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Xu J, Smith L. Curating models from BioModels: Developing a workflow for creating OMEX files. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585236. [PMID: 38559029 PMCID: PMC10979985 DOI: 10.1101/2024.03.15.585236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the validation procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.
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Affiliation(s)
- Jin Xu
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Lucian Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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Ponsiglione AM, Montefusco F, Donisi L, Tedesco A, Cosentino C, Merola A, Romano M, Amato F. A General Approach for the Modelling of Negative Feedback Physiological Control Systems. Bioengineering (Basel) 2023; 10:835. [PMID: 37508862 PMCID: PMC10376068 DOI: 10.3390/bioengineering10070835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/23/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work's objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.
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Affiliation(s)
- Alfonso Maria Ponsiglione
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
| | - Francesco Montefusco
- Dipartimento di Scienze Economiche, Giuridiche, Informatiche e Motorie, Università degli Studi di Napoli Parthenope, 80035 Nola, Italy
| | - Leandro Donisi
- Dipartimento di Scienze Mediche e Chirurgiche Avanzate, Università degli studi della Campania "Luigi Vanvitelli", P.zza L. Miraglia 2, 80138 Napoli, Italy
| | - Annarita Tedesco
- Dipartimento di Ingegneria per l'Innovazione, Universitá del Salento, 73100 Lecce, Italy
| | - Carlo Cosentino
- School of Computer and Biomedical Engineering, Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi Magna Græcia di Catanzaro, Campus di Germaneto "Salvatore Venuta", 88100 Catanzaro, Italy
| | - Alessio Merola
- School of Computer and Biomedical Engineering, Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi Magna Græcia di Catanzaro, Campus di Germaneto "Salvatore Venuta", 88100 Catanzaro, Italy
| | - Maria Romano
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
| | - Francesco Amato
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
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Wang S, She Z, Ge SS. Inner-Estimating Domains of Attraction for Nonpolynomial Systems With Polynomial Differential Inclusions. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:1628-1641. [PMID: 32386182 DOI: 10.1109/tcyb.2020.2987326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, based on polynomial differential inclusions, we propose a heuristic iterative approach for estimating the domains of attraction for nonpolynomial systems. First, we use the fuzzy model to construct a polynomial differential inclusion for the nonpolynomial system, which can be equivalently written as a time-invariant uncertain polynomial system. Then, beginning with an initial inner estimation, we present an iterative approach to enlarge this initial inner estimation by calculating common Lyapunov-like functions. Furthermore, the domains of attraction are estimated by combining this iterative approach with heuristic construction of differential inclusions. In the end, our heuristic iterative approach is implemented with linear semidefinite programming and then tested on some nonpolynomial examples with comparisons to the existing methods in the literature.
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A dual approach for positive T–S fuzzy controller design and its application to cancer treatment under immunotherapy and chemotherapy. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Pourhashemi A, Haghighatnia S, Moghaddam RK. Identification of tumor-immune system via recurrent neural network. HEALTH AND TECHNOLOGY 2014. [DOI: 10.1007/s12553-013-0069-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Khodadadi L, Samadi B, Khaloozadeh H. Estimation of region of attraction for polynomial nonlinear systems: a numerical method. ISA TRANSACTIONS 2014; 53:25-32. [PMID: 24008183 DOI: 10.1016/j.isatra.2013.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Revised: 06/12/2013] [Accepted: 08/08/2013] [Indexed: 06/02/2023]
Abstract
This paper introduces a numerical method to estimate the region of attraction for polynomial nonlinear systems using sum of squares programming. This method computes a local Lyapunov function and an invariant set around a locally asymptotically stable equilibrium point. The invariant set is an estimation of the region of attraction for the equilibrium point. In order to enlarge the estimation, a subset of the invariant set defined by a shape factor is enlarged by solving a sum of squares optimization problem. In this paper, a new algorithm is proposed to select the shape factor based on the linearized dynamic model of the system. The shape factor is updated in each iteration using the computed local Lyapunov function from the previous iteration. The efficiency of the proposed method is shown by a few numerical examples.
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Affiliation(s)
- Larissa Khodadadi
- Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Behzad Samadi
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
| | - Hamid Khaloozadeh
- Department of Systems and Control, Industrial Control Center of Excellence, K.N.Toosi University of Technology, Tehran, Iran.
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Eftimie R, Bramson JL, Earn DJD. Interactions between the immune system and cancer: a brief review of non-spatial mathematical models. Bull Math Biol 2010; 73:2-32. [PMID: 20225137 DOI: 10.1007/s11538-010-9526-3] [Citation(s) in RCA: 170] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2009] [Accepted: 02/18/2010] [Indexed: 12/14/2022]
Abstract
We briefly review spatially homogeneous mechanistic mathematical models describing the interactions between a malignant tumor and the immune system. We begin with the simplest (single equation) models for tumor growth and proceed to consider greater immunological detail (and correspondingly more equations) in steps. This approach allows us to clarify the necessity for expanding the complexity of models in order to capture the biological mechanisms we wish to understand. We conclude by discussing some unsolved problems in the mathematical modeling of cancer-immune system interactions.
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Affiliation(s)
- Raluca Eftimie
- Department of Mathematics and Statistic, McMaster University, Hamilton, ON, Canada, L8S 4K1.
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