5737475
Toward Optimal Motif Enumeration
We present algorithms that reduce the time and space needed to solve problems of finding all motifs common to a set of sequences. In particular, we give algorithms that (1) require time and space linear in the size of the input, (2) succinctly encode the output so that the time and space requirements depend on the number of motifs, not directly on motif length, and (3) efficiently parallelize the enumeration.
7234685
Mining ChIP-chip data for transcription factor and cofactor binding sites
MOTIVATION
Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data.


RESULTS
We describe methodology to identify de novo individual and interacting pairs of binding site motifs from ChIP-chip data, using an algorithm that integrates localization data directly into the motif discovery process. We combine matrix-enumeration based motif discovery with multivariate regression to evaluate candidate motifs and identify motif interactions. When applied to the HNF localization data in liver and pancreatic islets, our methods produce motifs that are either novel or improved known motifs. All motif pairs identified to predict localization are further evaluated according to how well they predict expression in liver and islets and according to how conserved are the relative positions of their occurrences. We find that interaction models of HNF1 and CDP motifs provide excellent prediction of both HNF1 localization and gene expression in liver. Our results demonstrate that ChIP-chip data can be used to identify interacting binding site motifs.


AVAILABILITY
Motif discovery programs and analysis tools are available on request from the authors.
716092
Complexity of Approximating Closest Substring Problems
The closest substring problem, where a short string is sought that minimizes the number of mismatches between it and each of a given set of strings, is a minimization problem with a polynomial time approximation scheme [6]. In this paper, both this problem and its maximization complement, where instead the number of matches is maximized, are examined and bounds on their hardness of approximation are proved. Related problems differing only in their objective functions, seeking either to maximize the number of strings covered by the substring or maximize the length of the substring, are also examined and bounds on their approximability proved. For this last problem of length maximization, the approximation bound of 2 is proved to be tight by presenting a 2-approximation algorithm.
19034554
SIMPROT: Using an empirically determined indel distribution in simulations of protein evolution
BackgroundGeneral protein evolution models help determine the baseline expectations for the evolution of sequences, and they have been extensively useful in sequence analysis and for the computer simulation of artificial sequence data sets.ResultsWe have developed a new method of simulating protein sequence evolution, including insertion and deletion (indel) events in addition to amino-acid substitutions. The simulation generates both the simulated sequence family and a true sequence alignment that captures the evolutionary relationships between amino acids from different sequences. Our statistical model for indel evolution is based on the empirical indel distribution determined by Qian and Goldstein. We have parameterized this distribution so that it applies to sequences diverged by varying evolutionary times and generalized it to provide flexibility in simulation conditions. Our method uses a Monte-Carlo simulation strategy, and has been implemented in a C++ program named Simprot.ConclusionSimprot will be useful for testing methods of analysis of protein sequence families particularly alignment methods, phylogenetic tree building, detection of recombination and horizontal gene transfer, and homology detection, where knowing the true course of sequence evolution is essential.
4331453
Computational prediction of novel components of lung transcriptional networks
MOTIVATION
Little is known regarding the transcriptional mechanisms involved in forming and maintaining epithelial cell lineages of the mammalian respiratory tract.


RESULTS
Herein, a motif discovery approach was used to identify novel transcriptional regulators in the lung using genes previously found to be regulated by Foxa2 or Wnt signaling pathways. A human-mouse comparison of both novel and known motifs was also performed. Some of the factors and families identified here were previously shown to be involved epithelial cell differentiation (ETS family, HES-1 and MEIS-1), and ciliogenesis (RFX family), but have never been characterized in lung epithelia. Other unidentified over-represented motifs suggest the existence of novel mammalian lung transcription factors. Of the fraction of motifs examined we describe 25 transcription factor family predictions for lung. Fifteen novel factors were shown here to be expressed in mouse lung, and/or human bronchial or distal lung epithelial tissues or lung epithelial cell lineages.


