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Liguori-Bills N, Blinov ML. bnglViz: online visualization of rule-based models. Bioinformatics 2024; 40:btae351. [PMID: 38814806 PMCID: PMC11176710 DOI: 10.1093/bioinformatics/btae351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 05/01/2024] [Accepted: 05/29/2024] [Indexed: 06/01/2024] Open
Abstract
MOTIVATION Rule-based modeling is a powerful method to describe and simulate interactions among multi-site molecules and multi-molecular species, accounting for the internal connectivity of molecules in chemical species. This modeling technique is implemented in BioNetGen software that is used by various tools and software frameworks, such as BioNetGen stand-alone software, NFSim simulation engine, Virtual Cell simulation and modeling framework, SmolDyn and PySB software tools. These tools exchange models using BioNetGen scripting language (BNGL). Until now, there was no online visualization of such rule-based models. Modelers and researchers reading the manuscripts describing rule-based models had to learn BNGL scripting or master one of these tools to understand the models. RESULTS Here, we introduce bnglViz, an online platform for visualizing BNGL files as graphical cartoons, empowering researchers to grasp the nuances of rule-based models swiftly and efficiently, and making the exploration of complex biological systems more accessible than ever before. The produced visualizations can be used as supplemental figures in publications or as a way to annotate BNGL models on web repositories. AVAILABILITY AND IMPLEMENTATION Available at https://bnglviz.github.io/.
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Affiliation(s)
- Noah Liguori-Bills
- Marine Earth and Atmospheric Sciences Department, North Carolina State University, Raleigh, NC 27695, United States
| | - Michael L Blinov
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, United States
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Dimitrova ES, Knapp AC, Stigler B, Stillman ME. Cyclone: open-source package for simulation and analysis of finite dynamical systems. Bioinformatics 2023; 39:btad634. [PMID: 37856334 PMCID: PMC10634519 DOI: 10.1093/bioinformatics/btad634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 08/11/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023] Open
Abstract
MOTIVATION While there are software packages that analyze Boolean, ternary, or other multi-state models, none compute the complete state space of function-based models over any finite set. Results: We propose Cyclone, a simple light-weight software package which simulates the complete state space for a finite dynamical system over any finite set. AVAILABILITY AND IMPLEMENTATION Source code is freely available at https://github.com/discretedynamics/cyclone under the Apache-2.0 license.
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Affiliation(s)
- Elena S Dimitrova
- Mathematics Department, California Polytechnic State University, San Luis Obispo, CA 93407, United States
| | - Adam C Knapp
- Department of MD-Pulmonary Laboratory for Systems Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Brandilyn Stigler
- Department of Mathematics, P.O. Box 750156, Southern Methodist University, Dallas, TX 75275, United States
| | - Michael E Stillman
- Department of Mathematics, Cornell University, Ithaca, NY 14853, United States
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Liu Y, Hu M, Zhang R, Xu T, Wang Y, Zhou Z. Visual aggregation of large multivariate networks with attribute-enhanced representation learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Guimapi RA, Niassy S, Mudereri BT, Abdel-Rahman EM, Tepa-Yotto GT, Subramanian S, Mohamed SA, Thunes KH, Kimathi E, Agboka KM, Tamò M, Rwaburindi JC, Hadi B, Elkahky M, Sæthre MG, Belayneh Y, Ekesi S, Kelemu S, Tonnang HE. Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae). Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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Floricel C, Nipu N, Biggs M, Wentzel A, Canahuate G, Van Dijk L, Mohamed A, Fuller CD, Marai GE. THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:151-161. [PMID: 34591766 PMCID: PMC8785360 DOI: 10.1109/tvcg.2021.3114810] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
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Lauber N, Flamm C, Ruiz-Mirazo K. "Minimal metabolism": A key concept to investigate the origins and nature of biological systems. Bioessays 2021; 43:e2100103. [PMID: 34426986 DOI: 10.1002/bies.202100103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 11/07/2022]
Abstract
The systems view on life and its emergence from complex chemistry has remarkably increased the scientific attention on metabolism in the last two decades. However, during this time there has not been much theoretical discussion on what constitutes a metabolism and what role it actually played in biogenesis. A critical and updated review on the topic is here offered, including some references to classical models from last century, but focusing more on current and future research. Metabolism is considered as intrinsically related to the living but not necessarily equivalent to it. More precisely, the idea of "minimal metabolism", in contrast to previous, top-down conceptions, is formulated as a heuristic construct, halfway between chemistry and biology. Thus, rather than providing a complete or final characterization of metabolism, our aim is to encourage further investigations on it, particularly in the context of life's origin, for which some concrete methodological suggestions are provided. Also see the video abstract here: https://youtu.be/DP7VMKk2qpA.
