ECHO, the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease

Stefano Schivo, Sakshi Khurana, Kannan Govindaraj, Jetse Scholma, Johan Kerkhofs, Leilei Zhong, Xiaobin Huang, Jaco van de Pol, Rom Langerak, André J van Wijnen, Liesbet Geris, Marcel Karperien, Janine N Post*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.

Original languageEnglish
Article number109471
Pages (from-to)1-17
Number of pages17
JournalCellular Signalling
Volume68
Early online date11 Dec 2019
DOIs
Publication statusE-pub ahead of print - 11 Dec 2019

Fingerprint

Chondrocytes
Joints
Interleukin-1beta
Computer Simulation
Cartilage
Health
RNA Sequence Analysis
Mathematics
Matrix Metalloproteinases
Osteoarthritis
Cell Differentiation
Signal Transduction
Stem Cells
Inflammation

Cite this

Schivo, Stefano ; Khurana, Sakshi ; Govindaraj, Kannan ; Scholma, Jetse ; Kerkhofs, Johan ; Zhong, Leilei ; Huang, Xiaobin ; van de Pol, Jaco ; Langerak, Rom ; van Wijnen, André J ; Geris, Liesbet ; Karperien, Marcel ; Post, Janine N. / ECHO, the executable CHOndrocyte : A computational model to study articular chondrocytes in health and disease. In: Cellular Signalling. 2020 ; Vol. 68. pp. 1-17.
@article{c8afbf16bbcd4716a850c0f96f5f0326,
title = "ECHO, the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease",
abstract = "Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.",
author = "Stefano Schivo and Sakshi Khurana and Kannan Govindaraj and Jetse Scholma and Johan Kerkhofs and Leilei Zhong and Xiaobin Huang and {van de Pol}, Jaco and Rom Langerak and {van Wijnen}, {Andr{\'e} J} and Liesbet Geris and Marcel Karperien and Post, {Janine N}",
note = "Copyright {\circledC} 2019 The Author(s). Published by Elsevier Inc. All rights reserved.",
year = "2019",
month = "12",
day = "11",
doi = "10.1016/j.cellsig.2019.109471",
language = "English",
volume = "68",
pages = "1--17",
journal = "Cellular Signalling",
issn = "0898-6568",
publisher = "Elsevier Inc.",

}

Schivo, S, Khurana, S, Govindaraj, K, Scholma, J, Kerkhofs, J, Zhong, L, Huang, X, van de Pol, J, Langerak, R, van Wijnen, AJ, Geris, L, Karperien, M & Post, JN 2020, 'ECHO, the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease', Cellular Signalling, vol. 68, 109471, pp. 1-17. https://doi.org/10.1016/j.cellsig.2019.109471

ECHO, the executable CHOndrocyte : A computational model to study articular chondrocytes in health and disease. / Schivo, Stefano; Khurana, Sakshi; Govindaraj, Kannan; Scholma, Jetse; Kerkhofs, Johan; Zhong, Leilei; Huang, Xiaobin; van de Pol, Jaco; Langerak, Rom; van Wijnen, André J; Geris, Liesbet; Karperien, Marcel; Post, Janine N.

In: Cellular Signalling, Vol. 68, 109471, 04.2020, p. 1-17.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - ECHO, the executable CHOndrocyte

T2 - A computational model to study articular chondrocytes in health and disease

AU - Schivo, Stefano

AU - Khurana, Sakshi

AU - Govindaraj, Kannan

AU - Scholma, Jetse

AU - Kerkhofs, Johan

AU - Zhong, Leilei

AU - Huang, Xiaobin

AU - van de Pol, Jaco

AU - Langerak, Rom

AU - van Wijnen, André J

AU - Geris, Liesbet

AU - Karperien, Marcel

AU - Post, Janine N

N1 - Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

PY - 2019/12/11

Y1 - 2019/12/11

N2 - Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.

AB - Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.

U2 - 10.1016/j.cellsig.2019.109471

DO - 10.1016/j.cellsig.2019.109471

M3 - Article

C2 - 31837466

VL - 68

SP - 1

EP - 17

JO - Cellular Signalling

JF - Cellular Signalling

SN - 0898-6568

M1 - 109471

ER -