Designable DNA-binding domains enable construction of logic circuits in mammalian cells.

Electronic computer circuits consisting of a large number of connected logic gates of the same type, such as NOR, can be easily fabricated and can implement any logic function. In contrast, designed genetic circuits must employ orthogonal information mediators owing to free diffusion within the cell. Combinatorial diversity and orthogonality can be provided by designable DNA- binding domains. Here, we employed the transcription activator-like repressors to optimize the construction of orthogonal functionally complete NOR gates to construct logic circuits. We used transient transfection to implement all 16 two-input logic functions from combinations of the same type of NOR gates within mammalian cells. Additionally, we present a genetic logic circuit where one input is used to select between an AND and OR function to process the data input using the same circuit. This demonstrates the potential of designable modular transcription factors for the construction of complex biological information-processing devices.

Biological reactivity of nanoparticles: mosaics from optical microscopy videos of giant lipid vesicles.

Emerging fields such as nanomedicine and nanotoxicology, demand new information on the effects of nanoparticles on biological membranes and lipid vesicles are suitable as an experimental model for bio-nano interaction studies. This paper describes image processing algorithms which stitch video sequences into mosaics and recording the shapes of thousands of lipid vesicles, which were used to assess the effect of CoFe(2)O(4) nanoparticles on the population of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine lipid vesicles. The applicability of this methodology for assessing the potential of engineered nanoparticles to affect morphological properties of lipid membranes is discussed.

Interactions of nanoparticles with lipid vesicles: a population based computer aided image analysis approach.

Novel properties of nanoparticles have numerous potential technological applications but at the same time they underlie new kinds of biological effects. Uniqueness of nanoparticles and nanomaterials requires a new experimental methodology. Much evidence suggests that nanoparticles affect cell membrane stability and subsequently exert toxic effects. For this kind of research, lipid vesicles are of high value due to controllability and repeatability of experimental conditions. The aim of work presented here was to develop a computer aided analysis of lipid vesicles shape transformations. We studied a population of palmitoyloleoylphosphatidylcholine (POPC) lipid vesicles after exposure to nanoparticles (C(60)) or a reference chemical (ZnCl(2)). With the use of computer image analysis methods, we detected differences in size distributions of vesicles in different exposure groups. Though, at the present state, we are not able to precisely identify effects of nanoparticles on shape transformations of vesicles, those incubated with nanoparticles were in average larger than those in other groups. This population based approach holds many promises for future investigation of nanoparticles-lipid vesicles, or even nanoparticles-biological membranes interactions. However, in order to get reliable results, numerous images have to be analyzed which requires improved and highly automated image segmentation and analyses methods.

On-line identification and reconstruction of finite automata with generalized recurrent neural networks.

In this paper finite automata are treated as general discrete dynamical systems from the viewpoint of systems theory. The unconditional on-line identification of an unknown finite automaton is the problem considered. A generalized architecture of recurrent neural networks with a corresponding on-line learning scheme is proposed as a solution to the problem. An on-line rule-extraction algorithm is further introduced. The architecture presented, the on-line learning scheme and the on-line rule-extraction method are tested on different, strongly connected automata, ranging from a very simple example with two states only to a more interesting and complex one with 64 states; the results of both training and extraction processes are very promising.