AVAILABILITY
DME: http://rulai.cshl.edu/dme. MATCOMPARE: http://rulai.cshl.edu/MatCompare. MOTIFCLASS is available from the authors.
15197279
On the complexity of finding common approximate substrings
Problems associated with finding strings that are within a specified Hamming distance of a given set of strings occur in several disciplines. In this paper, we use techniques from parameterized complexity to assess non-polynomial time algorithmic options and complexity for the COMMON APPROXIMATE SUBSTRING (CAS) problem. Our analyses indicate under which parameter restrictions useful algorithms are possible, and include both class membership and parameterized reductions to prove class hardness. In order to achieve fixed-parameter tractability, either a fixed string length or both a fixed size alphabet and fixed substring length are sufficient. Fixing either the string length or the alphabet size and Hamming distance is shown to be necessary, unless W[1] = FPT. An assortment of parameterized class membership and hardness results cover all other parameterized variants, showing in particular the effect of fixing the number of strings.
589196
Using user interface event information in dynamic voltage scaling algorithms
Increasingly, mobile computers use dynamic voltage scaling (DVS) to reduce CPU voltage and speed and thereby increase battery life. To determine how to change voltage and speed when responding to user interface events, we analyze traces of real user workloads. We evaluate a new heuristic for inferring when user interface tasks complete and find it is more efficient and nearly as effective as other approaches. We compare DVS algorithms and find that for a given performance level, the PACE algorithm uses the least energy and the Stepped algorithm uses the second least. We find that different types of user interface event (mouse movements, mouse clicks, and keystrokes) trigger tasks with significantly different CPU use, suggesting one should use different speeds for different event types. We also find differences in CPU use between categories of the same event type, e.g., between pressing spacebar and pressing enter, and between events of different applications. Thus, it is better to predict task CPU use based solely on tasks of the same category and application. However, energy savings from such improved predictions are small.
42361348
Analysis of the Optimal, Look-Ahead Demand Paging Algorithms
We express the future behavior of programs that may be described by two common program behavior models, the independent reference model and the LRU stack model, by a discrete time Markov chain. Using this Markov chain model, we are able to calculate the theoretical minimum number of page faults for a program representable by either of these models in either a fixed or variable size memory. The behavior of optimal look-ahead and optimal realizable demand paging algorithms are compared, and it is seen that look-ahead paging demonstrates an inherent advantage sufficient to account for the differences observed between currently implemented demand paging algorithms and theoretically optimal algorithms.
519042
Evolving a Scalable Multirobot Controller Using an Artificial Neural Tissue Paradigm
We present an "artificial neural tissue" (ANT) architecture as a control system for autonomous multirobot tasks. This architecture combines a typical neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue which in turn allows such emergent capabilities as task decomposition. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to derive controllers for the task in simulation. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT-based controllers are shown to exhibit self-organization, employ stigmergy and make use of templates (unlabeled environmental cues). These controllers have been tested on a multirobot resource-collection task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings. The issues of scalability and antagonism are addressed
2002086
Exploration of heterogeneous reconfigurable architectures
In contrast to early reconfigurable hardware, modern reconfigurable devices consist of a mixture of heterogeneous components. The heterogeneous components introduced in recent devices are both line and coarse-grain in their nature, and are primarily aimed at improving the speed, power consumption and area metrics of target designs. The aim of this PhD project is to look at analytical ways of exploring the design space of modern reconfigurable hardware. By automatically generating architectures and estimating benchmark performance, linear programming can eliminate architectural possibilities in an efficient way, meaning that a large number of different architectures can be examined in a fast and optimal manner.
314094
Correcting errors without leaking partial information
This paper explores what kinds of information two parties must communicate in order to correct errors which occur in a shared secret string W. Any bits they communicate must leak a significant amount of information about W --- that is, from the adversary's point of view, the entropy of W will drop significantly. Nevertheless, we construct schemes with which Alice and Bob can prevent an adversary from learning any useful information about W. Specifically, if the entropy of W is sufficiently high, then there is no function f(W) which the adversary can learn from the error-correction information with significant probability.This leads to several new results: (a) the design of noise-tolerant "perfectly one-way" hash functions in the sense of Canetti et al. [7], which in turn leads to obfuscation of proximity queries for high entropy secrets W; (b) private fuzzy extractors [11], which allow one to extract uniformly random bits from noisy and nonuniform data W, while also insuring that no sensitive information about W is leaked; and (c) noise tolerance and stateless key re-use in the Bounded Storage Model, resolving the main open problem of Ding [10].The heart of our constructions is the design of strong randomness extractors with the property that the source W can be recovered from the extracted randomness and any string W' which is close to W.