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Computation, Volume 2, Issue 4 (December 2014) – 6 articles , Pages 131-257

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302 KiB

Open AccessArticle

SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

by Alexander Dörr, Roland Keller, Andreas Zell and Andreas Dräger

Computation 2014, 2(4), 246-257; https://doi.org/10.3390/computation2040246 - 18 Dec 2014

Cited by 10 | Viewed by 9248

Abstract

The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values. Full article

(This article belongs to the Section Computational Biology)

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2430 KiB

Open AccessArticle

Computational and Statistical Analyses of Insertional Polymorphic Endogenous Retroviruses in a Non-Model Organism

by Le Bao, Daniel Elleder, Raunaq Malhotra, Michael DeGiorgio, Theodora Maravegias, Lindsay Horvath, Laura Carrel, Colin Gillin, Tomáš Hron, Helena Fábryová, David R. Hunter and Mary Poss

Computation 2014, 2(4), 221-245; https://doi.org/10.3390/computation2040221 - 28 Nov 2014

Cited by 5 | Viewed by 9992

Abstract

Endogenous retroviruses (ERVs) are a class of transposable elements found in all vertebrate genomes that contribute substantially to genomic functional and structural diversity. A host species acquires an ERV when an exogenous retrovirus infects a germ cell of an individual and becomes part of the genome inherited by viable progeny. ERVs that colonized ancestral lineages are fixed in contemporary species. However, in some extant species, ERV colonization is ongoing, which results in variation in ERV frequency in the population. To study the consequences of ERV colonization of a host genome, methods are needed to assign each ERV to a location in a species’ genome and determine which individuals have acquired each ERV by descent. Because well annotated reference genomes are not widely available for all species, de novo clustering approaches provide an alternative to reference mapping that are insensitive to differences between query and reference and that are amenable to mobile element studies in both model and non-model organisms. However, there is substantial uncertainty in both identifying ERV genomic position and assigning each unique ERV integration site to individuals in a population. We present an analysis suitable for detecting ERV integration sites in species without the need for a reference genome. Our approach is based on improved de novo clustering methods and statistical models that take the uncertainty of assignment into account and yield a probability matrix of shared ERV integration sites among individuals. We demonstrate that polymorphic integrations of a recently identified endogenous retrovirus in deer reflect contemporary relationships among individuals and populations. Full article

(This article belongs to the Special Issue Genomes and Evolution: Computational Approaches)

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267 KiB

Open AccessReview

Computation of the Likelihood in Biallelic Diffusion Models Using Orthogonal Polynomials

by Claus Vogl

Computation 2014, 2(4), 199-220; https://doi.org/10.3390/computation2040199 - 14 Nov 2014

Cited by 3 | Viewed by 4654

Abstract

In population genetics, parameters describing forces such as mutation, migration and drift are generally inferred from molecular data. Lately, approximate methods based on simulations and summary statistics have been widely applied for such inference, even though these methods waste information. In contrast, probabilistic methods of inference can be shown to be optimal, if their assumptions are met. In genomic regions where recombination rates are high relative to mutation rates, polymorphic nucleotide sites can be assumed to evolve independently from each other. The distribution of allele frequencies at a large number of such sites has been called “allele-frequency spectrum” or “site-frequency spectrum” (SFS). Conditional on the allelic proportions, the likelihoods of such data can be modeled as binomial. A simple model representing the evolution of allelic proportions is the biallelic mutation-drift or mutation-directional selection-drift diffusion model. With series of orthogonal polynomials, specifically Jacobi and Gegenbauer polynomials, or the related spheroidal wave function, the diffusion equations can be solved efficiently. In the neutral case, the product of the binomial likelihoods with the sum of such polynomials leads to finite series of polynomials, i.e., relatively simple equations, from which the exact likelihoods can be calculated. In this article, the use of orthogonal polynomials for inferring population genetic parameters is investigated. Full article

(This article belongs to the Special Issue Genomes and Evolution: Computational Approaches)

