5909245
Modeling Gene Expression with Differential Equations
We propose a differential equation model for gene expression and provide two methods to construct the model from a set of temporal data. We model both transcription and translation by kinetic equations with feedback loops from translation products to transcription. Degradation of proteins and mRNAs is also incorporated. We study two methods to construct the model from experimental data: Minimum Weight Solutions to Linear Equations (MWSLE), which determines the regulation by solving under-determined linear equations, and Fourier Transform for Stable Systems (FTSS), which refines the model with cell cycle constraints. The results suggest that a minor set of temporal data may be sufficient to construct the model at the genome level. We also give a comprehensive discussion of other extended models: the RNA Model, the Protein Model, and the Time Delay Model.
8315237
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
Traditional quantitative structure-activity relationship (QSAR) models aim to capture global structure-activity trends present in a data set. In many situations, there may be groups of molecules which exhibit a specific set of features which relate to their activity or inactivity. Such a group of features can be said to represent a local structure-activity relationship. Traditional QSAR models may not recognize such local relationships. In this work, we investigate the use of local lazy regression (LLR), which obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set. This modeling approach is especially useful for very large data sets because no a priori model need be built. We applied the technique to three biological data sets. In the first case, the root-mean-square error (RMSE) for an external prediction set was 0.94 log units versus 0.92 log units for the global model. However, LLR was able to characterize a specific group of anomalous molecules with much better accuracy (0.64 log units versus 0.70 log units for the global model). For the second data set, the LLR technique resulted in a decrease in RMSE from 0.36 log units to 0.31 log units for the external prediction set. In the third case, we obtained an RMSE of 2.01 log units versus 2.16 log units for the global model. In all cases, LLR led to a few observations being poorly predicted compared to the global model. We present an analysis of why this was observed and possible improvements to the local regression approach.
31567164
Prediction of protein function using protein-protein interaction data
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database, using the protein-protein interaction data from the Munich Information Center for Protein Sequences. We show that our approach outperforms other available methods for function prediction based on protein interaction data.
2822926
Sorting with Fixed-length Reversals
Abstract A popular puzzle TOP-SPIN consists of a permutation of 20 numbered disks on an oval track, with a turnstile capable of reversing a string of 4 consecutive disks. The goal is to sort the disks into identity permutation using reversals. We consider the more general case of sorting n element permutations with a turnstile of size k. The problem of computing the reversal distances is of considerable importance in reconstructing the evolutionary history of the genome. The minimum reversal sequence sorting the one genome to another corresponds to the most likely evolutionary path between them. We give a complete solution for all n and k, of the number of equivalence classes of n-permutations under k-reversal, for both permutations and circular permutations. We also prove the upper and lower bounds on the reversal distances between two permutations.
2057857
Gene-finding via tandem mass spectrometry
We propose a new gene-finding methodology that combines high performance liquid chromatograph (HPLC)-tandem mass spectrometry experiments with a fast computer algorithm to locate coding regions and introns. Proteins are first extracted from cells and digested by enzymes, and then the resulting peptides are separated and analyzed by HPLC-tandem mass spectrometry. We designed an algorithm to find DNA coding sequences, corresponding to open reading frames (ORF), in the genome such that their translated amino acid sequences are optimally correlated with these tandem mass spectra. In this algorithm, we also allow one gap, corresponding to an intron, between two DNA coding sequences, such that their concatenation becomes one coding sequence. Finally, the algorithm assembles these candidate coding sequences and introns into gene structures. Our algorithm was implemented to predict genes on 4 contigs with a total of 123 kbps using two sets of simulated digestion- HPLC-tandem mass spectrometry data of 2523 Caenorhabditis elegans Chromosome IV proteins, digested by trypsin and Asp-N respectively. Among 15 annotated genes in the forward strand, all 98 exons are hit by the predicted no-gap coding sequences, and 60 out of 83 introns are correctly predicted. We also tested gene structure prediction in a contig containing 3 genes. Combining splicing site predictions with predicted coding sequences and introns, we found all 3 gene structures.
10712046
Algorithms for identifying protein cross-links via tandem mass spectrometry
Cross-linking technology combined with tandem mass spectrometry (MS-MS) is a powerful method that provides a rapid solution to the discovery of protein-protein interactions and protein structures. We studied the problem of detecting cross-linked peptides and cross-linked amino acids from tandem mass spectral data. Our method consists of two steps: the first step finds two protein subsequences whose mass sum equals a given mass measured from the mass spectrometry; and the second step finds the best cross-linked amino acids in these two peptide sequences that are optimally correlated to a given tandem mass spectrum. We designed fast and space-efficient algorithms for these two steps, and implemented and tested them on experimental data of cross-linked Hemoglobin proteins.
16035611
Swimming across the Pacific: a VR swimming interface
A locomotion interface for swimming and floating in a virtual ocean is part of the interactive installation of the Swimming Across the Pacific artwork. In the 'Swimming Across the Pacific' artwork, the authors create a space for collaborative artwork in an airplane by having a swimmer swim while flying across the Pacific Ocean. The swimmer's swimming represents a transformation of the airplane into an art gallery where the medium is the airplane, much as Misheff transformed the Queen Elizabeth II while he swam during her journey. By using elements such as the AV system, food, and clothing in the airplane for expression, fellow artists, scientists, engineers, musicians, media, and other passengers participate together to create artwork. This article is available with a short video documentary on CD-ROM.
7864516
On Finite Alphabets and Infinite Bases III: Simulation
This paper studies the (in)equational theory of simulation preorder and equivalence over the process algebra BCCSP. We prove that in the presence of a finite alphabet with at least two actions, the (in)equational theory of BCCSP modulo simulation preorder or equivalence does not have a finite basis. In contrast, in the presence of an alphabet that is infinite or a singleton, the equational theory for simulation equivalence does have a finite basis.
21381089
Dividing Strategies for the Optimization of a Test Suite
Use of the divide-and-conquer approach to optimize a test suite does not guarantee that the optimal solutions could be re-constructed from those of the sub-problems. This paper investigates how the re-construction could be achieved with two dividing strategies. Results of a simulation study are also reported.
1214419
On the Axiomatizability of Priority
This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. It is proven that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatization over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatizations using conditional equations. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatization consisting of conditional equations over BCCSP. Sufficient conditions on the priority structure over actions are identified that lead to a finite, ground-complete axiomatization of bisimulation equivalence using conditional equations
16174340
A new encryption algorithm for image cryptosystems
Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.