Reduction of Qualitative Models of Biological Networks for Transient Dynamics Analysis

Paulevé, Loı̈c

doi:10.1109/tcbb.2017.2749225

Cited by 18 publications

(20 citation statements)

References 41 publications

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“…This is particularly important to verify if a model is able of generate and justify some observed behaviour, or to predict how a network can evolve from a specific state. Model reduction techniques to reduce the size of the generated dynamics have also been proposed (Naldi et al, 2011;Pauleve, 2018). These techniques aim to reduce the combinatorial explosion of the state space when analysing dynamical properties of a network.…”

Section: Related Workmentioning

confidence: 99%

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Gouveia

Lynce

Monteiro

2020

Preprint

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Motivation: Complex cellular processes can be represented by biological regulatory networks. Computational models of such networks have successfully allowed the reprodution of known behaviour and to have a better understanding of the associated cellular processes. However, the construction of these models is still mainly a manual task, and therefore prone to error. Additionally, as new data is acquired, existing models must be revised. Here, we propose a model revision approach of Boolean logical models capable of repairing inconsistent models confronted with time-series observations. Moreover, we account for both synchronous and asynchronous dynamics. Results: The proposed tool is tested on five well known biological models. Different time-series observations are generated, consistent with these models. Then, the models are corrupted with different random changes. The proposed tool is able to repair the majority of the corrupted models, considering the generated time-series observations. Moreover, all the optimal solutions to repair the models are produced. Contact:

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Section: Related Workmentioning

confidence: 99%

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Gouveia

Lynce

Monteiro

2020

Preprint

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“…Nevertheless, different approaches enable the analysis of large scale qualitative networks by means of structural analyses, model reductions or abstractions. The CoLoMoTo Interactive Notebook provides access to methods for model reductions , such as by Naldi et al ( 2011 ), implemented in bio LQM, which preserves stable states, while cyclic attractors and reachability can be affected in predictable ways, or by using formal approximations of the dynamical behavior, as implemented in P int , which allow tackling networks with several thousands of nodes (Paulevé, 2017 , in press ). Other approaches include, for instance, Petri net model reduction for trajectories in signaling pathways (Talcott and Dill, 2006 ), subnetwork analysis (Siebert, 2009 ), computational algebra (Veliz-Cuba et al, 2014 ), and motif-based abstractions for attractors (Gan and Albert, 2018 ).…”

Section: Background On Qualitative Dynamical Models and Their Compmentioning

confidence: 99%

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

et al. 2018

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Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.

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“…inhibition) of some gene or transcription factor or kinase, etc. From an initial given state for each entity in BRN, the goal is reachable if a sequence of steps is present which leads to the state which contains the goal (Paulevé, 2018). For example, the goal in a signaling network is usually the activation of the downstream transcription factor starting from the initial inactive state.…”

Section: Process Hitting (Ph) Frameworkmentioning

confidence: 99%

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks

et al. 2019

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Modeling and simulation of molecular systems helps in understanding the behavioral mechanism of biological regulation. Time delays in production and degradation of expressions are important parameters in biological regulation. Constraints on time delays provide insight into the dynamical behavior of a Biological Regulatory Network (BRN). A recently introduced Process Hitting (PH) Framework has been found efficient in static analysis of large BRNs, however, it lacks the inference of time delays and thus determination of their constraints associated with the evolution of the expression levels of biological entities of BRN is not possible. In this paper we propose a Hybrid Process Hitting scheme for introducing time delays in Process Hitting Framework for dynamical modeling and analysis of Large Biological Regulatory Networks. It provides valuable insights into the time delays corresponding to the changes in the expression levels of biological entities thus possibly helping in identification of therapeutic targets. The proposed framework is applied to a well-known BRNs of Bacteriophage λ and ERBB Receptor-regulated G1/S transition involved in the breast cancer to demonstrate the viability of our approach. Using the proposed approach, we are able to perform goal-oriented reduction of the BRN and also determine the constraints on time delays characterizing the evolution (dynamics) of the reduced BRN.

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Reduction of Qualitative Models of Biological Networks for Transient Dynamics Analysis

Cited by 18 publications

References 41 publications

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks

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