By Arto Salomaa (auth.), Anne Condon, David Harel, Joost N. Kok, Arto Salomaa, Erik Winfree (eds.)
A primary realizing of algorithmic bioprocesses is essential to studying how info processing happens in nature on the mobile point. the sector is anxious with the interactions among laptop technology at the one hand and biology, chemistry, and DNA-oriented nanoscience at the different. specifically, this e-book bargains a complete evaluate of analysis into algorithmic self-assembly, RNA folding, the algorithmic foundations for biochemical reactions, and the algorithmic nature of developmental processes.
The editors of the publication invited 36 chapters, written by means of the prime researchers during this sector, and their contributions comprise exact tutorials at the major subject matters, surveys of the cutting-edge in learn, experimental effects, and discussions of particular study ambitions. the most matters addressed are series discovery, new release, and research; nanoconstructions and self-assembly; membrane computing; formal versions and research; method calculi and automata; biochemical reactions; and different themes from common computing, together with molecular evolution, legislation of gene expression, light-based computing, mobile automata, lifelike modelling of organic platforms, and evolutionary computing.
This topic is inherently interdisciplinary, and this e-book can be of price to researchers in laptop technological know-how and biology who learn the effect of the intriguing mutual interplay among our realizing of bioprocesses and our realizing of computation.
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Extra info for Algorithmic bioprocesses
3 Patterns with Mismatches Patterns of approximate nature are frequent in all walks of information processing and ubiquitous in computational biology. In fact, in most practical cases, “similarity” among objects is more informative than sheer identity. , Hamming  and Levenshtein  distance, and variations thereof. We treat first the problem of extracting from a given source x strings that occur unusually often in x within a prescribed maximum number of mismatches. In this context, we look thus for pairs (w, k) where w is a string of characters from an alphabet Σ and k is the number of errors or mismatches allowed on w.
For each such subtree Tji , we evaluate the associated Pji . The computation of Pji is done as for P0 above. Based on the Pji s, for j = 1, . . , S i , we compute the evolution content at a position i, denoted ECi , with the entropy function ECi = γ nαi log2 βni − log2 β where α, β, γ are parameters depending on the specific tree T we are working with. We define them and comment their significance below. The value ni is computed Si Pi i from n∗i = jP=10 j , where Sj =1 Pji ≥ N , by considering log10 n∗i (this operation gives a value in the interval [−x, 0], for some x) and by rescaling the result to the Pi interval [0, 1].
In: Proceedings of DCC 2004. IEEE Computer Society, Los Alamitos, pp 92–101 Information Content of Sets of Biological Sequences Revisited Alessandra Carbone and Stefan Engelen Abstract To analyze the information included in a pool of amino acid sequences, a first approach is to align the sequences, to estimate the probability of each amino acid to occur within columns of the aligned sequences and to combine these values through an “entropy” function whose minimum corresponds to absence of information, that is, to the case where each amino acid has the same probability to occur.