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Khurana S, Schivo S, Plass JRM, Mersinis N, Scholma J, Kerkhofs J, Zhong L, van de Pol J, Langerak R, Geris L, Karperien M, Post JN. An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation. Front Bioeng Biotechnol 2021; 9:732917. [PMID: 34869253 PMCID: PMC8634894 DOI: 10.3389/fbioe.2021.732917] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.
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Affiliation(s)
- Sakshi Khurana
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Stefano Schivo
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands.,Department of Formal Methods and Tools, CTIT Institute, University of Twente, Enschede, Netherlands
| | - Jacqueline R M Plass
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Nikolas Mersinis
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Jetse Scholma
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Johan Kerkhofs
- Biomechanics Research Unit, GIGA In Silico Medicine, ULiège, Liège, Belgium
| | - Leilei Zhong
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Jaco van de Pol
- Department of Formal Methods and Tools, CTIT Institute, University of Twente, Enschede, Netherlands.,Dept. of Computer Science, Aarhus University, Aarhus, Denmark
| | - Rom Langerak
- Department of Formal Methods and Tools, CTIT Institute, University of Twente, Enschede, Netherlands
| | - Liesbet Geris
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Marcel Karperien
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
| | - Janine N Post
- Technical Medicine Centre, Department of Developmental BioEngineering, University of Twente, Enschede, Netherlands
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Vilallonga G, Riesco D, de Almeida ACG, Rodrigues AM, Campos SVA. In Silico Laboratory Experiments Using Statistical Model Checking: A New Model of the Palytoxin-Induced Pump Channel as Case Study. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2816-2822. [PMID: 33017286 DOI: 10.1109/tcbb.2020.3028776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Studying biological systems is a difficult but important task. Traditional methods include laboratory experimentation and computer simulations. However, often researchers need to explore important but potentially rare events that are not easily observed or simulated. We use UPPAAL-SMC, a formal verification tool to support a methodology that allows us to model biological systems, specify events and conditions that we want to analyze, and to explore system executions using controlled simulations. We also describe an efficient way to reproduce laboratory experiments in silico. Unlike traditional simulations, we are able to guide the experiment to explore special events and conditions by expressing these conditions in temporal logic formulas. We have applied this methodology to create a more detailed model of Palytoxin-induced Na +/K + pump channels than was previously possible. Moreover, we have reproduced experimental protocols and their associated electrophysiological recordings, which has not been done in previous works. As a consequence, we have been able to propose a new diprotomeric model for the PTX-pump complex and study its behaviour. The use of our methodology has enabled us to reduce the effort and time to perform this research. It can be used to model and analyze other complex biological systems, potentially increasing the productivity of such studies.
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Wetselaar P, Lobbezoo F, de Jong P, Choudry U, van Rooijen J, Langerak R. A methodology for evaluating tooth wear monitoring using timed automata modelling. J Oral Rehabil 2020; 47:353-360. [PMID: 31721264 PMCID: PMC7027495 DOI: 10.1111/joor.12908] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/23/2019] [Accepted: 10/29/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Tooth wear is a multifactorial condition leading to the loss of dental hard tissues. A counselling/monitoring protocol is of importance in order to keep that loss as limited as possible. Since many factors are involved and a time span of decades is included, research to disentangle all these processes in patients is difficult. Instead, a modelling technique was used that is able to deal with time, costs and probabilistic and stochastic information. The aim was to shed light on the question: does a yearly or a once-in-five-years counselling/monitoring protocol yield better outcome measures? METHODS A so-called timed automata model was adopted, analysed with the tool UPPAAL. To our knowledge, this is the first time that formal modelling is applied in dentistry. In this article, a UPPAAL model for the evaluation of tooth wear is described. RESULTS Using the UPPAAL model, it was calculated that with a yearly counselling/monitoring protocol the severity of tooth wear at age 74, the total costs per person and the number of restorative treatments were less, and the number of so-called "good years" was higher. CONCLUSIONS With the use of the UPPAAL model, it may be concluded that a yearly counselling/monitoring protocol can yield better outcome measures. CLINICAL SIGNIFICANCE Regarding dentistry in general and tooth wear in particular, with the use of a timed automata model in UPPAAL, actual research questions can be answered, factors of influence in a multifactorial condition like tooth wear can be clarified, and future research topics can be determined.
