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PubMed1987

Induction of common patterns of polypeptide synthesis and phosphorylation by calcium and 12-O-tetradecanoylphorbol-13-acetate in mouse epidermal cell culture.

Terminal differentiation can be induced in cultured basal cells by either increasing the Ca2+ level in the medium from 0.05 to 1.4 mM or by exposure to the tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA). If Ca2+ and TPA act by a common mechanism, then a common pattern of protein synthesis and/or phosphorylation would be expected. Computer-assisted analysis of radioactively labeled polypeptides separated by two-dimensional-polyacrylamide gel electrophoresis was utilized to study protein synthesis and phosphorylation. Within 1 h of increasing the Ca2+ level in the medium, the synthesis of 57 polypeptides was altered by 2-fold or more. Similarly, exposure to TPA for 1 h affected the synthesis of 106 polypeptides. Sixteen polypeptides were affected by both Ca2+ and TPA; the synthesis of nine was increased and seven was decreased, with changes in the same direction for both effectors. By 4 h, the synthesis of 32 polypeptides was similarly modulated by both Ca2+ and TPA. Only one polypeptide which was increased at 1 h was still elevated at 4 h. These results suggest that a common dynamic program of protein synthesis, likely to be related to terminal keratinocyte differentiation, is induced by both Ca2+ and TPA. Overall phosphorylation of epidermal proteins was increased after 30 min of TPA treatment, but was not increased by Ca2+ at this time. Keratin polypeptides were heavily phosphorylated in low Ca2+ medium, but the level or pattern of phosphorylation of these proteins was not altered by either Ca2+ or TPA. Although phosphorylation of a minor polypeptide (pI 5.1/Mr 45,000) was increased 2-3-fold by both Ca2+ and TPA, most of the specific protein phosphorylation changes induced in keratinocytes by Ca2+ and TPA appear to be unique. Thus, if protein phosphorylation is an early signal for epidermal differentiation by each effector, only a single apparent common substrate is involved and multiple kinases are activated. Alternatively, substrate specificity of a single kinase may be differentially altered by each effector.

Animals↗
PubMed1985

Redistribution of arterial blood flow in metastases-bearing livers after infusion of degradable starch microspheres.

Changes in intrahepatic arterial blood flow after intraarterial injection of degradable starch microspheres (DSM) were studied in four patients undergoing hepatic arterial chemotherapy. All four livers contained metastases, three from colorectal cancer and one from melanoma. Using a CT scanner with a dynamic program, 8 mm liver sections were studied in each patient before and after the DSM infusion (180(-6) in 3 min). Density plots were obtained from 12 tumoral and 12 parenchymal areas after 5 ml push arterial injections of nonionic contrast medium. The areas under the curves (ID) were calculated. The ID after DSM infusion was reduced by 94% in a single hyperdense colorectal metastasis and by a mean of c. 82% in ten parenchymal areas. By contrast, nine hypodense colorectal metastases showed an average ID decrease of c. 156%. The ID of two melanoma metastases was reduced after DSM (-48% and -68%), while the ID of two matched parenchymal areas showed an approximately similar degree of increase (+36% and +64%). Since ID after contrast injection can be assumed to be a function of blood volume, mutual changes of parenchymal and tumoral blood flow appear to take place in metastases-bearing liver after arterial infusion of DSM. This phenomenon may be of diagnostic and therapeutic value for intraarterial chemotherapy of liver tumors.

Antineoplastic Agents↗
PubMedDate not supplied

[Tasks in planning biological experiments].

The paper describes certain cases of using mathematical methods in the planning of biological experiments. The paper presents an algorithm of the distribution of the experimental data based on dynamic programming. The paper discusses an application of computer-aided calculations for the formation of homogeneous groups of experimental and control tests.

Animals↗
PubMed1993

A modular learning environment for protein modeling.

We propose in this paper a modular learning environment for protein modeling. In this system, the protein modeling problem is tackled in two successive phases. First, partial structural informations are determined via numerical learning techniques. Then, in the second phase, the multiple available informations are combined in pattern matching searches via dynamic programming. It is shown on real problems that various protein structure predictions can be improved in this way, such as secondary structure prediction, alignment of weakly homologous protein sequences or protein model evaluations.

