115,237 research outputs found
Natural Computing and Beyond
This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics
Natural Computing and Beyond
This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics
Evolution of associative learning in chemical networks
Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells
Ions in Fluctuating Channels: Transistors Alive
Ion channels are proteins with a hole down the middle embedded in cell
membranes. Membranes form insulating structures and the channels through them
allow and control the movement of charged particles, spherical ions, mostly
Na+, K+, Ca++, and Cl-. Membranes contain hundreds or thousands of types of
channels, fluctuating between open conducting, and closed insulating states.
Channels control an enormous range of biological function by opening and
closing in response to specific stimuli using mechanisms that are not yet
understood in physical language. Open channels conduct current of charged
particles following laws of Brownian movement of charged spheres rather like
the laws of electrodiffusion of quasi-particles in semiconductors. Open
channels select between similar ions using a combination of electrostatic and
'crowded charge' (Lennard-Jones) forces. The specific location of atoms and the
exact atomic structure of the channel protein seems much less important than
certain properties of the structure, namely the volume accessible to ions and
the effective density of fixed and polarization charge. There is no sign of
other chemical effects like delocalization of electron orbitals between ions
and the channel protein. Channels play a role in biology as important as
transistors in computers, and they use rather similar physics to perform part
of that role. Understanding their fluctuations awaits physical insight into the
source of the variance and mathematical analysis of the coupling of the
fluctuations to the other components and forces of the system.Comment: Revised version of earlier submission, as invited, refereed, and
published by journa
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