The analysis of hospital infection data using hidden Markov models

Surveillance data for communicable nosocomial pathogens usually consist of short time series of low-numbered counts of infected patients. These often show overdispersion and autocorrelation. To date, almost all analyses of such data have ignored the communicable nature of the organisms and have used methods appropriate only for independent outcomes. Inferences that depend on such analyses cannot be considered reliable when patient-to-patient transmission is important. We propose a new method for analysing these data based on a mechanistic model of the epidemic process. Since important nosocomial pathogens are often carried asymptomatically with overt infection developing in only a proportion of patients, the epidemic process is usually only partially observed by routine surveillance data. We therefore develop a 'structured' hidden Markov model where the underlying Markov chain is generated by a simple transmission model. We apply both structured and standard (unstructured) hidden Markov models to time series for three important pathogens. We find that both methods can offer marked improvements over currently used approaches when nosocomial spread is important. Compared to the standard hidden Markov model, the new approach is more parsimonious, is more biologically plausible, and allows key epidemiological parameters to be estimated.     

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.     

Prediction of Translation Initiation Sites on the Genome of Synechocystis sp. Strain PCC6803 by Hidden Markov Model

We developed a computer program, GeneHackerTL, which predictsthe most probable translation initiation site for a given nucleotidesequence. The program requires that information be extractedfrom the nucleotide sequence data surrounding the translationinitiation sites according to the framework of the Hidden MarkovModel. Since the translation initiation sites of 72 highly abundantproteins have already been assigned on the genome of Synechocystissp. strain PCC6803 by amino-terminal analysis, we extractednecessary information for GeneHackerTL from the nucleotide sequencedata. The prediction rate of the GeneHackerTL for these proteinswas estimated to be 86.1%. We then used GeneHackerTL for predictionof the translation initiation sites of 24 other proteins, ofwhich the initiation sites were not assigned experimentally,because of the lack of a potential initiation codon at the amino-terminalposition. For 20 out of the 24 proteins, the initiation siteswere predicted in the upstream of their amino-terminal positions.According to this assignment, the processed regions representa typical feature of signal peptides. We could also predictmultiple translation initiation sites for a particular genefor which at least two initiation sites were experimentallydetected. This program would be e.ective for the predictionof translation initiationsites of other proteins, not only inthis species but also in other prokaryotes as well.     

Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations

The underlying molecular mechanisms of metabolic and genetic regulations are computationally identical and can be described by a finite state Markov process. We establish a common computational model for both regulations based on the stationary distribution of the Markov process with the aim of establishing a unified, quantitative model of general biological regulations. Various existing results regarding intracellular regulations are derived including the classical Michaelis-Menten equation and its generalization to more complex allosteric enzymes in a systematic way. The notion of probability flow is introduced to distinguish the equilibrium stationary distribution from the non-equilibrium one; it plays a crucial role in the analysis of stationary state equations. A graphical criterion to guarantee the existence of an equilibrium stationary distribution is derived, which turns out to be identical to the classical Wegscheider condition. Simple graphical methods to compute the equilibrium and non-equilibrium stationary distributions are derived based crucially on the probability flow, which dramatically simplifies the classical methods still used in enzymology.