Control of the G-protein cascade dynamics by GDP dissociation inhibitors

Nikonova, Elena; Tsyganov, M. A.; Kölch, Walter; Fey, Dirk; Kholodenko, Boris N.

doi:10.1039/c3mb70152b

Cited by 16 publications

(16 citation statements)

References 57 publications

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“…Therefore a number of modelling approaches have been adopted in order to investigate the interplay between Rac1 and RhoA signalling (Hetmanski et al, ). This has included Boolean models that have defined the network logic associated with mutually antagonistic Rac1 and RhoA signalling (Hetmanski et al, ) and dynamic ODE‐based models that describe the spatiotemporal dynamics and bistability of Rac1 and RhoA signalling (Tsyganov et al, ; Nikonova et al, ; Byrne et al, ).…”

Section: Potential Applications For Modelling Of Matrix Signallingmentioning

confidence: 99%

The extracellular matrix as a key regulator of intracellular signalling networks

Hastings

Skhinas

Fey

et al. 2018

British J Pharmacology

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159

120

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The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.

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Section: Potential Applications For Modelling Of Matrix Signallingmentioning

confidence: 99%

The extracellular matrix as a key regulator of intracellular signalling networks

Hastings

Skhinas

Fey

et al. 2018

British J Pharmacology

Self Cite

159

120

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“…1A and 1B). Differential localization of DIA and ROCK (as well as different spatial distribution of GEFs, GAPs, and guanosine nucleotide dissociation inhibitors (de Beco et al, 2018;Nikonova et al, 2013;Tsyganov et al, 2012)) can generate distinct circuitries of RhoA-Rac1 interactions and different RhoA and Rac1 kinetics along a cell ( Fig. 2B-F).…”

Section: Discussionmentioning

confidence: 99%

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Bolado-Carrancio

Rukhlenko

Nikonova

et al. 2020

Preprint

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Migrating cells need to coordinate distinct leading and trailing edge dynamics but the underlying mechanisms are unclear. Here, we combine experiments and mathematical modeling to elaborate the minimal autonomous biochemical machinery necessary and sufficient for this dynamic coordination and cell movement. RhoA activates Rac1 via DIA and inhibits Rac1 via ROCK, while Rac1 inhibits RhoA through PAK. Our data suggest that in motile, polarized cells, RhoA-ROCK interactions prevail at the rear whereas RhoA-DIA interactions dominate at the front where Rac1/Rho oscillations drive protrusions and retractions. At the rear, high RhoA and low Rac1 activities are maintained until a wave of oscillatory GTPase activities from the cell front reaches the rear, inducing transient GTPase oscillations and RhoA activity spikes. After the rear retracts, the initial GTPase pattern resumes. Our findings show how periodic, propagating GTPase waves coordinate distinct GTPase patterns at the leading and trailing edge dynamics in moving cells.

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“…Overfitting occurs when free parameters are used to fit noise rather than biologically meaningful trends. Although the risk of overfitting is generally higher when more parameters are fitted, the structure of the dynamic model also plays a key role: 24, 38 flexible models that can exhibit a range of behaviours (for example due to multiple feedback loops 39, 40 ) are more prone to overfitting even when the number of parameters remains low. 41, 42 That the SFs might be used by the optimisation to overfit the data is also supported by our observation that many kinetic parameters clustered together with a SF, instead of another biologically related parameter.…”

Section: Discussionmentioning

confidence: 99%

Performance of objective functions and optimisation procedures for parameter estimation in system biology models

2017

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Mathematical modelling of signalling pathways aids experimental investigation in system and synthetic biology. Ever increasing data availability prompts the development of large dynamic models with numerous parameters. In this paper, we investigate how the number of unknown parameters affects the convergence of three frequently used optimisation algorithms and four objective functions. We compare objective functions that use data-driven normalisation of the simulations with those that use scaling factors. The data-driven normalisation of the simulation approach implies that simulations are normalised in the same way as the data, making both directly comparable. The scaling factor approach, which is commonly used for parameter estimation in dynamic systems, introduces scaling factors that multiply the simulations to convert them to the scale of the data. Here we show that the scaling factor approach increases, compared to data-driven normalisation of the simulations, the degree of practical non-identifiability, defined as the number of directions in the parameter space, along which parameters are not identifiable. Further, the results indicate that data-driven normalisation of the simulations greatly improve the speed of convergence of all tested algorithms when the overall number of unknown parameters is relatively large (74 parameters in our test problems). Data-driven normalisation of the simulations also markedly improve the performance of the non-gradient-based algorithm tested even when the number of unknown parameters is relatively small (10 parameters in our test problems). As the models and the unknown parameters increase in size, the data-driven normalisation of the simulation approach can be the preferred option, because it does not aggravate non-identifiability and allows for obtaining parameter estimates in a reasonable amount of time.

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Control of the G-protein cascade dynamics by GDP dissociation inhibitors

Cited by 16 publications

References 57 publications

The extracellular matrix as a key regulator of intracellular signalling networks

The extracellular matrix as a key regulator of intracellular signalling networks

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Performance of objective functions and optimisation procedures for parameter estimation in system biology models

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