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

This paper studies some basic problems in a multiple-object auction model using methodologies from theoretical computer science. We are especially concerned with situations where an adversary bidder knows the bidding algorithms of all the other bidders. In the two-bidder case, we derive an optimal randomized bidding algorithm, by which the disadvantaged bidder can procure at least half of the auction objects despite the adversary's a priori knowledge of his algorithm. In the general k-bidder case, if the number of objects is a multiple of k, an optimal randomized bidding algorithm is found. If the k − 1 disadvantaged bidders employ that same algorithm, each of them can obtain at least 1/k of the objects regardless of the bidding algorithm the adversary uses. These two algorithms are based on closed-form solutions to certain multivariate probability distributions. In situations where a closed-form solution cannot be obtained, we study a restricted class of bidding algorithms as an approximation to desired optimal algorithms.

Optimal Bidding Algorithms Against Cheating in Multiple-Object Auctions

Given a multiset X={x 1,... x n } of real numbers, the floating-point set summation (FPS) problem asks for S n = x 1+...+ x n , and the floating point prefix set summation problem (FPPS) asks for S k =x 1+...+x k for all k = 1, ... n. Let E k * denote the minimum worst-case error over all possible orderings of evaluating S k . We prove that if X has both positive and negative numbers, it is NP-hard to compute S n with the worst-case error equal to E n * . We then give the first known polynomial-time approximation algorithm for computing Sn that has a provably small error for arbitrary X. Our algorithm incurs a worstcase error at most 2([log(n−1)]+1) E n * . After X is sorted, it runs in O(n) time, yielding an O(n 2)-time approximation algorithm for computing S k for all k = 1, ..., n such that the worst-case error for each S k is less than 2(⌈ log(k−1)⌋+ 1)E k * .

Efficient Minimization of Numerical Summation Errors

A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala involving the sampling of log-concave functions can be generalized to other classes of investment strategies.

Fast Universalization of Investment Strategies with Provably Good Relative Returns

Given a multiset X={x1,. . .,xn} of real numbers, the floating-point set summation problem asks for Sn=x1 + . . . + xn. Let $E^*_n$ denote the minimum worst-case error over all possible orderings of evaluating Sn. We prove that if X has both positive and negative numbers, it is NP-hard to compute Sn with the worst-case error equal to $E^*_n$. We then give the first known polynomial-time approximation algorithm that has a provably small error for arbitrary X. Our algorithm incurs a worst-case error at most $2(\lceil\log(n-1)\rceil+1)E^*_n$. (All logarithms log in this paper are base 2.) After X is sorted, it runs in O(n) time. For the case where X is either all positive or all negative, we give another approximation algorithm with a worst-case error at most $\lceil\log\log n\rceil E^*_n$. Even for unsorted X, this algorithm runs in O(n) time. Previously, the best linear-time approximation algorithm had a worst-case error at most $\lceil\log n\rceil E^*_n$, while $E^*_n$ was known to be attainable in O(n log n) time using Huffman coding.

Ming-Yang Kao

Papers

Optimal Bidding Algorithms Against Cheating in Multiple-Object Auctions

Efficient Minimization of Numerical Summation Errors

Fast Universalization of Investment Strategies with Provably Good Relative Returns

Linear-Time Approximation Algorithms for Computing Numerical Summation with Provably Small Errors

Non-shared edges and nearest neighbor interchanges revisited