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Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean Network Sketches: A Unifying Framework for Logical Model Inference. Bioinformatics 2023; 39:7099622. [PMID: 37004199 PMCID: PMC10122605 DOI: 10.1093/bioinformatics/btad158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
MOTIVATION The problem of model inference is of fundamental importance to systems biology. Logical models (e.g., Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. RESULTS We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network's topology and the update logic (obtained through, e.g., a biological knowledge base or a literature search), as well as further assumptions about the properties of the network's transitions (e.g., the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an initial sketch which is extended by adding restrictions representing experimental data to a data-informed sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data. AVAILABILITY All software and data are freely available as a reproducible artefact at https://doi.org/10.5281/zenodo.7688740. SUPPLEMENTARY INFORMATION Supplementary data available online through Bioinformatics.
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Affiliation(s)
- Nikola Beneš
- Faculty of Informatics, Brno, 602 00, Czech Republic
| | - Luboš Brim
- Faculty of Informatics, Brno, 602 00, Czech Republic
| | - Ondřej Huvar
- Faculty of Informatics, Brno, 602 00, Czech Republic
| | - Samuel Pastva
- Institute of Science and Technology Austria, Klosterneuburg, 3400, Austria
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Troják M, Šafránek D, Pastva S, Brim L. Rule-based modelling of biological systems using regulated rewriting. Biosystems 2023; 225:104843. [PMID: 36736686 DOI: 10.1016/j.biosystems.2023.104843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/03/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
In systems biology, models play a crucial role in understanding studied systems. There are many modelling approaches, among which rewriting systems provide a framework for describing systems on a mechanistic level. Describing biochemical processes often requires incorporating knowledge on an abstract level to simplify the system description or substitute the missing details. For this purpose, we present regulation mechanisms, an extension of this formalism with additional controls on the rewriting process. We introduce several regulation mechanisms and apply them to a rule-based language, a notation suitable for modelling biological phenomena. Finally, we demonstrate the usage of such regulations on several case studies from the biochemical domain.
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Affiliation(s)
- Matej Troják
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic.
| | - David Šafránek
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Samuel Pastva
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Luboš Brim
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
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Beneš N, Brim L, Huvar O, Pastva S, Šafránek D, Šmijáková E. AEON.py: Python Library for Attractor Analysis in Asynchronous Boolean Networks. Bioinformatics 2022; 38:4978-4980. [DOI: 10.1093/bioinformatics/btac624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/02/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Summary
AEON.py is a Python library for the analysis of the long-term behaviour in very large asynchronous Boolean networks. It provides significant computational improvements over the state of the art methods for attractor detection. Furthermore, it admits the analysis of partially specified Boolean networks with uncertain update functions. It also includes techniques for identifying viable source-target control strategies and the assessment of their robustness with respect to parameter perturbations.
Availability and Implementation
All relevant results are available in supplementary materials. The tool is accessible through https://github.com/sybila/biodivine-aeon-py.
Supplementary information
Supplementary data are available online through Bioinformatics.
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Affiliation(s)
- Nikola Beneš
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
| | - Luboš Brim
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
| | - Ondřej Huvar
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
| | - Samuel Pastva
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
| | - David Šafránek
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
| | - Eva Šmijáková
- Masaryk University Faculty of Informatics, , Brno, 602 00, Czech Republic
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Beneš N, Brim L, Kadlecaj J, Pastva S, Šafránek D. Exploring attractor bifurcations in Boolean networks. BMC Bioinformatics 2022; 23:173. [PMID: 35546394 PMCID: PMC9092939 DOI: 10.1186/s12859-022-04708-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/19/2022] [Indexed: 11/10/2022] Open
Abstract
Background Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. Results In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. Conclusions The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.
