Biology Reference
In-Depth Information
8
CHAPTER
Computational Methods for
Strain Design
Sang Yup Lee
1,2,3
, Seung Bum Sohn
1,2
, Yu Bin Kim
1,2
, Jae Ho Shin
1
, Jin Eyun Kim
1
and Tae Yong Kim
2
1
Institute for the BioCentury, KAIST, Daejeon, Republic of Korea
2
Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
3
Department of Bio and Brain Engineering and Bioinformatics Research Center, KAIST,
Daejeon, Republic of Korea
INTRODUCTION
To fully harness the synthetic capacity of biological systems, it is necessary to go beyond
natural genetic circuits and pathways for the design of engineered cells.
1
5
Early methods
were designed to assemble simple genetic components into the biological network such that
the cells would perform a distinct function atypical from the wild-type phenotype.
3
5
With
the development towards the construction of large-scale systems with complex functions,
integration of complex synthetic biology components into the cell is critical. However,
despite the increase in knowledge of the functions of the cellular components within these
systems, there is still much that remains to be illuminated, because of the complex nature
of cells, resulting in uncertainties and partial description of the biological network that the
synthetic version was designed to represent. To achieve such a comprehensive integration
of synthetic biology components in a large-scale system, computational methods would
be essential to represent the dynamics that the various components have with each other.
6,7
141
Computational approaches have contributed greatly in consolidating and enhancing the
predictive capabilities of synthetic biology for characterizing biological systems and their
components. The use of computational methods allows for the computational modeling
and simulation of biological systems for better understanding of the complexities within
these systems. The results of computational prediction of cellular behaviors are then
validated against actual observations of the system and discrepancies between the model
and the observations result in the refining of the model to account for the differences.
This process of simulation and validation is repeated iteratively until the model is able to
accurately represent the system of interest. With the availability of computational methods,
the analysis of data from observations allows for a more rapid and acceptable approach in
designing synthetic biological networks for different environmental and/or genetic
perturbations.
2,7,8
An important application of synthetic biology is the development of strategies to
engineering microorganisms for the purpose of producing high-valued compounds.
Metabolic engineering has been the main tool for altering the metabolic network to direct
