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Matzko RO, Konur S. BioNexusSentinel: a visual tool for bioregulatory network and cytohistological RNA-seq genetic expression profiling within the context of multicellular simulation research using ChatGPT-augmented software engineering. BIOINFORMATICS ADVANCES 2024; 4:vbae046. [PMID: 38571784 PMCID: PMC10990683 DOI: 10.1093/bioadv/vbae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
Summary Motivated by the need to parameterize ongoing multicellular simulation research, this paper documents the culmination of a ChatGPT augmented software engineering cycle resulting in an integrated visual platform for efficient cytohistological RNA-seq and bioregulatory network exploration. As contrasted to other systems and synthetic biology tools, BioNexusSentinel was developed de novo to uniquely combine these features. Reactome served as the primary source of remotely accessible biological models, accessible using BioNexusSentinel's novel search engine and REST API requests. The innovative, feature-rich gene expression profiler component was developed to enhance the exploratory experience for the researcher, culminating in the cytohistological RNA-seq explorer based on Human Protein Atlas data. A novel cytohistological classifier would be integrated via pre-processed analysis of the RNA-seq data via R statistical language, providing for useful analytical functionality and good performance for the end-user. Implications of the work span prospects for model orthogonality evaluations, gap identification in network modelling, prototyped automatic kinetics parameterization, and downstream simulation and cellular biological state analysis. This unique computational biology software engineering collaboration with generative natural language processing artificial intelligence was shown to enhance worker productivity, with evident benefits in terms of accelerating coding and machine-human intelligence transfer. Availability and implementation BioNexusSentinel project releases, with corresponding data and installation instructions, are available at https://github.com/RichardMatzko/BioNexusSentinel.
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Affiliation(s)
- Richard Oliver Matzko
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
| | - Savas Konur
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
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Shin W, Gennari JH, Hellerstein JL, Sauro HM. An automated model annotation system (AMAS) for SBML models. Bioinformatics 2023; 39:btad658. [PMID: 37882737 PMCID: PMC10628433 DOI: 10.1093/bioinformatics/btad658] [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: 06/29/2023] [Revised: 10/03/2023] [Accepted: 10/24/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. RESULTS We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AVAILABILITY AND IMPLEMENTATION Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.
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Affiliation(s)
- Woosub Shin
- Auckland Bioengineering Institute, University of Auckland, 1010 Auckland, New Zealand
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, United States
| | - Joseph L Hellerstein
- eScience Institute, University of Washington, Seattle, WA 98195, United States
- Paul G. Allen School of Computer Science, University of Washington, Seattle, WA 98195, United States
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98195, United States
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Shin W, Gennari JH, Hellerstein JL, Sauro HM. An Automated Model Annotation System (AMAS) for SBML Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549722. [PMID: 37503075 PMCID: PMC10370092 DOI: 10.1101/2023.07.19.549722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. Results We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g., species, reactions) by specifying the reference database (e.g., ChEBI for species) and the match score function (e.g., string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has sub-second response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. Availability Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.
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Affiliation(s)
- Woosub Shin
- Auckland Bioengineering Institute, University of Auckland, Auckland,1010,New Zealand
| | - John H. Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, 98195, WA, USA
| | - Joseph L. Hellerstein
- eScience Institute, University of Washington, Seattle,98195, WA, USA
- Paul G. Allen School of Computer Science, University of Washington, Seattle, 98195, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, 98195, WA, USA
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4
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Xu J. SBMLKinetics: a tool for annotation-independent classification of reaction kinetics for SBML models. BMC Bioinformatics 2023; 24:248. [PMID: 37312031 DOI: 10.1186/s12859-023-05380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/07/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Reaction networks are widely used as mechanistic models in systems biology to reveal principles of biological systems. Reactions are governed by kinetic laws that describe reaction rates. Selecting the appropriate kinetic laws is difficult for many modelers. There exist tools that attempt to find the correct kinetic laws based on annotations. Here, I developed annotation-independent technologies that assist modelers by focusing on finding kinetic laws commonly used for similar reactions. RESULTS Recommending kinetic laws and other analyses of reaction networks can be viewed as a classification problem. Existing approaches to determining similar reactions rely heavily on having good annotations, a condition that is often unsatisfied in model repositories such as BioModels. I developed an annotation-independent approach to find similar reactions via reaction classifications. I proposed a two-dimensional kinetics classification scheme (2DK) that analyzed reactions along the dimensions of kinetics type (K type) and reaction type (R type). I identified approximately ten mutually exclusive K types, including zeroth order, mass action, Michaelis-Menten, Hill kinetics, and others. R types were organized by the number of distinct reactants and the number of distinct products in reactions. I constructed a tool, SBMLKinetics, that inputted a collection of SBML models and then calculated reaction classifications as the probability of each 2DK class. The effectiveness of 2DK was evaluated on BioModels, and the scheme classified over 95% of the reactions. CONCLUSIONS 2DK had many applications. It provided a data-driven annotation-independent approach to recommending kinetic laws by using type common for the kind of models in combination with the R type of the reactions. Alternatively, 2DK could also be used to alert users that a kinetic law was unusual for the K type and R type. Last, 2DK provided a way to analyze groups of models to compare their kinetic laws. I applied 2DK to BioModels to compare the kinetics of signaling networks with the kinetics of metabolic networks and found significant differences in K type distributions.
