al., 2004;

and Leggewie, 2015; Nussinov, 2015]. The challenges not only

m the aspects before an analysis such as experiment condition,

tion and storage, but also the methods used in analysis including

putational approaches, the computing facility as well as the

ation and explanation after an analysis.

biological pattern discovery approaches based on the

ist principle have been criticised as “meaningless” due to four

ng issues [Mazzochhi, 2008]. They include the problem of

nt interaction, the problem of debatable extrapolation based on

all in-house mathematical models, the problem of deterministic

al thinking and the problem of insufficient treatment in an

nt or a modelling process regarding the self-organisation within

cal system. New trends of biological pattern analysis have been

o employ more sophisticated methods to understand, model and

biological systems. They have provided new conceptual ideas

hodologies for researching complicated biological systems