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

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

It has long been a fundamental open problem whether polylog time and {\em linear} processors are sufficient to find the strongly connected components of a directed graph and compute directed spanning trees for these components. This paper provides the first nontrivial partial solution to the tree problem: for a planar directed graph with $n$ vertices, if the graph is strongly connected, then a directed spanning tree rooted at a specified vertex can be built in $0(log^2~n)$ time using $0(n)$ processors. The algorithm is deterministic and runs on a parallel random access machine that allows concurrent reads and concurrent writes in its shared memory. The result complements an algorithm by Kao that computes the strongly connected components of a planar directed graph in $0(log^3~n)$ time and $0(n)$ processors.

Linear-processor NC algorithms for planar directed graphs II: directed spanning trees

The number of the non-shared edges of two phylogenies is a basic measure of the distance (dissimilarity) between the phylogenies. The non-shared edges are also the building block for approximating a more sophisticated metric called the NNI distance. In this paper we give the first sub-quadratic time algorithm for finding the non-shared edges, which are then used to speed up the existing approximation algorithm for the NNI distance. The time is improved from O(n2) to O(n log n). Another popular distance metric for phylogenies is the STT distance. Previous work on computing the STT distance focused on degree-3 trees only. We show that the STT distance can be applied in a broader sense, allowing us to cover degree-d trees, where d ≥ 3. In particular, we give a new approximation algorithm for the STT distance.

Improved Phylogeny Comparisons: Non-shared Edges, Nearest Neighbor Interchanges, and Subtree Transfers

Given an undirected graph G and two vertex subsets H1 and H2, the bi-level augmentation problem is that of adding to G the smallest number of edges such that the resulting graph contains two internally vertex-disjoint paths between every pair of vertices in H1 and two edge-disjoint paths between every pair of vertices in H2. We present an algorithm to solve this problem in linear time. By properly setting H1 and H2, this augmentation algorithm subsumes existing optimal algorithms for several graph augmentation problems.

A Unifying Augmentation Algorithm for Two-Edge Connectivity and Biconnectivity

An evolutionary tree is a rooted tree where each internal vertex has at least two children and where the leaves are labeled with distinct symbols representing species. Evolutionary trees are useful for modeling the evolutionary history of species. An agreement subtree of two evolutionary trees is an evolutionary tree which is also a topological subtree of the two given trees. We give an algorithm to determine the largest possible number of leaves in any agreement subtree of two trees T1 and T2 with n leaves each. If the maximum degree d of these trees is bounded by a constant, the time complexity is O(n log2n) and is within a log n factor of optimal. For general d, this algorithm runs in O(nd2 log d log2n) time or alternatively in O(nd√d log3n) time.

Ming-Yang Kao

Papers

Fast Universalization of Investment Strategies with Provably Good Relative Returns

Linear-processor NC algorithms for planar directed graphs II: directed spanning trees

Improved Phylogeny Comparisons: Non-shared Edges, Nearest Neighbor Interchanges, and Subtree Transfers

A Unifying Augmentation Algorithm for Two-Edge Connectivity and Biconnectivity

Tree Contractions and Evolutionary Trees