Evolution of Protein Molecules

THE JOURNAL OF BIOLOGICAL CHEMISTRY: The Biochemical Behavior of LeadL I. Influence of Calcium, Phosphorus, and Vitamin D on Lead in Blood and Bone

To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler.

http://science.sciencemag.org/content/sci/332/6034/1196.full.pdf

Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades

A number of different approaches have been described to identify proteins from tandem mass spectrometry (MS/MS) data. The most common approaches rely on the available databases to match experimental MS/MS data. These methods suffer from several drawbacks and cannot be used for the identification of proteins from unknown genomes. In this communication, we describe a new de novo sequencing software package, PEAKS, to extract amino acid sequence information without the use of databases. PEAKS uses a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum. The output of the software gives amino acid sequences with confidence scores for the entire sequences, as well as an additional novel positional scoring scheme for portions of the sequences. The performance of PEAKS is compared with Lutefisk, a well-known de novo sequencing software, using quadrupole-time-of-flight (Q-TOF) data obtained for several tryptic peptides from standard proteins.

/pdf/peaks-powerful-software-for-peptide-de-novo-sequencing-by-1htzy7q8mi.pdf

PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry

The impressive capabilities of the mammalian brain—ranging from perception, pattern recognition and memory formation to decision making and motor activity control—have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the ‘intelligent’ behaviour required for survival However, the study of how molecules can ‘think’ has not produced an equal variety of computational models and applications of artificial chemical systems Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo and the synthesis of artificial chemical analogues has been proposed theoretically, experimental work has so far fallen short of fully implementing even a single neuron Here, building on the richness of DNA computing and strand displacement circuitry, we show how molecular systems can exhibit autonomous brain-like behaviours Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, we systematically transform arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks Our approach even allows us to implement a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment

/pdf/neural-network-computation-with-dna-strand-displacement-2m7egtgffn.pdf

Neural network computation with DNA strand displacement cascades

Designing short DNA words is a problem of constructing n DNA strings (words) with the minimum length such that the Hamming distance between each pair is at least k and the words satisfy a set of extra constraints This problem has applications in DNA computing, DNA self-assembly, and DNA arrays Previous works include those that extended results from coding theory to obtain bounds on code size for biologically motivated constraints and those that applied heuristic local searches, genetic algorithms, and randomized algorithms In particular, Kao, Sanghi and Schweller developed polynomial-time randomized algorithms to construct n DNA words of length 9· max {logn,k} satisfying a sets of constraints with high probability In this paper, we give deterministic polynomial-time algorithms to construct DNA words based on expander codes, Ramanujan graphs, and derandomization techniques Our algorithms can construct n DNA words of length max {3logn, 4k} or 2.1 logn+6.28 k satisfying the same sets of constraints as the words constructed by the algorithms of Kao et al We have also extended these algorithms to construct words that satisfy a larger set of constraints for which the algorithms of Kao et al do not work.

Deterministic polynomial-time algorithms for designing short DNA words

We propose a general methodology for testing whether a given polynomial with integer coefficients is identically zero. The methodology evaluates the polynomial at efficiently computable approximations of suitable irrational points. In contrast to the classical technique of DeMillo, Lipton, Schwartz, and Zippel, this methodology can decrease the error probability by increasing the precision of the approximations instead of using more random bits. Consequently, randomized algorithms that use the classical technique can generally be improved using the new methodology. To demonstrate the methodology, we discuss two nontrivial applications. The first is to decide whether a graph has a perfect matching in parallel. Our new NC algorithm uses fewer random bits while doing less work than the previously best NC algorithm by Chari, Rohatgi, and Srinivasan. The second application is to test the equality of two multisets of integers. Our new algorithm improves upon the previously best algorithms by Blum and Kannan and can speed up their checking algorithm for sorting programs on a large range of inputs.

Reducing Randomness via Irrational Numbers

A widely-used method for determining the similarity of two labeled trees is to compute a maximum agreement subtree of the two trees Previous work on this similarity measure only concerns with the comparison of labeled trees of two special kinds, namely, uniformly labeled trees (ie, trees with all their nodes labeled by the same symbol) and evolutionary trees (ie, leaf-labeled trees with distinct symbols for distinct leaves) This paper presents an algorithm for comparing trees that are labeled in an arbitrary manner In addition to the generalization, our algorithm is faster than the previous algorithms in many cases

A Faster and Unifying Algorithm for Comparing Trees

Motivated by emerging applications for DNA code word design, we consider a generalization of the code word design problem in which an input graph is given which must be labeled with equal length binary strings of minimal length such that the Hamming distance is small between words of adjacent nodes and large between words of non-adjacent nodes. For general graphs we provide algorithms that bound the word length with respect to either the maximum degree of any vertex or the number of edges in either the input graph or its complement. We further provide multiple types of recursive, deterministic algorithms for trees and forests, and provide an improvement for forests that makes use of randomization.

/pdf/flexible-word-design-and-graph-labeling-29difspzs0.pdf

Flexible word design and graph labeling

This paper initiates a study into the century-old issue of market predictability from the perspective of computational complexity. We develop a simple agent-based model for a stock market where the agents are traders equipped with simple trading strategies, and their trades together determine the stock prices. Computer simulations show that a basic case of this model is already capable of generating price graphs which are visually similar to the recent price movements of high tech stocks. In the general model, we prove that if there are a large number of traders but they employ a relatively small number of strategies, then there is a polynomial-time algorithm for predicting future price movements with high accuracy. On the other hand, if the number of strategies is large, market prediction becomes complete in two new computational complexity classes CPP and BCPP, where PNP[O(log n)]e BCPP e CPP = PP. These computational completeness results open up a novel possibility that the price graph of a actual stock could be sufficiently deterministic for various prediction goals but appear random to all polynomial-time prediction algorithms.

Ming-Yang Kao

Papers

Deterministic polynomial-time algorithms for designing short DNA words

Reducing Randomness via Irrational Numbers

A Faster and Unifying Algorithm for Comparing Trees

Flexible word design and graph labeling

Towards understanding the predictability of stock markets from the perspective of computational complexity