Amino Acid Sequence↗
PubMed1993

Discovering sequence similarity by the algorithmic significance method.

The minimal-length encoding approach is applied to define concept of sequence similarity. A sequence is defined to be similar to another sequence or to a set of keywords if it can be encoded in a small number of bits by taking advantage of common subwords. Minimal-length encoding of a sequence is computed in linear time, using a data compression algorithm that is based on a dynamic programming strategy and the directed acyclic word graph data structure. No assumptions about common word ("k-tuple") length are made in advance, and common words of any length are considered. The newly proposed algorithmic significance method provides an exact upper bound on the probability that sequence similarity has occurred by chance, thus eliminating the need for any arbitrary choice of similarity thresholds. Preliminary experiments indicate that a small number of keywords can positively identify a DNA sequence, which is extremely relevant in the context of partial sequencing by hybridization.

Algorithms↗
PubMed1993

FLASH: a fast look-up algorithm for string homology.

A key issue in managing today's large amounts of genetic data is the availability of efficient, accurate, and selective techniques for detecting homologies (similarities) between newly discovered and already stored sequences. A common characteristic of today's most advanced algorithms, such as FASTA, BLAST, and BLAZE is the need to scan the contents of the entire database, in order to find one or more matches. This design decision results in either excessively long search times or, as is the case of BLAST, in a sharp trade-off between the achieved accuracy and the required amount of computation. The homology detection algorithm presented in this paper, on the other hand, is based on a probabilistic indexing framework. The algorithm requires minimal access to the database in order to determine matches. This minimal requirement is achieved by using the sequences of interest to generate a highly redundant number of very descriptive tuples; these tuples are subsequently used as indices in a table look-up paradigm. In addition to the description of the algorithm, theoretical and experimental results on the sensitivity and accuracy of the suggested approach are provided. The storage and computational requirements are described and the probability of correct matches and false alarms is derived. Sensitivity and accuracy are shown to be close to those of dynamic programming techniques. A prototype system has been implemented using the described ideas. It contains the full Swiss-Prot database rel 25 (10 MR) and the genome of E. Coli (2 MR). The system is currently being expanded to include the complete Genbank database.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1994

High speed pattern matching in genetic data base with reconfigurable hardware.

Homology detection in large data bases is probably the most time consuming operation in molecular genetic computing systems. Moreover, the progresses made all around the world concerning the mapping and sequencing of the genome of Homo Sapiens and other species have increased the size of data bases exponentially. Therefore even the best workstation would not be able to reach the scanning speed required. In order to answer this need we propose an algorithm, A2R2, and its implementation on a massively parallel system. Basically, two kinds of algorithms are used to search in molecular genetic data bases. The first kind is based on dynamic programming and the second on word processing, A2R2 belongs to the second kind. The structure of the motif (pattern) searched by A2R2 can support those from FAST, BLAST and FLASH algorithms. After a short presentation of the reconfigurable hardware concept and technology used in our massively parallel accelerator we present the A2R2 implementation. This parallel implementation outperforms any kind of previously published genetic data base scanning hardware or algorithms. We report up to 25 million nucleotides per scanning seconds as our best results.

Algorithms↗
PubMed1994

An improved system for exon recognition and gene modeling in human DNA sequences.

A new version of the GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992; Uberbacher et al., 1993), called GRAIL II, has recently been developed (Xu et al., 1994). GRAIL II is a hybrid AI system that supports a number of DNA sequence analysis tools including protein-coding region recognition, PolyA site and transcription promoter recognition, gene model construction, translation to protein, and DNA/protein database searching capabilities. This paper presents the core of GRAIL II, the coding exon recognition and gene model construction algorithms. The exon recognition algorithm recognizes coding exons by combining coding feature analysis and edge signal (acceptor/donor/translation-start sites) detection. Unlike the original GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992), this algorithm uses variable-length windows tailored to each potential exon candidate, making its performance almost exon length-independent. In this algorithm, the recognition process is divided into four steps. Initially a large number of possible coding exon candidates are generated. Then a rule-based prescreening algorithm eliminates the majority of the improbable candidates. As the kernel of the recognition algorithm, three neural networks are trained to evaluate the remaining candidates. The outputs of the neural networks are then divided into clusters of candidates, corresponding to presumed exons. The algorithm makes its final prediction by picking the best canadidate from each cluster. The gene construction algorithm (Xu, Mural and Uberbacher, 1994) uses a dynamic programming approach to build gene models by using as input the clusters predicted by the exon recognition algorithm. Extensive testing has been done on these two algorithms.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1995

Multiple alignment using hidden Markov models.