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Affiliation(s)
- Peter Wetselaar
- Department of Orofacial Pain and DysfunctionAcademic Centre for Dentistry Amsterdam (ACTA)University of Amsterdam and Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Frank Lobbezoo
- Department of Orofacial Pain and DysfunctionAcademic Centre for Dentistry Amsterdam (ACTA)University of Amsterdam and Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Pepijn de Jong
- Department of Orofacial Pain and DysfunctionAcademic Centre for Dentistry Amsterdam (ACTA)University of Amsterdam and Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Umarah Choudry
- Department of Orofacial Pain and DysfunctionAcademic Centre for Dentistry Amsterdam (ACTA)University of Amsterdam and Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Jasper van Rooijen
- Formal Methods and Tools GroupFaculty of EEMCSUniversity of TwenteEnschedeThe Netherlands
| | - Rom Langerak
- Formal Methods and Tools GroupFaculty of EEMCSUniversity of TwenteEnschedeThe Netherlands
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Zhong L, Schivo S, Huang X, Leijten J, Karperien M, Post JN. Nitric Oxide Mediates Crosstalk between Interleukin 1β and WNT Signaling in Primary Human Chondrocytes by Reducing DKK1 and FRZB Expression. Int J Mol Sci 2017; 18:ijms18112491. [PMID: 29165387 PMCID: PMC5713457 DOI: 10.3390/ijms18112491] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 11/16/2017] [Accepted: 11/17/2017] [Indexed: 12/20/2022] Open
Abstract
Interleukin 1 beta (IL1β) and Wingless-Type MMTV Integration Site Family (WNT) signaling are major players in Osteoarthritis (OA) pathogenesis. Despite having a large functional overlap in OA onset and development, the mechanism of IL1β and WNT crosstalk has remained largely unknown. In this study, we have used a combination of computational modeling and molecular biology to reveal direct or indirect crosstalk between these pathways. Specifically, we revealed a mechanism by which IL1β upregulates WNT signaling via downregulating WNT antagonists, DKK1 and FRZB. In human chondrocytes, IL1β decreased the expression of Dickkopf-1 (DKK1) and Frizzled related protein (FRZB) through upregulation of nitric oxide synthase (iNOS), thereby activating the transcription of WNT target genes. This effect could be reversed by iNOS inhibitor 1400W, which restored DKK1 and FRZB expression and their inhibitory effect on WNT signaling. In addition, 1400W also inhibited both the matrix metalloproteinase (MMP) expression and cytokine-induced apoptosis. We concluded that iNOS/NO play a pivotal role in the inflammatory response of human OA through indirect upregulation of WNT signaling. Blocking NO production may inhibit the loss of the articular phenotype in OA by preventing downregulation of the expression of DKK1 and FRZB.
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Affiliation(s)
- Leilei Zhong
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Stefano Schivo
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
- Formal Methods and Tools, CTIT, University of Twente, 7522 NB Enschede, The Netherlands.
| | - Xiaobin Huang
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
| | - Jeroen Leijten
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
| | - Marcel Karperien
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
| | - Janine N Post
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, 7522 NB Enschede, The Netherlands.
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Schivo S, Scholma J, van der Vet PE, Karperien M, Post JN, van de Pol J, Langerak R. Modelling with ANIMO: between fuzzy logic and differential equations. BMC SYSTEMS BIOLOGY 2016; 10:56. [PMID: 27460034 PMCID: PMC4962523 DOI: 10.1186/s12918-016-0286-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 06/02/2016] [Indexed: 11/23/2022]
Abstract
BACKGROUND Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. RESULTS ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. CONCLUSIONS Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic.
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Affiliation(s)
- Stefano Schivo
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Jetse Scholma
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Paul E van der Vet
- Human Media Interaction, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Marcel Karperien
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Janine N Post
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Jaco van de Pol
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Rom Langerak
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands.
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Rubinstein A, Bracha N, Rudner L, Zucker N, Sloin HE, Chor B. BioNSi: A Discrete Biological Network Simulator Tool. J Proteome Res 2016; 15:2871-80. [PMID: 27354160 DOI: 10.1021/acs.jproteome.6b00278] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.
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Affiliation(s)
- Amir Rubinstein
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
| | - Noga Bracha
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
| | - Liat Rudner
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
| | - Noga Zucker
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
| | - Hadas E Sloin
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
| | - Benny Chor
- Blavatnik School of Computer Science, and ‡Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, Israel
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A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks. PLoS One 2015; 10:e0130033. [PMID: 26067297 PMCID: PMC4489432 DOI: 10.1371/journal.pone.0130033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 05/15/2015] [Indexed: 12/29/2022] Open
Abstract
Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.
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