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

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 77303

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Special Issue Editors

Prof. Dr. Ross Carlson

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Guest Editor

Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University, 306 Cobleigh Hall, Bozeman, MT 59717, USA
Interests: systems biology; microbial consortia; biofuels
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Herbert Sauro

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Guest Editor

Department of Bioengineering, University of Washington, Seattle, WA 98195-5061, USA
Interests: computer simulation; cancer therapy

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 submissions that pass pre-check are 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 2400 CHF (Swiss Francs). 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 (11 papers)

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Editorial

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3 pages, 132 KiB

Open AccessEditorial

Special Issue: Methods in Computational Biology

by Ross P. Carlson and Herbert M. Sauro

Processes 2019, 7(4), 205; https://doi.org/10.3390/pr7040205 - 11 Apr 2019

Viewed by 2169

Abstract

Biological systems are multiscale with respect to time and space, exist at the interface of biological and physical constraints, and their interactions with the environment are often nonlinear [...] Full article

(This article belongs to the Special Issue Methods in Computational Biology)

Research

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17 pages, 514 KiB

Open AccessFeature PaperArticle

Application of Parameter Optimization to Search for Oscillatory Mass-Action Networks Using Python

by Veronica L. Porubsky and Herbert M. Sauro

Processes 2019, 7(3), 163; https://doi.org/10.3390/pr7030163 - 18 Mar 2019

Cited by 4 | Viewed by 3850

Abstract

Biological systems can be described mathematically to model the dynamics of metabolic, protein, or gene-regulatory networks, but locating parameter regimes that induce a particular dynamic behavior can be challenging due to the vast parameter landscape, particularly in large models. In the current work, a Pythonic implementation of existing bifurcation objective functions, which reward systems that achieve a desired bifurcation behavior, is implemented to search for parameter regimes that permit oscillations or bistability. A differential evolution algorithm progressively approximates the specified bifurcation type while performing a global search of parameter space for a candidate with the best fitness. The user-friendly format facilitates integration with systems biology tools, as Python is a ubiquitous programming language. The bifurcation–evolution software is validated on published models from the BioModels Database and used to search populations of randomly-generated mass-action networks for oscillatory dynamics. Results of this search demonstrate the importance of reaction enrichment to provide flexibility and enable complex dynamic behaviors, and illustrate the role of negative feedback and time delays in generating oscillatory dynamics. Full article

(This article belongs to the Special Issue Methods in Computational Biology)

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20 pages, 2192 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Metabolic Modeling of Clostridium difficile Associated Dysbiosis of the Gut Microbiota

by Poonam Phalak and Michael A. Henson

Processes 2019, 7(2), 97; https://doi.org/10.3390/pr7020097 - 15 Feb 2019

Cited by 10 | Viewed by 5167

Abstract

Recent in vitro experiments have demonstrated the ability of the pathogen Clostridium difficile and commensal gut bacteria to form biofilms on surfaces, and biofilm development in vivo is likely. Various studies have reported that 3%–15% of healthy adults are asymptomatically colonized with C. difficile, with commensal species providing resistance against C. difficile pathogenic colonization. C. difficile infection (CDI) is observed at a higher rate in immunocompromised patients previously treated with broad spectrum antibiotics that disrupt the commensal microbiota and reduce competition for available nutrients, resulting in imbalance among commensal species and dysbiosis conducive to C. difficile propagation. To investigate the metabolic interactions of C. difficile with commensal species from the three dominant phyla in the human gut, we developed a multispecies biofilm model by combining genome-scale metabolic reconstructions of C. difficile, Bacteroides thetaiotaomicron from the phylum Bacteroidetes, Faecalibacterium prausnitzii from the phylum Firmicutes, and Escherichia coli from the phylum Proteobacteria. The biofilm model was used to identify gut nutrient conditions that resulted in C. difficile-associated dysbiosis characterized by large increases in C. difficile and E. coli abundances and large decreases in F. prausnitzii abundance. We tuned the model to produce species abundances and short-chain fatty acid levels consistent with available data for healthy individuals. The model predicted that experimentally-observed host-microbiota perturbations resulting in decreased carbohydrate/increased amino acid levels and/or increased primary bile acid levels would induce large increases in C. difficile abundance and decreases in F. prausnitzii abundance. By adding the experimentally-observed perturbation of increased host nitrate secretion, the model also was able to predict increased E. coli abundance associated with C. difficile dysbiosis. In addition to rationalizing known connections between nutrient levels and disease progression, the model generated hypotheses for future testing and has the capability to support the development of new treatment strategies for C. difficile gut infections. Full article