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835 KiB

Open AccessArticle

Incongruencies in Vaccinia Virus Phylogenetic Trees

by Chad Smithson, Samantha Kampman, Benjamin M. Hetman and Chris Upton

Computation 2014, 2(4), 182-198; https://doi.org/10.3390/computation2040182 - 14 Oct 2014

Cited by 13 | Viewed by 9736

Abstract

Over the years, as more complete poxvirus genomes have been sequenced, phylogenetic studies of these viruses have become more prevalent. In general, the results show similar relationships between the poxvirus species; however, some inconsistencies are notable. Previous analyses of the viral genomes contained within the vaccinia virus (VACV)-Dryvax vaccine revealed that their phylogenetic relationships were sometimes clouded by low bootstrapping confidence. To analyze the VACV-Dryvax genomes in detail, a new tool-set was developed and integrated into the Base-By-Base bioinformatics software package. Analyses showed that fewer unique positions were present in each VACV-Dryvax genome than expected. A series of patterns, each containing several single nucleotide polymorphisms (SNPs) were identified that were counter to the results of the phylogenetic analysis. The VACV genomes were found to contain short DNA sequence blocks that matched more distantly related clades. Additionally, similar non-conforming SNP patterns were observed in (1) the variola virus clade; (2) some cowpox clades; and (3) VACV-CVA, the direct ancestor of VACV-MVA. Thus, traces of past recombination events are common in the various orthopoxvirus clades, including those associated with smallpox and cowpox viruses. Full article

(This article belongs to the Special Issue Genomes and Evolution: Computational Approaches)

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1775 KiB

Open AccessArticle

Multiscale Modeling of the Early CD8 T-Cell Immune Response in Lymph Nodes: An Integrative Study

by Sotiris A. Prokopiou, Loic Barbarroux, Samuel Bernard, Julien Mafille, Yann Leverrier, Christophe Arpin, Jacqueline Marvel, Olivier Gandrillon and Fabien Crauste

Computation 2014, 2(4), 159-181; https://doi.org/10.3390/computation2040159 - 29 Sep 2014

Cited by 25 | Viewed by 9596

Abstract

CD8 T-cells are critical in controlling infection by intracellular pathogens. Upon encountering antigen presenting cells, T-cell receptor activation promotes the differentiation of naïve CD8 T-cells into strongly proliferating activated and effector stages. We propose a 2D-multiscale computational model to study the maturation of CD8 T-cells in a lymph node controlled by their molecular profile. A novel molecular pathway is presented and converted into an ordinary differential equation model, coupled with a cellular Potts model to describe cell-cell interactions. Key molecular players such as activated IL2 receptor and Tbet levels control the differentiation from naïve into activated and effector stages, respectively, while caspases and Fas-Fas ligand interactions control cell apoptosis. Coupling this molecular model to the cellular scale successfully reproduces qualitatively the evolution of total CD8 T-cell counts observed in mice lymph node, between Day 3 and 5.5 post-infection. Furthermore, this model allows us to make testable predictions of the evolution of the different CD8 T-cell stages. Full article

(This article belongs to the Special Issue Computational Studies of Immune System Function)

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1143 KiB

Open AccessReview

Computational Models of the NF-_KB Signalling Pathway

by Richard A. Williams, Jon Timmis and Eva E. Qwarnstrom

Computation 2014, 2(4), 131-158; https://doi.org/10.3390/computation2040131 - 29 Sep 2014

Cited by 50 | Viewed by 19551

Abstract

In this review article, we discuss the current state of computational modelling of the nuclear factor-kappa B (NF-_ΚB) signalling pathway. NF-_ΚB is a transcription factor, which is ubiquitous within cells and controls a number of immune responses, including inflammation and apoptosis. The NF-_ΚB signalling pathway is tightly regulated, commencing with activation at the cell membrane, signal transduction through various components within the cytoplasm, translocation of NF-_ΚB into the nucleus and, finally, the transcription of various genes relating to the innate and adaptive immune responses. There have been a number of computational (mathematical) models developed of the signalling pathway over the past decade. This review describes how these approaches have helped advance our understanding of NF-_ΚB control. Full article

(This article belongs to the Special Issue Computational Studies of Immune System Function)

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