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Affiliation(s)
- Nikola Beneš
- Faculty of Informatics, Masaryk University, Brno, Czechia.
| | - Luboš Brim
- Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Jakub Kadlecaj
- Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Samuel Pastva
- Faculty of Informatics, Masaryk University, Brno, Czechia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, Czechia
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Troják M, Šafránek D, Mertová L, Brim L. Executable biochemical space for specification and analysis of biochemical systems. PLoS One 2020; 15:e0238838. [PMID: 32915842 PMCID: PMC7485897 DOI: 10.1371/journal.pone.0238838] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 08/25/2020] [Indexed: 01/28/2023] Open
Abstract
Computational systems biology provides multiple formalisms for modelling of biochemical processes among which the rule-based approach is one of the most suitable. Its main advantage is a compact and precise mechanistic description of complex processes. However, state-of-the-art rule-based languages still suffer several shortcomings that limit their use in practice. In particular, the elementary (low-level) syntax and semantics of rule-based languages complicate model construction and maintenance for users outside computer science. On the other hand, mathematical models based on differential equations (ODEs) still make the most typical used modelling framework. In consequence, robust re-interpretation and integration of models are difficult, thus making the systems biology paradigm technically challenging. Though several high-level languages have been developed at the top of rule-based principles, none of them provides a satisfactory and complete solution for semi-automated description and annotation of heterogeneous biophysical processes integrated at the cellular level. We present the second generation of a rule-based language called Biochemical Space Language (BCSL) that combines the advantages of different approaches and thus makes an effort to overcome several problems of existing solutions. BCSL relies on the formal basis of the rule-based methodology while preserving user-friendly syntax of plain chemical equations. BCSL combines the following aspects: the level of abstraction that hides structural and quantitative details but yet gives a precise mechanistic view of systems dynamics; executable semantics allowing formal analysis and consistency checking at the level of the language; universality allowing the integration of different biochemical mechanisms; scalability and compactness of the specification; hierarchical specification and composability of chemical entities; and support for genome-scale annotation.
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Affiliation(s)
- Matej Troják
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - David Šafránek
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Lukrécia Mertová
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Luboš Brim
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
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Abstract
We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
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Affiliation(s)
- Milan Česka
- Systems Biology Laboratory at Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - David Šafránek
- Systems Biology Laboratory at Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Sven Dražan
- Systems Biology Laboratory at Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Luboš Brim
- Systems Biology Laboratory at Faculty of Informatics, Masaryk University, Brno, Czech Republic
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Safránek D, Cervený J, Klement M, Pospíšilová J, Brim L, Lazár D, Nedbal L. E-photosynthesis: web-based platform for modeling of complex photosynthetic processes. Biosystems 2010; 103:115-24. [PMID: 21073914 DOI: 10.1016/j.biosystems.2010.10.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2010] [Revised: 10/22/2010] [Accepted: 10/23/2010] [Indexed: 10/18/2022]
Abstract
E-photosynthesis framework is a web-based platform for modeling and analysis of photosynthetic processes. Compared to its earlier version, the present platform employs advanced software methods and technologies to support an effective implementation of vastly diverse kinetic models of photosynthesis. We report on the first phase implementation of the tool new version and demonstrate the functionalities of model visualization, presentation of model components, rate constants, initial conditions and of model annotation. The demonstration also includes export of a model to the Systems Biology Markup Language format and remote numerical simulation of the model.
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Affiliation(s)
- David Safránek
- Systems Biology Laboratory, Faculty of Informatics Masaryk University, Botanická 68a, CZ-60200 Brno, Czech Republic
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Barnat J, Brim L, Safranek D. High-performance analysis of biological systems dynamics with the DiVinE model checker. Brief Bioinform 2010; 11:301-12. [DOI: 10.1093/bib/bbp074] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Brim L, Kiefer K, Hanson G, Fujimoto B. Are media just media? (Part II). Am Biotechnol Lab 1991; 9:33-5. [PMID: 1367557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Affiliation(s)
- L Brim
- HyClone Laboratories, Inc., Logan, Utah
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