Computation

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Open AccessReview

Evolutionary Dynamics in Gene Networks and Inference Algorithms

by Daniel Aguilar-Hidalgo, María C. Lemos and Antonio Córdoba

Computation 2015, 3(1), 99-113; https://doi.org/10.3390/computation3010099 - 13 Mar 2015

Cited by 6 | Viewed by 5852

Dynamical interactions among sets of genes (and their products) regulate developmental processes and some dynamical diseases, like cancer. Gene regulatory networks (GRNs) are directed networks that define interactions (links) among different genes/proteins involved in such processes. Genetic regulation can be modified during the time course of the process, which may imply changes in the nodes activity that leads the system from a specific state to a different one at a later time (dynamics). How the GRN modifies its topology, to properly drive a developmental process, and how this regulation was acquired across evolution are questions that the evolutionary dynamics of gene networks tackles. In the present work we review important methodology in the field and highlight the combination of these methods with evolutionary algorithms. In recent years, this combination has become a powerful tool to fit models with the increasingly available experimental data. Full article

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

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1389 KiB

Open AccessReview

Use of CMEIAS Image Analysis Software to Accurately Compute Attributes of Cell Size, Morphology, Spatial Aggregation and Color Segmentation that Signify in Situ Ecophysiological Adaptations in Microbial Biofilm Communities

by Frank B. Dazzo and Brighid C. Niccum

Computation 2015, 3(1), 72-98; https://doi.org/10.3390/computation3010072 - 9 Mar 2015

Cited by 20 | Viewed by 7170

Abstract

In this review, we describe computational features of computer-assisted microscopy that are unique to the Center for Microbial Ecology Image Analysis System (CMEIAS) software, and examples illustrating how they can be used to gain ecophysiological insights into microbial adaptations occurring at micrometer spatial scales directly relevant to individual cells occupying their ecological niches in situ. These features include algorithms that accurately measure (1) microbial cell length relevant to avoidance of protozoan bacteriovory; (2) microbial biovolume body mass relevant to allometric scaling and local apportionment of growth-supporting nutrient resources; (3) pattern recognition rules for morphotype classification of diverse microbial communities relevant to their enhanced fitness for success in the particular habitat; (4) spatial patterns of coaggregation that reveal the local intensity of cooperative vs. competitive adaptations in colonization behavior relevant to microbial biofilm ecology; and (5) object segmentation of complex color images to differentiate target microbes reporting successful cell-cell communication. These unique computational features contribute to the CMEIAS mission of developing accurate and freely accessible tools of image bioinformatics that strengthen microscopy-based approaches for understanding microbial ecology at single-cell resolution. Full article

(This article belongs to the Section Computational Biology)

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2190 KiB

Open AccessArticle

Visual Simulation of Soil-Microbial System Using GPGPU Technology

by Ruth E. Falconer and Alasdair N. Houston

Computation 2015, 3(1), 58-71; https://doi.org/10.3390/computation3010058 - 27 Feb 2015

Cited by 8 | Viewed by 5607

Abstract

General Purpose (use of) Graphics Processing Units (GPGPU) is a promising technology for simulation upscaling; in particular for bottom–up modelling approaches seeking to translate micro-scale system processes to macro-scale properties. Many existing simulations of soil ecosystems do not recover the emergent system scale properties and this may be a consequence of “missing” information at finer scales. Interpretation of model output can be challenging and we advocate the “built-in” visual simulation afforded by GPGPU implementations. We apply this GPGPU approach to a reaction–diffusion soil ecosystem model with the intent of linking micro (micron) and core (cm) spatial scales to investigate how microbes respond to changing environments and the consequences on soil respiration. The performance is evaluated in terms of computational speed up, spatial upscaling and visual feedback. We conclude that a GPGPU approach can significantly improve computational efficiency and offers the potential added benefit of visual immediacy. For massive spatial domains distribution over GPU devices may still be required. Full article

(This article belongs to the Special Issue Capturing Adaptive Processes in Computational Models of Ecosystems and the Earth System)

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Open AccessArticle

A Review of Two Multiscale Methods for the Simulation of Macromolecular Assemblies: Multiscale Perturbation and Multiscale Factorization

by Stephen Pankavich and Peter Ortoleva

Computation 2015, 3(1), 29-57; https://doi.org/10.3390/computation3010029 - 5 Feb 2015

Cited by 1 | Viewed by 5155

Abstract

Many mesoscopic N-atom systems derive their structural and dynamical properties from processes coupled across multiple scales in space and time. That is, they simultaneously deform or display collective behaviors, while experiencing atomic scale vibrations and collisions. Due to the large number of atoms involved and the need to simulate over long time periods of biological interest, traditional computational tools, like molecular dynamics, are often infeasible for such systems. Hence, in the current review article, we present and discuss two recent multiscale methods, stemming from the N-atom formulation and an underlying scale separation, that can be used to study such systems in a friction-dominated regime: multiscale perturbation theory and multiscale factorization. These novel analytic foundations provide a self-consistent approach to yield accurate and feasible long-time simulations with atomic detail for a variety of multiscale phenomena, such as viral structural transitions and macromolecular self-assembly. As such, the accuracy and efficiency of the associated algorithms are demonstrated for a few representative biological systems, including satellite tobacco mosaic virus (STMV) and lactoferrin. Full article

(This article belongs to the Special Issue Multiscale Modeling and Simulation in Computational Biology)

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Open AccessReview

Computational Studies of the Intestinal Host-Microbiota Interactome

by Scott Christley, Chase Cockrell and Gary An

Computation 2015, 3(1), 2-28; https://doi.org/10.3390/computation3010002 - 14 Jan 2015

Cited by 6 | Viewed by 8835

Abstract

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health. Full article

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

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Open AccessEditorial

Acknowledgement to Reviewers of Computation in 2014

by Computation Editorial Office

Computation 2015, 3(1), 1; https://doi.org/10.3390/computation3010001 - 9 Jan 2015

Viewed by 3114

Abstract

The editors of Computation would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article

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Computation, Volume 3, Issue 1 (March 2015) – 6 articles , Pages 1-113

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