DNA computing

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This work explores various models and applications of molecular and membrane computing, focusing on spiking neural P systems and their computational capabilities. It discusses minimal parallelism in polarizationless P systems and the characterization of PSPACE through active membrane systems. The text highlights the potential of splicing processor networks to solve NP problems in polynomial time and examines test tube systems for length separation. Gene assembly algorithms for ciliates, complexity analysis, and the spectrum of DNA complexes are also covered. The complexity of graph self-assembly in accretive systems and viral genome compression are addressed, alongside the properties of DNA codes and optimal designs for DNA computing. The work delves into dynamic neighborhood searches for DNA sequence design and the stability of DNA tiles, as well as hairpin structures defined by DNA trajectories. It presents models for self-assembly, self-repairing DNA lattices, and the computational times for 2D tile assembly. The limits of compact error resilience methods in self-assembly are discussed, along with a mathematical approach to viral capsid structures for drug delivery. Additionally, it introduces simulators for DNA computing, frameworks for molecular system modeling, and software for DNA sequence generation. The text also covers probabilistic models of DNA conformational changes, microreactor simulations, and new applications