Protein-DNA computation by stochastic assembly cascade

The assembly of RecA on single-stranded DNA is measured and interpreted as a stochastic finite-state machine that is able to discriminate fine differences between sequences, a basic computational operation. RecA filaments efficiently scan DNA sequence through a cascade of random nucleation and disassembly events that is mechanistically similar to the dynamic instability of microtubules. This iterative cascade is a multistage kinetic proofreading process that amplifies minute differences, even a single base change. Our measurements suggest that this stochastic Turing-like machine can compute certain integral transforms.Comment: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC129313/ http://www.pnas.org/content/99/18/11589.abstrac

DNA ANALYSIS USING GRAMMATICAL INFERENCE

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA. An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm. Testing shows that the accuracy of inferred languages for components of DNA are consistently accurate. By using the proposed algorithm languages are inferred for coding DNA with average conditional probability over 80%. This reveals that languages for components of DNA can be inferred and are useful independent of the process that created them. These languages can then be analyzed or used for other tasks in computational biology. To illustrate potential applications of regular grammars for DNA components, an inferred language for exon sequences is applied as post processing to Hidden Markov exon prediction to reduce the number of wrong exons detected and improve the specificity of the model significantly

DNA Sequence Representation by Use of Statistical Finite Automata

This project defines and intends to solve the problem of representing information carried by DNA sequences in terms of amino acids, through application of the theory of finite automata. Sequences can be compared against each other to find existing patterns, if any, which may include important genetic information. Comparison can state whether the DNA sequences belong to the same, related or entirely different species in the ‘Tree of Life’ (phylogeny). This is achieved by using extended and statistical finite automata. In order to solve this problem, the concepts of automata and their extension, i.e. Alergia algorithm have been used. In this specific case, we have used the chemical property - polarity of amino acids to analyze the DNA sequences

PATTERN DISCOVERY IN DNA USING STOCHASTIC AUTOMATA

We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. A protein always starts with a start codon ATG that belongs to the group polar and ends with one of the stop codons that belongs to the group stop codon. After identifying sentences our method converts it into a symbolic representation of strings where each number represents the group to which an amino acid belongs to. We then generate a PTA tree and merge equivalent states to produce a Stochastic Finite Automata for a DNA. In addition to producing SFA, we apply secondary storage to handle huge DNA sequences. We also explain some concepts that are necessary to understand our paper

Pattern Recognition of DNA Sequences using Automata with application to Species Distinction

Darwin wasn\u27t just provocative in saying that we descend from the apes—he didn\u27t go far enough, we are apes in every way, from our long arms and tailless bodies to our habits and temperament. said Frans de Waal, a primate scientist at Emory University in Atlanta, Georgia. 1.3 million Species have been named and analyzed by scientists. This project focuses on capturing various nucleotide sequences of various species and determining the similarity and differences between them. Finite state automata have been used to accomplish this. The automata for a DNA genome is created using Alergia algorithm and is used as the foundation for comparing it to the other species DNA sequences

Compositionality, stochasticity and cooperativity in dynamic models of gene regulation

We present an approach for constructing dynamic models for the simulation of gene regulatory networks from simple computational elements. Each element is called a ``gene gate'' and defines an input/output-relationship corresponding to the binding and production of transcription factors. The proposed reaction kinetics of the gene gates can be mapped onto stochastic processes and the standard ode-description. While the ode-approach requires fixing the system's topology before its correct implementation, expressing them in stochastic pi-calculus leads to a fully compositional scheme: network elements become autonomous and only the input/output relationships fix their wiring. The modularity of our approach allows to pass easily from a basic first-level description to refined models which capture more details of the biological system. As an illustrative application we present the stochastic repressilator, an artificial cellular clock, which oscillates readily without any cooperative effects.Comment: 15 pages, 8 figures. Accepted by the HFSP journal (13/09/07

Formal methods for modeling and analysis of hybrid systems

A technique based on the use of a quantifier elimination decision procedure for real closed fields and simple theorem proving to construct a series of successively finer qualitative abstractions of hybrid automata is taught. The resulting abstractions are always discrete transition systems which can then be used by any traditional analysis tool. The constructed abstractions are conservative and can be used to establish safety properties of the original system. The technique works on linear and non-linear polynomial hybrid systems: the guards on discrete transitions and the continuous flows in all modes can be specified using arbitrary polynomial expressions over the continuous variables. An exemplar tool in the SAL environment built over the theorem prover PVS is detailed. The technique scales well to large and complex hybrid systems