Opposing Pressures of Speed and Efficiency Guide the Evolution of Molecular Machines.

Many biomolecular machines need to be both fast and efficient. How has evolution optimized these machines along the tradeoff between speed and efficiency? We explore this question using optimizable dynamical models along coordinates that are plausible evolutionary degrees of freedom. Data on 11 motors and ion pumps are consistent with the hypothesis that evolution seeks an optimal balance of speed and efficiency, where any further small increase in one of these quantities would come at great expense to the other. For FoF1-ATPases in different species, we also find apparent optimization of the number of subunits in the c-ring, which determines the number of protons pumped per ATP synthesized. Interestingly, these ATPases appear to more optimized for efficiency than for speed, which can be rationalized through their key role as energy transducers in biology. The present modeling shows how the dynamical performance properties of biomolecular motors and pumps may have evolved to suit their corresponding biological actions

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

Rustless translation

ATP binding cassette proteins are a large and diverse family of molecular machines and include transmembrane transporter, chromosome maintenance and DNA repair proteins, and translation factors. However, the function of the ABCE1, the only member of subfamily E of ABC proteins, remained mysterious for over a decade, even though it is perhaps the most conserved ABC protein in eukaryotes and archaea. Recent results have now identified ABCE1 as the ribosome-recycling factor of eukaryotes and archaea. Thus, two iron-sulfur clusters - the hallmark feature of ABCE1 - help catalyze an integral step of the translational cycle at the core of the protein synthesis machinery

'Playing robot': an interactive sound installation in human-robot interaction design for new media art

In this study artistic human-robot interaction design is in- troduced as a means for scientific research and artistic inves- tigations. It serves as a methodology for situated cognition integrating empirical methodology and computational mod- eling, and is exemplified by the installation playing robot. Its artistic purpose is to aid to create and explore robots as a new medium for art and entertainment. We discuss the use of finite state machines to organize robots’ behavioral reac- tions to sensor data, and give a brief outlook on structured observation as a potential method for data collection

Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM