NDLI: Evolving Genetic Networks for Synthetic Biology

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Evolving Genetic Networks for Synthetic Biology

Content Provider	Springer Nature Link
Author	man, Nasimul Palafox, Leon Iba, Hitoshi
Copyright Year	2013
Abstract	The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to engineer artificial gene networks with predicted functions. The significant and important role that computational intelligence can play to steer the life engineering discipline towards its ultimate goal, has been acknowledged since its time of birth. However, as the field is facing many challenges in building complex modules/systems from the simpler parts/devices, whether from scratch or through redesign, the role of computational assistance becomes even more crucial. Evolutionary computation, falling under the broader domain of artificial intelligence, is well-acknowledged for its near optimal solution seeking capability for poorly known and partially understood problems. Since the post genome period, these natural-selection simulating algorithms are playing a noteworthy role in identifying, analyzing and optimizing different types of biological networks. This article calls attention to how evolutionary computation can help synthetic biologists in assembling larger network systems from the lego-like parts.
Starting Page	71
Ending Page	88
Page Count	18
File Format	PDF
ISSN	02883635
Journal	New Generation Computing
Volume Number	31
Issue Number	2
e-ISSN	18827055
Language	English
Publisher	Verlag Omsha Tokio
Publisher Date	2013-04-28
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetic Network Evolutionary Algorithms Synthetic Biology Systems Biology In Silico Evolution Artificial Intelligence (incl. Robotics) Computer Hardware Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems Computing Methodologies
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Networks and Communications Software Hardware and Architecture

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