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Affiliation(s)
- Jin Xu
- Department of Bioengineering, University of Washington, Seattle, USA.
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5
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Bhandari G, Sharma M, Negi S, Gangola S, Bhatt P, Chen S. System biology analysis of endosulfan biodegradation in bacteria and its effect in other living systems: modeling and simulation studies. J Biomol Struct Dyn 2022; 40:13171-13183. [PMID: 34622744 DOI: 10.1080/07391102.2021.1982773] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Endosulfan is a broadly applied cyclodiene insecticide which has been in use across 80 countries since last 5 decades. Owing to its recalcitrant nature, endosulfan residues have been reported from air, water and soil causing toxicity to various non-target organisms. Microbial decontamination of endosulfan has been reported previously by several authors. In the current study, we have evaluated the pathways of endosulfan degradation and its hazardous impact on other living beings including insects, humans, plants, aquatic life and environment by in-silico methods. For establishment of the endosulfan metabolism in different ecosystems, cell designer was employed. The established model was thereafter assessed and simulated to understand the biochemical and physiological metabolism of the endosulfan in various systems of the network. Topological investigation analysis of the endosulfan metabolism validated the presence of 207 nodes and 274 edges in the network. We have concluded that biomagnification of the endosulfan generally occurs in the various elements of the ecosystem. Dynamics study of endosulfan degrading enzymes suggested the important role of monooxygenase I, II and hydrolase in endosulfan bioremediation. Endosulfan shows toxicity in human beings, fishes and plants, however it is biodegraded by the microbes. To date, there are no reports of in- silico analysis of bioremediation of endosulfan and its hazardous effects on the environment. Thus, this report can be important in terms of modelling and simulation of biodegradation network of endosulfan and similar compounds and their impact on several other systems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Geeta Bhandari
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Mukund Sharma
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Shalini Negi
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Saurabh Gangola
- School of Agriculture, Graphic Era Hill University, Bhimtal Campus, Uttarakhand, India
| | - Pankaj Bhatt
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
| | - Shaohua Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
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Weilandt DR, Salvy P, Masid M, Fengos G, Denhardt-Erikson R, Hosseini Z, Hatzimanikatis V. Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models. Bioinformatics 2022; 39:6887139. [PMID: 36495209 PMCID: PMC9825757 DOI: 10.1093/bioinformatics/btac787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 11/22/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently. RESULTS We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. AVAILABILITY AND IMPLEMENTATION The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Bhatt P, Sethi K, Gangola S, Bhandari G, Verma A, Adnan M, Singh Y, Chaube S. Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems. J Biomol Struct Dyn 2020; 40:3285-3295. [PMID: 33179575 DOI: 10.1080/07391102.2020.1846623] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Atrazine is the most commonly used herbicide worldwide in the agricultural system. The increased environmental concentration of the atrazine showed the toxic effects on the non-target living species. Biodegradation of the atrazine is possible with the bacterial systems. The present study investigated biodegradation potential of atrazine degrading bacteria and the impact of atrazine on environmental systems. Model of atrazine fate in ecological systems constructed using the cell designer. The used model further analyzed and simulated to know the biochemistry and physiology of the atrazine in different cellular networks. Topological analysis of the atrazine degradation confirmed the 289 nodes and 300 edges. Our results showed that the overall biomagnification of the atrazine in the different environmental systems. Atrazine is showing toxic effects on humans and plants, whereas degraded by the bacterial systems. To date, no one has analyzed the complete degradation and poisonous effects of the atrazine in the environment. Therefore, this study is useful for overall system biology based modeling and simulation analysis of atrazine in living systems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Pankaj Bhatt
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Kanika Sethi
- Department of Microbiology, Dolphin (P.G) Institute of Biomedical and Natural Sciences, Dehradun, India
| | - Saurabh Gangola
- School of Agriculture, Graphic Era Hill University Bhimtal Campus, Uttarakhand, India
| | - Geeta Bhandari
- Department of Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Amit Verma
- Department of Biochemistry, College of Basic Science and Humanities, SD Agricultural University, Gujarat, India
| | - Muhammad Adnan
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Yashpal Singh
- Department of Veterinary Physiology and Biochemistry, G.B Pant University of Agriculture and Technology, Pantnagar, India
| | - Shshank Chaube
- Department of Mathematics, University of Petrolium and Energy Studies, Dehradun, India
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Bhatt K, Maheshwari DK. Insights into zinc-sensing metalloregulator 'Zur' deciphering mechanism of zinc transportation in Bacillus spp. by modeling, simulation and molecular docking. J Biomol Struct Dyn 2020; 40:764-779. [PMID: 32924811 DOI: 10.1080/07391102.2020.1818625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