(This article belongs to the Special Issue Methods in Computational Biology)

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23 pages, 1613 KiB

Open AccessFeature PaperArticle

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

by Kerri-Ann Norton, Chang Gong, Samira Jamalian and Aleksander S. Popel

Processes 2019, 7(1), 37; https://doi.org/10.3390/pr7010037 - 13 Jan 2019

Cited by 101 | Viewed by 12782

Abstract

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics. Full article

(This article belongs to the Special Issue Methods in Computational Biology)

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16 pages, 4666 KiB

Open AccessArticle

Early Afterdepolarisations Induced by an Enhancement in the Calcium Current

by André H. Erhardt

Processes 2019, 7(1), 20; https://doi.org/10.3390/pr7010020 - 04 Jan 2019

Cited by 7 | Viewed by 3386

Abstract

Excitable biological cells, such as cardiac muscle cells, can exhibit complex patterns of oscillations such as spiking and bursting. Moreover, it is well known that an enhancement in calcium currents may yield certain kind of cardiac arrhythmia, so-called early afterdepolarisations (EADs). The presence of EADs strongly correlates with the onset of dangerous cardiac arrhythmia. In this paper we study mathematically and numerically the dynamics of a cardiac muscle cell with respect to the calcium current by investigating a simplistic system of differential equations. For the study of this phenomena, we use bifurcation theory, numerical bifurcation analysis, geometric singular perturbation theory and computational methods to investigate a nonlinear multiple time scales system. It will turn out that EADs related to an enhanced calcium current are canard–induced and that we have to combine these theories to derive a better understanding of the dynamics behind EADs. Moreover, a suitable time scale separation argument determines the important and sensitive system parameters which are related to the occurrence of EADs. Full article

(This article belongs to the Special Issue Methods in Computational Biology)

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34 pages, 1250 KiB

Open AccessFeature PaperArticle

An Integrated Mathematical Model of Cellular Cholesterol Biosynthesis and Lipoprotein Metabolism

by Frances Pool, Peter K. Sweby and Marcus J. Tindall

Processes 2018, 6(8), 134; https://doi.org/10.3390/pr6080134 - 18 Aug 2018

Cited by 11 | Viewed by 5209

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 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)

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16 pages, 1449 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Framework for the Development of Integrated and Computationally Feasible Models of Large-Scale Mammalian Cell Bioreactors

by Parham Farzan and Marianthi G. Ierapetritou

Processes 2018, 6(7), 82; https://doi.org/10.3390/pr6070082 - 29 Jun 2018

Cited by 7 | Viewed by 5872

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 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)

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14 pages, 2743 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

ADAR Mediated RNA Editing Modulates MicroRNA Targeting in Human Breast Cancer

by Justin T. Roberts, Dillon G. Patterson, Valeria M. King, Shivam V. Amin, Caroline J. Polska, Dominika Houserova, Aline Crucello, Emmaline C. Barnhill, Molly M. Miller, Timothy D. Sherman and Glen M. Borchert

Processes 2018, 6(5), 42; https://doi.org/10.3390/pr6050042 - 25 Apr 2018

Cited by 15 | Viewed by 6253

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 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)

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10 pages, 3684 KiB

Open AccessArticle

FluxVisualizer, a Software to Visualize Fluxes through Metabolic Networks

by Tim Daniel Rose and Jean-Pierre Mazat

Processes 2018, 6(5), 39; https://doi.org/10.3390/pr6050039 - 24 Apr 2018

Cited by 5 | Viewed by 6676

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 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)

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27 pages, 5371 KiB

Open AccessFeature PaperArticle

Measuring Cellular Biomass Composition for Computational Biology Applications

by Ashley E. Beck, Kristopher A. Hunt and Ross P. Carlson

Processes 2018, 6(5), 38; https://doi.org/10.3390/pr6050038 - 24 Apr 2018

Cited by 37 | Viewed by 18759

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 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)

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Review

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23 pages, 1248 KiB

Open AccessFeature PaperEditor’s ChoiceReview

The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena

by C. Anthony Hunt, Ahmet Erdemir, William W. Lytton, Feilim Mac Gabhann, Edward A. Sander, Mark K. Transtrum and Lealem Mulugeta

Processes 2018, 6(5), 56; https://doi.org/10.3390/pr6050056 - 14 May 2018

Cited by 15 | Viewed by 5997

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 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)

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