scholarly journals Probabilistic models of biological enzymatic polymerization

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244858
Author(s):  
Marshall Hampton ◽  
Miranda Galey ◽  
Clara Smoniewski ◽  
Sara L. Zimmer
Keyword(s):  
Trypanosoma Brucei ◽  
Probabilistic Models ◽  
Markov Models ◽  
Length Distribution ◽  
Nucleotide Composition ◽  
Gene Transcript ◽  
Human Pathogen ◽  
Sequencing Errors ◽  
Enzymatic Action ◽  
Nucleotide Addition

In this study, hierarchies of probabilistic models are evaluated for their ability to characterize the untemplated addition of adenine and uracil to the 3’ ends of mitochondrial mRNAs of the human pathogen Trypanosoma brucei, and for their generative abilities to reproduce populations of these untemplated adenine/uridine “tails”. We determined the most ideal Hidden Markov Models (HMMs) for this biological system. While our HMMs were not able to generatively reproduce the length distribution of the tails, they fared better in reproducing nucleotide composition aspects of the tail populations. The HMMs robustly identified distinct states of nucleotide addition that correlate to experimentally verified tail nucleotide composition differences. However they also identified a surprising subclass of tails among the ND1 gene transcript populations that is unexpected given the current idea of sequential enzymatic action of untemplated tail addition in this system. Therefore, these models can not only be utilized to reflect biological states that we already know about, they can also identify hypotheses to be experimentally tested. Finally, our HMMs supplied a way to correct a portion of the sequencing errors present in our data. Importantly, these models constitute rare simple pedagogical examples of applied bioinformatic HMMs, due to their binary emissions.

Nonlinear Multiresponse Parameter Estimation Using Simplex Optimization Method

2015 ◽  
Vol 1105 ◽  
pp. 299-304
Author(s):  
A. Al Saleh Mohammad ◽  
A. Yussuf Abdirahman
Keyword(s):  
Probabilistic Models ◽  
Length Distribution ◽  
Simulated Data ◽  
Optimization Method ◽  
Sequence Length ◽  
Distribution Data ◽  
Multiple Site ◽  
First Order ◽  
Site Type ◽  
Market Requirements

Polyolefin molecular architectures are designed according to customer needs and demands. Hence, it is essential to determine the catalytic behavior that gives the polymer the characteristics it needs to meet the market requirements. Today most of the industrial polyolefin production depends on multiple-site-type catalysts such as Ziegler-Natta catalysts. In this work a methodology to estimate parameters for polyolefin multiple-site-type catalysts was presented. The sequence length distribution data were simulated using Zeroth-order and First-order Markovian models. These simulated data were used to test the robustness of the optimization method. The optimization method used was able to retrieve and comprehend the proper probabilistic models and provide acceptable polymerization parameters estimates.

scholarly journals Mitochondrial DNAs provide insight into trypanosome phylogeny and molecular evolution

2020 ◽  
Author(s):  
Christopher Kay ◽  
Tom A Williams ◽  
Wendy Gibson
Keyword(s):  
Molecular Evolution ◽  
Trypanosoma Brucei ◽  
Rna Editing ◽  
Phylogenetic Trees ◽  
Nucleotide Composition ◽  
Sub Saharan Africa ◽  
African Trypanosomes ◽  
Animal Pathogens ◽  
Recent Emergence ◽  
The Impact

Abstract Background: Trypanosomes are single-celled eukaryotic parasites characterised by the unique biology of their mitochondrial DNA (mtDNA). African livestock trypanosomes impose a major burden on agriculture across sub-Saharan Africa, but are poorly understood compared to those that cause sleeping sickness and Chagas disease in humans. Here we explore the potential of trypanosome mtDNA to study the evolutionary history of trypanosomes and the molecular evolution of their mtDNAs.Results: We used long-read sequencing to completely assemble mtDNAs from four previously uncharacterized African trypanosomes, and leveraged these assemblies to scaffold and assemble a further 103 trypanosome mtDNAs from published short-read data. While synteny was largely conserved, there were repeated, independent losses of Complex I genes. Comparison of edited and non-edited genes revealed the impact of RNA editing on nucleotide composition, with non-edited genes approaching the limits of GC loss. African tsetse-transmitted trypanosomes showed high levels of RNA editing compared to other trypanosomes. Whole mtDNA coding regions were used to construct time-resolved phylogenetic trees, revealing deep divergence events among isolates of the pathogens Trypanosoma brucei and T. congolense .Conclusions: Our mtDNA data represents a new resource for experimental and evolutionary analyses of trypanosome phylogeny, molecular evolution and function. Molecular clock analyses yielded a timescale for trypanosome evolution congruent with major biogeographical events in Africa and revealed the recent emergence of Trypanosoma brucei gambiense and T. equiperdum , major human and animal pathogens.

