Thomas E. Renz

A weighted insertion-deletion stacked pair thermodynamic metric for DNA codes

Thermodynamic distance functions are important components in the construction of DNA codes and DNA codewords are structural and information building blocks in biomolecular computing and other biotechnical applications that employ DNA hybridization assays. We introduce new metrics for DNA code design that capture key aspects of the nearest neighbor thermodynamic model for hybridized DNA duplexes. One version of our metric gives the maximum number of stacked pairs of hydrogen bonded nucleotide base pairs that can be present in any secondary structure in a hybridized DNA duplex without pseudoknots. We introduce the concept of (t-gap) block isomorphic subsequences to describe new string metrics that are similar to the weighted Levenshtein insertion-deletion metric. We show how our new distances can be calculated by a generalization of the folklore longest common subsequence dynamic programming algorithm. We give a Varshamov-Gilbert like lower bound on the size of some of codes using our distance functions as constraints. We also discuss software implementation of our DNA code design methods.

Performance optimization for pattern recognition using associative neural memory

In this paper, we present our work in the implementation and performance optimization of the recall operation of the brain-state-in-a-box (BSB) model on the cell broadband engine processor. We have applied optimization techniques on different parts of the algorithm to improve the overall computing and communication performance of the BSB recall algorithm. Runtime measurements show that, we have been able to achieve about 70% of the theoretical peak performance of the processor.

New results on DNA codes

For q-ary n-sequences, we develop the concept of similarity functions that can be used (for q = 4) to model a thermodynamic similarity on DNA sequences. A similarity function is identified by the length of a longest common subsequence between two q-ary n-sequences. Codes based on similarity functions are called DNA codes. DNA codes are important components in biomolecular computing and other biotechnical applications that employ DNA hybridization assays. We present our unpublished results connected with the conventional deletion similarity function used in the theory of error-correcting codes. The main aim of this paper - to obtain lower bounds on the rate of optimal DNA codes for a biologically motivated similarity function called a similarity of blocks. We also present constructions of suboptimal DNA codes based on the parity-check code detecting one error in the Hamming metric

New t-Gap Insertion-Deletion-Like Metrics for DNA Hybridization Thermodynamic Modeling

We discuss the concept of t-gap block isomorphic subsequences and use it to describe new abstract string metrics that are similar to the Levenshtein insertion-deletion metric. Some of the metrics that we define can be used to model a thermodynamic distance function on single-stranded DNA sequences. Our model captures a key aspect of the nearest neighbor thermodynamic model for hybridized DNA duplexes. One version of our metric gives the maximum number of stacked pairs of hydrogen bonded nucleotide base pairs that can be present in any secondary structure in a hybridized DNA duplex without pseudoknots. Thermodynamic distance functions are important components in the construction of DNA codes, and DNA codes are important components in biomolecular computing, nanotechnology, and other biotechnical applications that employ DNA hybridization assays. We show how our new distances can be calculated by using a dynamic programming method, and we derive a Varshamov-Gilbert-like lower bound on the size of some of codes using these distance functions as constraints. We also discuss software implementation of our DNA code design methods.

PCR NONADAPTIVE GROUP TESTING OF DNA LIBRARIES FOR BIOMOLECULAR COMPUTING AND TAGGANT APPLICATIONS

Using a universal and parallel battery of PCR reactions, we give a nonadaptive group testing method for identifying the individual strands in a pooled sample of several different DNA sequences taken from a DNA library. The method discussed here has potential applications to DNA taggants, DNA memory and DNA computing.

Random Coding Bounds for DNA codes based on Fibonacci ensembles of DNA sequences

We consider DNA codes based on the concept of a weighted 2-stem similarity measure which reflects the ldquohybridization potentialrdquo of two DNA sequences. A random coding bound on the rate of DNA codes with respect to a thermodynamic motivated similarity measure is proved. Ensembles of DNA strands whose sequence composition is restricted in a manner similar to the restrictions in binary Fibonacci sequences are introduced to obtain the bound.

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

We define new measures of sequence similarity for oligonucleotide probe design. These new measures incorporate the nearest neighbor k-stem motifs in their definition, but can be efficiently computed by means of a bit-vector method. They are not as computationally costly as algorithms that predict nearest neighbor hybridization potential. Our new measures for sequence similarity correlate significantly better with nearest neighbor thermodynamic predictions than either BLAST or the standard edit or insertion-deletion defined similarities already in use in many different probe design applications.

On critical relative distance of DNA codes for additive stem similarity

We consider DNA codes based on the nearest-neighbor (stem) similarity model which adequately reflects the “hybridization potential” of two DNA sequences. Our aim is to present a survey of bounds on the rate of DNA codes with respect to a thermodynamically motivated similarity measure called an additive stem similarity. These results yield a method to analyze and compare known samples of the nearest neighbor “thermo-dynamic weights” associated to stacked pairs that occurred in DNA secondary structures.

Accelerating cogent confabulation: An exploration in the architecture design space

Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software and hardware optimization. On the software implementation side, appropriate data structures can improve the performance of the software by more than 5,000X. On the hardware implementation side, the cogent confabulation algorithm is an ideal candidate for parallel processing and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost tradeoffs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve good cost performance tradeoffs.

Hypothesis group testing for disjoint pairs

Abstract Classical group testing (CGT) is a widely applicable biotechnical technique used to identify a small number of distinguished objects from a population when the presence of any one of these distinguished objects among a group of others produces an observable result. This paper discusses a variant of CGT called group testing for disjoint pairs (GTAP). The difference between the two is that in GTDP, the distinguished items are pairs from, not individual objects in, the population. There are several biological examples of when this abstract model applies. One biological example is DNA hybridization. The presence of pairs of hybridized DNA strands can be detected in a pool of DNA strands. Another situation is the detection of binding interactions between prey and bait proteins. This paper gives a random pooling method, similar in spirit to hypothesis testing, which identifies pairs of objects from a population that collectively have an observable function. This method is simply to apply, achieves good results, is amenable to automation and can be easily modified to compensate for testing errors.