Special Issue "Methods in Computational Biology"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Systems".

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Ross Carlson

Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Thermal Biology Institute, 306 Cobleigh Hall, Montana State University Bozeman, MT 59717, USA
Website | E-Mail
Interests: The Carlson group studies design principles of biological systems using a combination of in silico and experimental analyses. Focus areas includes resource allocation theory in monocultures and consortia as well as during planktonic and biofilm modes of growth. This theory is broadly applicable to biological organization and is being studied in systems relevant to ecological, medical and bioprocess applications
Guest Editor
Dr. Herbert Sauro

Department of Bioengineering, University of Washington, Seattle, USA
Website | E-Mail
Interests: My research group is developing the next generation of high performance software that can simulate human disease states such as cancer or heart disease. In the future, we anticipate that doctors will have detailed computer simulations of their patients enabling doctors to try out therapies on the patient simulation first before actually treating the patient

Special Issue Information

Dear Colleagues,

Rapid development of omics technologies has created large datasets and a need for computational biology approaches that can leverage this data to extract and test biological theory. This is a major challenge for the life sciences including the medical, environmental, and bioprocess fields.

The focus of this special issue is methods in computational biology which can extract and test theory using large datasets. A primary goal of this issue is the communication of computational biology methods with enough detail to permit reproduction of results. Topics of interest span the gamut of fundamental and applied biology and can include analyses of medical, environmental and bioprocess systems. Modeling approaches of interest include, but are not limited to, flux balance analysis, elementary flux mode analysis, agent-based modeling as well as other dynamic modeling approaches. Papers combining experimental and computational studies are highly encouraged as are manuscripts that propose standards for model writing, storage and distribution. This issue aims to integrate highly interdisciplinary researchers such as biologists, computer scientists, engineers and mathematicians who focus on advances in biological systems analysis.

The issue is coordinated with the Metabolic Pathway Analysis 2017 conference held in Bozeman MT and select IMAG MultiScale Modeling (MSM) working groups.

Prof. Dr. Ross Carlson
Dr. Herbert Sauro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2018 an APC of 1100 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational biology
  • systems biology
  • stoichiometric modeling
  • synthetic biology
  • synthetic ecology
  • omics analysis
  • computational methods

Published Papers (6 papers)

View options order results:
result details:
Displaying articles 1-6
Export citation of selected articles as:

Research

Jump to: Review

Open AccessFeature PaperArticle An Integrated Mathematical Model of Cellular Cholesterol Biosynthesis and Lipoprotein Metabolism
Processes 2018, 6(8), 134; https://doi.org/10.3390/pr6080134
Received: 29 June 2018 / Revised: 8 August 2018 / Accepted: 10 August 2018 / Published: 18 August 2018
PDF Full-text (1250 KB) | HTML Full-text | XML Full-text
Abstract
Cholesterol regulation is an important aspect of human health. In this work we bring together and extend two recent mathematical models describing cholesterol biosynthesis and lipoprotein endocytosis to create an integrated model of lipoprotein metabolism in the context of a single hepatocyte. The
[...] Read more.
Cholesterol regulation is an important aspect of human health. In this work we bring together and extend two recent mathematical models describing cholesterol biosynthesis and lipoprotein endocytosis to create an integrated model of lipoprotein metabolism in the context of a single hepatocyte. The integrated model includes a description of low density lipoprotein (LDL) receptor and cholesterol synthesis, delipidation of very low density lipoproteins (VLDLs) to LDLs and subsequent lipoprotein endocytosis. Model analysis shows that cholesterol biosynthesis produces the majority of intracellular cholesterol. The availability of free receptors does not greatly effect the concentration of intracellular cholesterol, but has a detrimental effect on extracellular VLDL and LDL levels. We test our model by considering its ability to reproduce the known biology of Familial Hypercholesterolaemia and statin therapy. In each case the model reproduces the known biological behaviour. Quantitative differences in response to statin therapy are discussed in the context of the need to extend the work to a more in vivo setting via the incorporation of more dietary lipoprotein related processes and the need for further testing and parameterisation of in silico models of lipoprotein metabolism. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Figure 1