Extending hidden Markov models to allow conditioning on previous observations

2018 ◽  
Vol 16 (05) ◽  
pp. 1850019 ◽  
Author(s):  
Ioannis A. Tamposis ◽  
Margarita C. Theodoropoulou ◽  
Konstantinos D. Tsirigos ◽  
Pantelis G. Bagos
Keyword(s):  
Hidden Markov Models ◽  
Probabilistic Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Simple Extension ◽  
Computational Molecular Biology ◽  
Current State ◽  
Observation Sequence ◽  
Hidden States

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%–8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

scholarly journals Lipidomic analysis of bloodstream and procyclic form Trypanosoma brucei

Parasitology ◽  
2010 ◽  
Vol 137 (9) ◽  
pp. 1357-1392 ◽  
Author(s):  
GREGORY S. RICHMOND ◽  
FEDERICA GIBELLINI ◽  
SIMON A. YOUNG ◽  
LOUISE MAJOR ◽  
HELEN DENTON ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Trypanosoma Brucei ◽  
De Novo ◽  
High Resolution Mass Spectrometry ◽  
Negative Ion ◽  
Tandem Mass ◽  
Human Pathogen ◽  
Electrospray Tandem Mass Spectrometry ◽  
Procyclic Form ◽  
Lipidomic Analysis

SUMMARYThe biological membranes of Trypanosoma brucei contain a complex array of phospholipids that are synthesized de novo from precursors obtained either directly from the host, or as catabolised endocytosed lipids. This paper describes the use of nanoflow electrospray tandem mass spectrometry and high resolution mass spectrometry in both positive and negative ion modes, allowing the identification of ~500 individual molecular phospholipids species from total lipid extracts of cultured bloodstream and procyclic form T. brucei. Various molecular species of all of the major subclasses of glycerophospholipids were identified including phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, and phosphatidylinositol as well as phosphatidic acid, phosphatidylglycerol and cardolipin, and the sphingolipids sphingomyelin, inositol phosphoceramide and ethanolamine phosphoceramide. The lipidomic data obtained in this study will aid future biochemical phenotyping of either genetically or chemically manipulated commonly used bloodstream and procyclic strains of Trypanosoma brucei. Hopefully this will allow a greater understanding of the bizarre world of lipids in this important human pathogen.

Probabilistic Self-Organizing Map for Clustering and Visualizing non-i.i.d Data

2015 ◽  
Vol 14 (02) ◽  
pp. 1550007 ◽  
Author(s):  
Mustapha Lebbah ◽  
Rakia Jaziri ◽  
Younès Bennani ◽  
Jean-Hugues Chenot
Keyword(s):  
Probabilistic Models ◽  
Markov Models ◽  
Function Optimization ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Topographic Map ◽  
New Paradigm ◽  
Real World Data ◽  
Self Organizing

We present a generative approach to train a new probabilistic self-organizing map (PrSOMS) for dependent and nonidentically distributed data sets. Our model defines a low-dimensional manifold allowing friendly visualizations. To yield the topology preserving maps, our model has the SOM like learning behavior with the advantages of probabilistic models. This new paradigm uses hidden Markov models (HMM) formalism and introduces relationships between the states. This allows us to take advantage of all the known classical views associated to topographic map. The objective function optimization has a clear interpretation, which allows us to propose expectation-maximization (EM) algorithm, based on the forward–backward algorithm, to train the model. We demonstrate our approach on two data sets: The real-world data issued from the "French National Audiovisual Institute" and handwriting data captured using a WACOM tablet.