To comprehend the molecular mechanism of zinc transportation by bacteria tends to be a very complicated and time-consuming method. To date, fragmented and scanty studies are available about the mechanism of zinc transportation at molecular level. So, the present study scrutinizes in silico pathways of zinc fractions transportation, specifically in Bacillus spp. stimulating dynamic performance of zinc. For this, the constructed model reveals Zur to be the prime regulatory transport protein maintaining bacterial survivability at fluctuation in zinc concentrations, thereby attaining zinc homeostasis. Topology for hub nodes displays appropriate evidence of the molecular basis of bacterial zinc imports and exports. Further, the molecular docking reveals interaction of Zur protein with the zinc ligands (ZnCO3 and ZnSO4). By validation of binding affinity, binding energy and docking score via Autodock Vina and X-Score, the ZnSO4 compound was found to possess excellent stability in the active pocket site of Zur, stating Zur-ZnSO4 complex to be the most potential. Owing to which, the Zur-ZnSO4 complex was selected and subjected to molecular dynamics simulation, revealing RMSD, RG, RMSF, SASA and interaction energy for 20 ns trajectory period. Henceforth,the study provides novel insight into revealing the unrecognized Zur protein pathway, assisting zinc transportation, besides retaining best interaction with ZnSO4 ligand. This is the first system biology where molecular docking and molecular dynamics simulation-based investigation decipher the role of Zur transport protein system and interaction of its amino acids with zinc ligands in a simpler and economical form via in silico techniques.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kalpana Bhatt
- Department of Botany and Microbiology, Gurukula Kangri University, Haridwar, Uttarakhand, India
| | - Dinesh Kumar Maheshwari
- Department of Botany and Microbiology, Gurukula Kangri University, Haridwar, Uttarakhand, India
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Wittig U, Rey M, Weidemann A, Kania R, Müller W. SABIO-RK: an updated resource for manually curated biochemical reaction kinetics. Nucleic Acids Res 2019; 46:D656-D660. [PMID: 29092055 PMCID: PMC5753344 DOI: 10.1093/nar/gkx1065] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/18/2017] [Indexed: 01/19/2023] Open
Abstract
SABIO-RK (http://sabiork.h-its.org/) is a manually curated database containing data about biochemical reactions and their reaction kinetics. The data are primarily extracted from scientific literature and stored in a relational database. The content comprises both naturally occurring and alternatively measured biochemical reactions and is not restricted to any organism class. The data are made available to the public by a web-based search interface and by web services for programmatic access. In this update we describe major improvements and extensions of SABIO-RK since our last publication in the database issue of Nucleic Acid Research (2012). (i) The website has been completely revised and (ii) allows now also free text search for kinetics data. (iii) Additional interlinkages with other databases in our field have been established; this enables users to gain directly comprehensive knowledge about the properties of enzymes and kinetics beyond SABIO-RK. (iv) Vice versa, direct access to SABIO-RK data has been implemented in several systems biology tools and workflows. (v) On request of our experimental users, the data can be exported now additionally in spreadsheet formats. (vi) The newly established SABIO-RK Curation Service allows to respond to specific data requirements.
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Affiliation(s)
- Ulrike Wittig
- Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Maja Rey
- Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Andreas Weidemann
- Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Renate Kania
- Modelling of Biological Processes, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Wolfgang Müller
- Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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10
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Abstract
Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informative, as it provides a direct, quantitative biochemical phenotype. There are a number of ways in which metabolomic data can be analyzed in systems biology; however, the thermodynamic and kinetic relevance of these data cannot be overstated. Genome-scale metabolic network reconstructions provide a natural context to incorporate metabolomic data in order to provide insight into the condition-specific kinetic characteristics of metabolic networks. Herein we discuss how metabolomic data can be incorporated into constraint-based models in a flexible framework that enables scaling from small pathways to cell-scale models, while being able to accommodate coarse-grained to more detailed, allosteric interactions, all using the well-known principle of mass action.
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11
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Smith RW, van Rosmalen RP, Martins Dos Santos VAP, Fleck C. DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems. BMC SYSTEMS BIOLOGY 2018; 12:72. [PMID: 29914475 PMCID: PMC6006996 DOI: 10.1186/s12918-018-0584-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 05/14/2018] [Indexed: 12/21/2022]
Abstract
Background Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. Results In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. Conclusion The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future. Electronic supplementary material The online version of this article (10.1186/s12918-018-0584-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert W Smith
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,LifeGlimmer GmbH, Markelstrasse 38, Berlin, 12163, Germany
| | - Rik P van Rosmalen
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,LifeGlimmer GmbH, Markelstrasse 38, Berlin, 12163, Germany
| | - Christian Fleck
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.