Open AccessFeature PaperArticle A Framework for the Development of Integrated and Computationally Feasible Models of Large-Scale Mammalian Cell Bioreactors
Processes 2018, 6(7), 82; https://doi.org/10.3390/pr6070082
Received: 5 May 2018 / Revised: 11 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
PDF Full-text (1449 KB) | HTML Full-text | XML Full-text
Abstract
Industrialization of bioreactors has been achieved by applying several core concepts of science and engineering. Modeling has deepened the understanding of biological and physical phenomena. In this paper, the state of existing cell culture models is summarized. A framework for development of dynamic
[...] Read more.
Industrialization of bioreactors has been achieved by applying several core concepts of science and engineering. Modeling has deepened the understanding of biological and physical phenomena. In this paper, the state of existing cell culture models is summarized. A framework for development of dynamic and computationally feasible models that capture the interactions of hydrodynamics and cellular activities is proposed. Operating conditions are described by impeller rotation speed, gas sparging flowrate, and liquid fill level. A set of admissible operating states is defined over discretized process parameters. The burden on a dynamic solver is reduced by assuming hydrodynamics at its fully developed state and implementation of compartmental modeling. A change in the conditions of operation is followed by hydrodynamics switching instantaneously to the steady state that would be reached under new conditions. Finally, coupling the model with optimization solvers leads to improvements in operation. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Graphical abstract

Open AccessFeature PaperArticle ADAR Mediated RNA Editing Modulates MicroRNA Targeting in Human Breast Cancer
Processes 2018, 6(5), 42; https://doi.org/10.3390/pr6050042
Received: 6 April 2018 / Revised: 19 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
PDF Full-text (2743 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
RNA editing by RNA specific adenosine deaminase acting on RNA (ADAR) is increasingly being found to alter microRNA (miRNA) regulation. Editing of miRNA transcripts can affect their processing, as well as which messenger RNAs (mRNAs) they target. Further, editing of target mRNAs can
[...] Read more.
RNA editing by RNA specific adenosine deaminase acting on RNA (ADAR) is increasingly being found to alter microRNA (miRNA) regulation. Editing of miRNA transcripts can affect their processing, as well as which messenger RNAs (mRNAs) they target. Further, editing of target mRNAs can also affect their complementarity to miRNAs. Notably, ADAR editing is often increased in malignancy with the effect of these RNA changes being largely unclear. In addition, numerous reports have now identified an array of miRNAs that directly contribute to various malignancies although the majority of their targets remain largely undefined. Here we propose that modulating the targets of miRNAs via mRNA editing is a frequent occurrence in cancer and an underappreciated participant in pathology. In order to more accurately characterize the relationship between these two regulatory processes, this study examined RNA editing events within mRNA sequences of two breast cancer cell lines (MCF-7 and MDA-MB-231) and determined whether or not these edits could modulate miRNA associations. Computational analyses of RNA-Seq data from these two cell lines identified over 50,000 recurrent editing sites within human mRNAs, and many of these were located in 3′ untranslated regions (UTRs). When these locations were screened against the list of currently-annotated miRNAs we discovered that editing caused a subset (~9%) to have significant alterations to mRNA complementarity. One miRNA in particular, miR-140-3p, is known to be misexpressed in many breast cancers, and we found that mRNA editing allowed this miRNA to directly target the apoptosis inducing gene DFFA in MCF-7, but not in MDA-MB-231 cells. As these two cell lines are known to have distinct characteristics in terms of morphology, invasiveness and physiological responses, we hypothesized that the differential RNA editing of DFFA in these two cell lines could contribute to their phenotypic differences. Indeed, we confirmed through western blotting that inhibiting miR-140-3p increases expression of the DFFA protein product in MCF-7, but not MDA-MB-231, and further that inhibition of miR-140-3p also increases cellular growth in MCF-7, but not MDA-MB-231. Broadly, these results suggest that the creation of miRNA targets may be an underappreciated function of ADAR and may help further elucidate the role of RNA editing in tumor pathogenicity. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Figure 1