scholarly journals Opposing Effects of Polyadenylation on the Stability of Edited and Unedited Mitochondrial RNAs in Trypanosoma brucei

2005 ◽  
Vol 25 (5) ◽  
pp. 1634-1644 ◽  
Author(s):  
Chia-Ying Kao ◽  
Laurie K. Read
Keyword(s):  
Trypanosoma Brucei ◽  
Rna Stability ◽  
Protein S ◽  
Rna Degradation ◽  
Nucleotide Composition ◽  
Rna Turnover ◽  
Degradation Reactions ◽  
The Stability ◽  
Opposing Effects

ABSTRACT Mitochondrial RNAs in Trypanosoma brucei undergo posttranscriptional RNA editing and polyadenylation. We previously showed that polyadenylation stimulates turnover of unedited RNAs. Here, we investigated the role of polyadenylation in decay of edited RPS12 RNA. In in vitro turnover assays, nonadenylated fully edited RNA degrades significantly faster than its unedited counterpart. Rapid turnover of nonadenylated RNA is facilitated by editing at just six editing sites. Surprisingly, in direct contrast to unedited RNA, turnover of fully edited RNA is dramatically slowed by addition of a poly(A)20 tail. The same minimal edited sequence that stimulates decay of nonadenylated RNA is sufficient to switch the poly(A) tail from a destabilizing to a stabilizing element. Both nucleotide composition and length of the 3′ extension are important for stabilization of edited RNA. Titration of poly(A) into RNA degradation reactions has no effect on turnover of polyadenylated edited RNA. These results suggest the presence of a protective protein(s) that simultaneously recognizes the poly(A) tail and small edited element and which blocks the action of a 3′-5′ exonuclease. This study provides the first evidence for opposing effects of polyadenylation on RNA stability within a single organelle and suggests a novel and unique regulation of RNA turnover in this system.

Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Thomas Plötz ◽  
Gernot A. Fink
Keyword(s):  
Probabilistic Models ◽  
Markov Models ◽  
Target Identification ◽  
Biochemical Properties ◽  
Local Context ◽  
Homology Detection ◽  
Profile Hmms ◽  
Remote Homology ◽  
Feature Based ◽  
Remote Homology Detection

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we propose feature based semi-continuous Profile HMMs. Based on a richer sequence representation consisting of features which capture the biochemical properties of residues in their local context, family specific semi-continuous models are estimated completely data-driven. Additionally, for substantially reducing the number of false predictions an explicit rejection model is estimated. Both the family specific semi-continuous Profile HMM and the non-target model are competitively evaluated. In the experimental evaluation of superfamily based screening of the SCOP database we demonstrate that semi-continuous Profile HMMs significantly outperform their discrete counterparts. Using the rejection model the number of false positive predictions could be reduced substantially which is an important prerequisite for target identification applications.

CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION

2004 ◽  
Vol 02 (02) ◽  
pp. 333-342 ◽  
Author(s):  
WEI-MOU ZHENG
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Markov Models ◽  
Secondary Structure Prediction ◽  
Length Distribution ◽  
Protein Secondary Structure ◽  
Coarse Graining ◽  
High Prediction ◽  
Duration Effect

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.

scholarly journals Modeling sequencing errors by combining Hidden Markov models

2003 ◽  
Vol 19 (Suppl 2) ◽  
pp. ii103-ii112 ◽  
Author(s):  
C. Lottaz ◽  
C. Iseli ◽  
C. V. Jongeneel ◽  
P. Bucher
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Sequencing Errors

Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model

2021 ◽  
Author(s):  
Azadeh Sadoughi ◽  
Mohammad Bagher Shamsollahi ◽  
Emad Fatemizadeh
Keyword(s):  
Sleep Apnea ◽  
Probabilistic Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Respiratory Disorder ◽  
Respiratory Events ◽  
Increased Risk ◽  
Respiratory Event

Abstract Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach. In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital’s database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep. Main results. The best F1 score of the event by event detection algorithm was between 0.22±0.16 and 0.70±0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2=0.91, p<0.0001). The best recall of RERA detection was achieved 0.45±0.27. Significance. The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.

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