Quantum Nano-Automata (QNA) : Towards Microphysical Measurements with Quantum, Nanoscale 'Instruments'

Two important concepts for nanoscience and nanotechnology-- the quantum automaton and quantum computation--were introduced in the context of quantum genetics and complex genetic networks with nonlinear dynamics. In previous publications (Baianu,1971a, b) the formal definition of quantum automaton was initially presented in the Schrodinger representation of quantum mechanics, and several possible implications for genetic processes and metabolic activities in living cells and organisms were considered. This was followed by reports on quantum, as well as symbolic, abstract computations based on the theory of categories, functors and natural transformations (Baianu,1971b; 1977; 1987; 2004; Baianu et al, 2004). The notions of quantum topological semigroup, quantum automaton, and/or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. A representation of interacting quantum automata of nanoscale ‘sizes’, such as devices possibly made of quantum dots, and their quantum state decoherence modalities is here considered in the Heisenberg picture of quantum dynamics. The interesting question of microphysical measurements performed by quantum nano-automata is raised in terms of quantum logics suitable for countable observations with definite probability densities. It is expected that such quantum nano-automata experience decoherence when they become irreversibly entangled with interacting systems in their surrounding environment. Novel experiments with small, nano-clusters of such quantum automata can be now designed for different types of quantized fields, and the results of such experiments for specific molecular force fields are then finitely computable by standard quantum theory in the Heisenberg picture. Keywords: Automata Theory/ Sequential Machines, Bioinformatics, Complex Biological Systems, Complex Systems Biology, Computer Simulations and Modeling, Dynamical Systems , Quantum Dynamics, Quantum Field Theory, Quantum Groups,Topological Quantum Field Theory (TQFT), Quantum Automata, Quantum Dots, Cognitive Systems, Graph Transformations, Logic, Mathematical Modeling; applications of the Theory of Categories, Functors and Natural Transformations; pushouts, pullbacks, presheaves, sheaves, Categories of sheaves, Topos, n-valued Logic, N-categories/ higher dimensional algebra, Homotopy theory; applications to quantum field theories, quantum gravity, complex systems biology, bioengineering, informatics, Bioinformatics, Computer simulations, Mathematical Biology of complex systems and phenomena in various types of Dynamical Systems; bioengineering, Computing, Neurosciences, Bioinformatics, biological and/or social networks; quantitative ecology and quantitative biology

Cell Interactomics and Carcinogenetic Mechanisms

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments

Is there a direct chloride cofactor requirement in the oxygen-evolving reactions of photosystem II?

The dark incubation at room temperature of photosystem II (PS II) membrane fragments in a chloride-free medium at pH 6.3 slowly leads to large chloride-restorable and non-restorable O evolution activity losses with time as compared with control samples incubated in the presence of 10 mM NaCl. The chloride requirement in O evolution generated under these conditions reveals a complex interplay among various experimental parameters, including the source of the plant material, the times of incubation, the sample concentration, the chloride concentration, as well as those treatments which are believed to specifically displace chloride from PS II such as alkaline pH pretreatment and NaSO addition. The results indicate that secondary, structural changes within the PS II complex are an important factor in determining the influence of chloride on the O evolution activity and raise the question whether or not chloride ions actually play a direct cofactor role in the water-oxidizing reactions leading to O evolution