Open AccessArticle FluxVisualizer, a Software to Visualize Fluxes through Metabolic Networks
Processes 2018, 6(5), 39; https://doi.org/10.3390/pr6050039
Received: 2 March 2018 / Revised: 10 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018
PDF Full-text (3684 KB) | HTML Full-text | XML Full-text
Abstract
FluxVisualizer (Version 1.0, 2017, freely available at https://fluxvisualizer.ibgc.cnrs.fr) is a software to visualize fluxes values on a scalable vector graphic (SVG) representation of a metabolic network by colouring or increasing the width of reaction arrows of the SVG file. FluxVisualizer does not aim
[...] Read more.
FluxVisualizer (Version 1.0, 2017, freely available at https://fluxvisualizer.ibgc.cnrs.fr) is a software to visualize fluxes values on a scalable vector graphic (SVG) representation of a metabolic network by colouring or increasing the width of reaction arrows of the SVG file. FluxVisualizer does not aim to draw metabolic networks but to use a customer’s SVG file allowing him to exploit his representation standards with a minimum of constraints. FluxVisualizer is especially suitable for small to medium size metabolic networks, where a visual representation of the fluxes makes sense. The flux distribution can either be an elementary flux mode (EFM), a flux balance analysis (FBA) result or any other flux distribution. It allows the automatic visualization of a series of pathways of the same network as is needed for a set of EFMs. The software is coded in python3 and provides a graphical user interface (GUI) and an application programming interface (API). All functionalities of the program can be used from the API and the GUI and allows advanced users to add their own functionalities. The software is able to work with various formats of flux distributions (Metatool, CellNetAnalyzer, COPASI and FAME export files) as well as with Excel files. This simple software can save a lot of time when evaluating fluxes simulations on a metabolic network. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Graphical abstract

Open AccessFeature PaperArticle Measuring Cellular Biomass Composition for Computational Biology Applications
Processes 2018, 6(5), 38; https://doi.org/10.3390/pr6050038
Received: 27 January 2018 / Revised: 6 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018
PDF Full-text (5371 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Computational representations of metabolism are increasingly common in medical, environmental, and bioprocess applications. Cellular growth is often an important output of computational biology analyses, and therefore, accurate measurement of biomass constituents is critical for relevant model predictions. There is a distinct lack of
[...] Read more.
Computational representations of metabolism are increasingly common in medical, environmental, and bioprocess applications. Cellular growth is often an important output of computational biology analyses, and therefore, accurate measurement of biomass constituents is critical for relevant model predictions. There is a distinct lack of detailed macromolecular measurement protocols, including comparisons to alternative assays and methodologies, as well as tools to convert the experimental data into biochemical reactions for computational biology applications. Herein is compiled a concise literature review regarding methods for five major cellular macromolecules (carbohydrate, DNA, lipid, protein, and RNA) with a step-by-step protocol for a select method provided for each macromolecule. Additionally, each method was tested on three different bacterial species, and recommendations for troubleshooting and testing new species are given. The macromolecular composition measurements were used to construct biomass synthesis reactions with appropriate quality control metrics such as elemental balancing for common computational biology methods, including flux balance analysis and elementary flux mode analysis. Finally, it was demonstrated that biomass composition can substantially affect fundamental model predictions. The effects of biomass composition on in silico predictions were quantified here for biomass yield on electron donor, biomass yield on electron acceptor, biomass yield on nitrogen, and biomass degree of reduction, as well as the calculation of growth associated maintenance energy; these parameters varied up to 7%, 70%, 35%, 12%, and 40%, respectively, between the reference biomass composition and ten test biomass compositions. The current work furthers the computational biology community by reviewing literature regarding a variety of common analytical measurements, developing detailed procedures, testing the methods in the laboratory, and applying the results to metabolic models, all in one publicly available resource. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Graphical abstract

Review

Jump to: Research

Open AccessFeature PaperReview The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena
Processes 2018, 6(5), 56; https://doi.org/10.3390/pr6050056
Received: 15 April 2018 / Revised: 5 May 2018 / Accepted: 6 May 2018 / Published: 14 May 2018
Cited by 1 | PDF Full-text (1248 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make
[...] Read more.
Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make reproduction challenging, even problematic. To encourage improved semantic and methodological clarity, we describe the spectrum of Mechanism-oriented Models being used to develop explanations of biological phenomena. We cluster explanations of phenomena into three broad groups. We then expand them into seven workflow-related model types having distinguishable features. We name each type and illustrate with examples drawn from the literature. These model types may contribute to the foundation of an ontology of mechanism-based biomedical simulation research. We show that the different model types manifest and exert their scientific usefulness by enhancing and extending different forms and degrees of explanation. The process starts with knowledge about the phenomenon and continues with explanatory and mathematical descriptions. Those descriptions are transformed into software and used to perform experimental explorations by running and examining simulation output. The credibility of inferences is thus linked to having easy access to the scientific and technical provenance from each workflow stage. Full article
(This article belongs to the Special Issue Methods in Computational Biology)
Figures

Graphical abstract

Back to Top