A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignments is evaluated on structural alignments of ten different protein families, and compared to the performance of other HMM training methods and the ClustalW program. Simulated annealing is better able to find near-global optima in the multiple alignment probability landscape than the other tested HMM training methods. Neither ClustalW nor simulated annealing produce consistently better alignments compared to each other. Examination of the specific cases in which ClustalW outperforms simulated annealing, and vice versa, provides insight into the strengths and weaknesses of current hidden Markov model approaches.

Algorithms↗
PubMed1995

Automatic RNA secondary structure determination with stochastic context-free grammars.

We have developed a method for predicting the common secondary structure of large RNA multiple alignments using only the information in the alignment. It uses a series of progressively more sensitive searches of the data in an iterative manner to discover regions of base pairing; the first pass examines the entire multiple alignment. The searching uses two methods to find base pairings. Mutual information is used to measure covariation between pairs of columns in the multiple alignment and a minimum length encoding method is used to detect column pairs with high potential to base pair. Dynamic programming is used to recover the optimal tree made up of the best potential base pairs and to create a stochastic context-free grammar. The information in the tree guides the next iteration of searching. The method is similar to the traditional comparative sequence analysis technique. The method correctly identifies most of the common secondary structure in 16S and 23S rRNA.

Algorithms↗
PubMed1995

3-D lookup: fast protein structure database searches at 90% reliability.

There are far fewer classes of three-dimensional protein folds than sequence families but the problem of detecting three-dimensional similarities is NP-complete. We present a novel heuristic for identifying 3-D similarities between a query structure and the database of known protein structures. Many methods for structure alignment use a bottom-up approach, identifying first local matches and then solving a combinatorial problem in building up larger clusters of matching substructures. Here, the top-down approach is to start with the global comparison and select a rough superimposition using a fast 3-D lookup of secondary structure motifs. The superimposition is then extended to an alignment of C alpha atoms by an iterative dynamic programming step. An all-against-all comparison of 385 representative proteins (150,000 pair comparisons) took 1 day of computer time on a single R8000 processor. In other words, one query structure is scanned against the database in a matter of minutes. The method is rated at 90% reliability at capturing statistically significant similarities. It is useful as a rapid preprocessor to a comprehensive protein structure database search system.

Amino Acid Sequence↗
PubMed1995

TOPITS: threading one-dimensional predictions into three-dimensional structures.

Homology modelling, currently, is the only theoretical tool which can successfully predict protein 3D structure. As 3D structure is conserved in sequence families, homology modelling allows to predict 3D structure for 20% of SWISSPROT. 20% of the proteins in PDB are remote homologues to another PDB protein. Threading techniques attempt to predict such remote homologues based on sequence information. Here, a new threading method is presented. First, for a list of PDB proteins, 3D structure was projected onto 1D strings of secondary structure and relative solvent accessibility. Then, secondary structure and accessibility were predicted by neural network systems (PHD). Finally, the predicted and observed 1D strings were aligned by dynamic programming. The resulting alignment was used to detect remote 3D homologues. Four results stand out. Firstly, even for an optimal prediction (assignment based on known structure), only about half the hits that ranked above a given threshold were correctly identified as remote homologues; only about 25% of the first hits were correct. Secondly, real predictions (PHD) were not much worse: about 20% of the first hits were correct. Thirdly, a simple filtering procedure improved prediction performance to about 30% correct first hits. The correct hit ranked among the first three for more than 23 out of 46 cases. Fourthly, the combination of the 1D threading and sequence alignments markedly improved the performance of the threading method TOPITS for some selected cases.

Amino Acid Sequence↗
PubMed1995

Automata-theoretic models of mutation and alignment.