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Affiliation(s)
- Nino Lauber
- Biofisika Institute (CSIC, UPV/EHU), University of the Basque Country, Leioa, Spain.,Department of Philosophy, University of the Basque Country, Leioa, Spain
| | - Christoph Flamm
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Kepa Ruiz-Mirazo
- Biofisika Institute (CSIC, UPV/EHU), University of the Basque Country, Leioa, Spain.,Department of Philosophy, University of the Basque Country, Leioa, Spain
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Ortega OO, Lopez CF. Interactive Multiresolution Visualization of Cellular Network Processes. iScience 2019; 23:100748. [PMID: 31884165 PMCID: PMC6941861 DOI: 10.1016/j.isci.2019.100748] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 11/08/2019] [Accepted: 11/25/2019] [Indexed: 12/24/2022] Open
Abstract
Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.
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Affiliation(s)
- Oscar O Ortega
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA
| | - Carlos F Lopez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA; Biochemistry Department, Vanderbilt University, Nashville, TN, USA.
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Ha S, Dimitrova E, Hoops S, Altarawy D, Ansariola M, Deb D, Glazebrook J, Hillmer R, Shahin H, Katagiri F, McDowell J, Megraw M, Setubal J, Tyler BM, Laubenbacher R. PlantSimLab - a modeling and simulation web tool for plant biologists. BMC Bioinformatics 2019; 20:508. [PMID: 31638901 PMCID: PMC6805577 DOI: 10.1186/s12859-019-3094-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 09/10/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. RESULTS This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. CONCLUSIONS Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.
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Affiliation(s)
- S Ha
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, VA, USA
| | - E Dimitrova
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | - S Hoops
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA
| | | | | | - D Deb
- Department of Natural Sciences, Mercy College, Dobbs Ferry, NY, USA
| | - J Glazebrook
- College of Biological Sciences, University of Minnesota, St. Paul, MN, USA
| | - R Hillmer
- Mendel Biological Solutions, San Franciso, CA, USA
| | - H Shahin
- Virginia Tech, Blacksburg, VA, USA
| | - F Katagiri
- College of Biological Sciences, University of Minnesota, St. Paul, MN, USA
| | - J McDowell
- Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, USA
| | - M Megraw
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - J Setubal
- Biochemistry Department, University of Sao Paolo, Sao Paolo, Brazil.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - B M Tyler
- Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR, USA
| | - R Laubenbacher
- Center for Quantitative Medicine, School of Medicine, University of Connecticut, Hartford, USA.
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Kaur S, Baldi B, Vuong J, O'Donoghue SI. Visualization and Analysis of Epiproteome Dynamics. J Mol Biol 2019; 431:1519-1539. [PMID: 30769119 DOI: 10.1016/j.jmb.2019.01.044] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 12/28/2022]
Abstract
The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.
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Affiliation(s)
- Sandeep Kaur
- University of New South Wales (UNSW), Kensington, NSW 2052, Australia; Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
| | - Benedetta Baldi
- Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia; Data 61, CSIRO, Eveleigh, NSW 2015, Australia.
| | - Jenny Vuong
- Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia; Data 61, CSIRO, Eveleigh, NSW 2015, Australia.
| | - Seán I O'Donoghue
- University of New South Wales (UNSW), Kensington, NSW 2052, Australia; Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia; Data 61, CSIRO, Eveleigh, NSW 2015, Australia.
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Keiriz JJG, Zhan L, Ajilore O, Leow AD, Forbes AG. NeuroCave: A web-based immersive visualization platform for exploring connectome datasets. Netw Neurosci 2018; 2:344-361. [PMID: 30294703 PMCID: PMC6145855 DOI: 10.1162/netn_a_00044] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 01/10/2018] [Indexed: 12/11/2022] Open
Abstract
We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.
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Affiliation(s)
- Johnson J. G. Keiriz
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Liang Zhan
- Department of Engineering and Technology, University of Wisconsin–Stout Menomonie, WI, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Alex D. Leow
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Angus G. Forbes
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
- Computational Media Department, University of California, Santa Cruz, Santa Cruz, CA, USA
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Boutillier P, Maasha M, Li X, Medina-Abarca HF, Krivine J, Feret J, Cristescu I, Forbes AG, Fontana W. The Kappa platform for rule-based modeling. Bioinformatics 2018; 34:i583-i592. [PMID: 29950016 PMCID: PMC6022607 DOI: 10.1093/bioinformatics/bty272] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Motivation We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction. Results We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling. Availability and implementation The Kappa platform is available via the project website at kappalanguage.org. All components of the platform are open source and freely available through the authors' code repositories.
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Affiliation(s)
- Pierre Boutillier
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Mutaamba Maasha
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Xing Li
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Edgewise Networks, Burlington, MA, USA
| | | | - Jean Krivine
- IRIF, Université Paris-Diderot – Paris Paris, France
| | - Jérôme Feret
- Département d’informatique de l’ENS (INRIA/ENS/CNRS), PSL Research University, Paris, France
| | - Ioana Cristescu
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Angus G Forbes
- Department of Computational Media, UC Santa Cruz, Santa Cruz, CA, USA
| | - Walter Fontana
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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