Computational Genetic Regulatory Networks Evolvable Self Organizing Systems
Download Computational Genetic Regulatory Networks Evolvable Self Organizing Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Computational Genetic Regulatory Networks Evolvable Self Organizing Systems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems
Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting from a single cell interacting with its environment, eventually including a changing local neighbourhood of other cells. These methods may help us understand the genesis, organization, adaptive plasticity, and evolvability of differentiated biological systems, and may also provide a paradigm for transferring these principles of biology's success to computational and engineering challenges at a scale not previously conceivable.
Morphogenetic Engineering
Generally, spontaneous pattern formation phenomena are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural. This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of “self-architecturing” systems. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science—while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. Altogether, the aim of this work is to provide a framework for and examples of a larger class of “self-architecturing” systems, while addressing fundamental questions such as br” How do biological organisms carry out morphogenetic tasks so reliably? br” Can we extrapolate their self-formation capabilities to engineered systems?br” Can physical systems be endowed with information (or informational systems be embedded in physics) so as to create autonomous morphologies and functions?br” What are the core principles and best practices for the design and engineering of such morphogenetic systems?
Transactions on Computational Systems Biology X
Technology is taking us to a world where myriads of heavily networked devices interact with the physical world in multiple ways, and at many levels, from the globalInternetdowntomicroandnanodevices. Manyofthesedevicesarehighly mobile and autonomous and must adapt to the surrounding environment in a totally unsupervised way. A fundamental research challenge is the design of robust decentralized c- puting systemsthat arecapableofoperating in changing environmentsandwith noisy input, and yet exhibit the desired behavior and response time, under c- straints such as energy consumption, size, and processing power. These systems should be able to adapt and learn how to react to unforeseen scenarios as well as to display properties comparable to social entities. The observation of nature has brought us many great and unforeseen concepts. Biological systems are able to handle many of these challenges with an elegance and e?ciency far beyond currenthumanartifacts. Basedonthisobservation,bio-inspiredapproacheshave been proposed as a means of handling the complexity of such systems. The goal is to obtain methods to engineer technical systems, which are of a stability and e?ciency comparable to those found in biological entities. This Special Issue on Biological and Biologically-inspired Communication contains the best papers from the Second International Conference on Bio- Inspired Models of Network, Information, and Computing Systems (BIONET- ICS 2007). The BIONETICS conference aims to bring together researchers and scientistsfromseveraldisciplines incomputerscienceandengineeringwhereb- inspired methods are investigated, as well as from bioinformatics, to deepen the information exchange and collaboration among the di?erent communities.