Finite-state automata called transducers, which have both input and output, can be used to model simple mechanisms of biological mutation. We present a methodology whereby numerically-weighted versions of such specifications can be mechanically adapted to create string edit machines that are essentially equivalent to recurrence relations of the sort that characterize dynamic programming alignment algorithms. Based on this, we have developed a visual programming system for designing new alignment algorithms in a rapid-prototyping fashion.

Algorithms↗
PubMed1995

Discrete strategies of cancer post-treatment surveillance. Estimation and optimization problems.

We consider the cancer post-treatment surveillance to be represented by a discrete observation process with a non-zero false-negative rate. Using a simple stochastic model of cancer recurrence derived within the random minima framework, we obtain parametric estimates of both the time-to-recurrence distribution and the probability of false-negative diagnosis. Then assuming the false-negative rate known, we give a nonparametric maximum likelihood estimator for the tumor latency time distribution. When designing an optimal strategy of post-treatment surveillance, we proceed from the minimum of the expected delay in detecting tumor recurrence as a pertinent criterion of optimality. To solve this problem we give a dynamic programming algorithm. We illustrate the methods by analyzing data on breast cancer recurrence.

Algorithms↗
PubMed1996

A generalized hidden Markov model for the recognition of human genes in DNA.

We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp:@www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems.

Chromosomes, Human↗
PubMed1996

Compact encoding strategies for DNA sequence similarity search.

Determining whether two DNA sequences are similar is an essential component of DNA sequence analysis. Dynamic programming is the algorithm of choice if computational time is not the most important consideration. Heuristic search tools, such as BLAST, are computationally more efficient, but they may miss some of the sequence similarities (Altschul et al., 1990). These tools often use common k-tuples (words) between the two sequences to determine anchor points for the alignment, and spend most of their computational time extending the alignment beyond these anchor points. We discuss and provide a DNA sequence similarity search implementation (called SENSEI) that improves upon the performance of BLASTN by almost an order of magnitude for comparable sensitivity. This improvement is a result of using compactly encoded scoring tables for k-tuples, encoding bases with a single bit, filtering the sequence to remove the simple sequence repeats using XNUN, and masking the known species-specific repeats in the query sequence. To reduce memory requirements, especially for large genomic DNA query sequences, we recommend generating the neighborhood words from the target sequence at run-time, instead of generating them by preprocessing the query sequence.

Base Sequence↗
PubMed1997

Hierarchical protein structure superposition using both secondary structure and atomic representations.

The structural comparison of proteins has become increasingly important as a means to identify protein motifs and fold families. In this paper we present a new algorithm for the comparison of proteins based on a hierarchy of structural representations, from the secondary structure level to the atomic level. Our technique represents alpha-helices and beta-strands as vectors and uses a set of seven scoring functions to compare pairs of vectors from different proteins. The scores obtained are used in a dynamic programming algorithm that finds the best local alignment of the two sets of vectors. The second step in our algorithm is based on the atomic coordinates of the protein structures and improves the initial vector alignment by iteratively minimizing the RMSD between pairs of nearest atoms from the two proteins. We refine the final alignment by determining a core of well aligned atoms and minimizing the RMSD of this core. In a comparison of our method to Holm and Sander's DALI algorithm, our program was able to detect structural similarity at the same level as DALI. We also performed searches of a representative set of the Protein Data Bank (PDB) using our program and detected structurally similarity between several distantly related proteins.

Algorithms↗
PubMed1997

Integrating database homology in a probabilistic gene structure model.

We present an improved stochastic model of genes in DNA, and describe a method for integrating database homology into the probabilistic framework. A generalized hidden Markov model (GHMM) describes the grammar of a legal parse of a DNA sequence. Probabilities are estimated for gene features by using dynamic programming to combine information from multiple sensors. We show how matches to homologous sequences from a database can be integrated into the probability estimation by interpreting the likelihood of a sequence in terms of the bit-cost to encode a sequence given a homology match. We also demonstrate how homology matches in protein databases can be exploited to help identify splice sites. Our experiments show significant improvements in the sensitivity and specificity of gene structure identification when these new features are added to our gene-finding system, Genie. Experimental results in tests using a standard set of annotated genes showed that Genie identified 95% of coding nucleotides correctly with a specificity of 91%, and 77% of exons were identified exactly.

Algorithms↗