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12
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Pathak RK, Baunthiyal M, Pandey N, Pandey D, Kumar A. Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica. Sci Rep 2017; 7:16790. [PMID: 29196636 PMCID: PMC5711873 DOI: 10.1038/s41598-017-16884-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 11/08/2017] [Indexed: 01/01/2023] Open
Abstract
The productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Biotechnology, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, 246194, Uttarakhand, India
| | - Mamta Baunthiyal
- Department of Biotechnology, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, 246194, Uttarakhand, India.
| | - Neetesh Pandey
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute (IASRI), Pusa, 110012, New Delhi, India
| | - Dinesh Pandey
- Department of Molecular Biology & Genetic Engineering, College of Basic Sciences & Humanities, G. B. Pant University of Agriculture & Technology, Pantnagar, 263145, India
| | - Anil Kumar
- Department of Molecular Biology & Genetic Engineering, College of Basic Sciences & Humanities, G. B. Pant University of Agriculture & Technology, Pantnagar, 263145, India.
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Gyori BM, Bachman JA, Subramanian K, Muhlich JL, Galescu L, Sorger PK. From word models to executable models of signaling networks using automated assembly. Mol Syst Biol 2017; 13:954. [PMID: 29175850 PMCID: PMC5731347 DOI: 10.15252/msb.20177651] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.
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Affiliation(s)
- Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - John A Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Kartik Subramanian
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jeremy L Muhlich
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Lucian Galescu
- Institute for Human and Machine Cognition, Pensacola, FL, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
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Kazantsev F, Akberdin I, Lashin S, Ree N, Timonov V, Ratushny A, Khlebodarova T, Likhoshvai V. MAMMOTh: A new database for curated mathematical models of biomolecular systems. J Bioinform Comput Biol 2017; 16:1740010. [PMID: 29172865 DOI: 10.1142/s0219720017400108] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
MOTIVATION Living systems have a complex hierarchical organization that can be viewed as a set of dynamically interacting subsystems. Thus, to simulate the internal nature and dynamics of the entire biological system, we should use the iterative way for a model reconstruction, which is a consistent composition and combination of its elementary subsystems. In accordance with this bottom-up approach, we have developed the MAthematical Models of bioMOlecular sysTems (MAMMOTh) tool that consists of the database containing manually curated MAMMOTh fitted to the experimental data and a software tool that provides their further integration. RESULTS The MAMMOTh database entries are organized as building blocks in a way that the model parts can be used in different combinations to describe systems with higher organizational level (metabolic pathways and/or transcription regulatory networks). The tool supports export of a single model or their combinations in SBML or Mathematica standards. The database currently contains 110 mathematical sub-models for Escherichia coli elementary subsystems (enzymatic reactions and gene expression regulatory processes) that can be combined in at least 5100 complex/sophisticated models concerning more complex biological processes as de novo nucleotide biosynthesis, aerobic/anaerobic respiration and nitrate/nitrite utilization in E. coli. All models are functionally interconnected and sufficiently complement public model resources. AVAILABILITY http://mammoth.biomodelsgroup.ru.
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Affiliation(s)
- Fedor Kazantsev
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Ilya Akberdin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia.,¶ Biology Department, San Diego State University, San Diego, CA 92182-4614, USA
| | - Sergey Lashin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Natalia Ree
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vladimir Timonov
- † Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Alexander Ratushny
- ‡ Center for Infectious Disease Research (Formerly Seattle, Biomedical Research Institute), Seattle, WA 98109, USA.,§ Institute for Systems Biology, Seattle, WA 98109-5234, USA
| | - Tamara Khlebodarova
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vitaly Likhoshvai
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
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Fröhlich F, Kaltenbacher B, Theis FJ, Hasenauer J. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. PLoS Comput Biol 2017; 13:e1005331. [PMID: 28114351 PMCID: PMC5256869 DOI: 10.1371/journal.pcbi.1005331] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 12/20/2016] [Indexed: 01/06/2023] Open
Abstract
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. In this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.
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Affiliation(s)
- Fabian Fröhlich
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | | | - Fabian J. Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
- * E-mail:
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Costa RS, Hartmann A, Vinga S. Kinetic modeling of cell metabolism for microbial production. J Biotechnol 2015; 219:126-41. [PMID: 26724578 DOI: 10.1016/j.jbiotec.2015.12.023] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/25/2015] [Accepted: 12/15/2015] [Indexed: 12/20/2022]
Abstract
Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.
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Affiliation(s)
- Rafael S Costa
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal.
| | - Andras Hartmann
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
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