1
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Nezamuldeen L, Jafri MS. Boolean Modeling of Biological Network Applied to Protein-Protein Interaction Network of Autism Patients. BIOLOGY 2024; 13:606. [PMID: 39194544 DOI: 10.3390/biology13080606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 08/01/2024] [Accepted: 08/06/2024] [Indexed: 08/29/2024]
Abstract
Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks' pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman's 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein-protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients' genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals.
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Affiliation(s)
- Leena Nezamuldeen
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
- King Fahd Medical Research Centre, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohsin Saleet Jafri
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
- Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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2
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Weidner FM, Ikonomi N, Werle SD, Schwab JD, Kestler HA. GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks. Bioinformatics 2024; 40:btae007. [PMID: 38195862 DOI: 10.1093/bioinformatics/btae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/23/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024] Open
Abstract
MOTIVATION Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community. RESULTS Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer. AVAILABILITY AND IMPLEMENTATION https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/.
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Affiliation(s)
- Felix M Weidner
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Silke D Werle
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
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3
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Werle SD, Ikonomi N, Lausser L, Kestler AMTU, Weidner FM, Schwab JD, Maier J, Buchholz M, Gress TM, Kestler AMR, Kestler HA. A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective. NPJ Syst Biol Appl 2023; 9:22. [PMID: 37270586 DOI: 10.1038/s41540-023-00283-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/17/2023] [Indexed: 06/05/2023] Open
Abstract
Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.
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Affiliation(s)
- Silke D Werle
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Ludwig Lausser
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
- Faculty of Computer Science, Technische Hochschule Ingolstadt, 85049, Ingolstadt, Germany
| | | | - Felix M Weidner
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Julia Maier
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
- Institute of Pathology, University Hospital Ulm, 89081, Ulm, Germany
| | - Malte Buchholz
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043, Marburg, Germany
| | - Thomas M Gress
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043, Marburg, Germany
| | | | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany.
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4
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Pušnik Ž, Mraz M, Zimic N, Moškon M. Review and assessment of Boolean approaches for inference of gene regulatory networks. Heliyon 2022; 8:e10222. [PMID: 36033302 PMCID: PMC9403406 DOI: 10.1016/j.heliyon.2022.e10222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/22/2022] [Accepted: 08/03/2022] [Indexed: 10/25/2022] Open
Abstract
Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.
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Affiliation(s)
- Žiga Pušnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Mraz
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Nikolaj Zimic
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Moškon
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
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5
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Discrete Logic Modeling of Cell Signaling Pathways. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2488:159-181. [PMID: 35347689 DOI: 10.1007/978-1-0716-2277-3_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Cell signaling pathways often crosstalk generating complex biological behaviors observed in different cellular contexts. Frequently, laboratory experiments focus on a few putative regulators, alone unable to predict the molecular mechanisms behind the observed phenotypes. Here, systems biology complements these approaches by giving a holistic picture to complex signaling crosstalk. In particular, Boolean network models are a meaningful tool to study large network behaviors and can cope with incomplete kinetic information. By introducing a model describing pathways involved in hematopoietic stem cell maintenance, we present a general approach on how to model cell signaling pathways with Boolean network models.
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6
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Werle SD, Schwab JD, Tatura M, Kirchhoff S, Szekely R, Diels R, Ikonomi N, Sipos B, Sperveslage J, Gress TM, Buchholz M, Kestler HA. Unraveling the Molecular Tumor-Promoting Regulation of Cofilin-1 in Pancreatic Cancer. Cancers (Basel) 2021; 13:725. [PMID: 33578795 PMCID: PMC7916621 DOI: 10.3390/cancers13040725] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/26/2021] [Accepted: 02/07/2021] [Indexed: 12/24/2022] Open
Abstract
Cofilin-1 (CFL1) overexpression in pancreatic cancer correlates with high invasiveness and shorter survival. Besides a well-documented role in actin remodeling, additional cellular functions of CFL1 remain poorly understood. Here, we unraveled molecular tumor-promoting functions of CFL1 in pancreatic cancer. For this purpose, we first show that a knockdown of CFL1 results in reduced growth and proliferation rates in vitro and in vivo, while apoptosis is not induced. By mechanistic modeling we were able to predict the underlying regulation. Model simulations indicate that an imbalance in actin remodeling induces overexpression and activation of CFL1 by acting on transcription factor 7-like 2 (TCF7L2) and aurora kinase A (AURKA). Moreover, we could predict that CFL1 impacts proliferation and apoptosis via the signal transducer and activator of transcription 3 (STAT3). These initial model-based regulations could be substantiated by studying protein levels in pancreatic cancer cell lines and human datasets. Finally, we identified the surface protein CD44 as a promising therapeutic target for pancreatic cancer patients with high CFL1 expression.
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Affiliation(s)
- Silke D. Werle
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Julian D. Schwab
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Marina Tatura
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Sandra Kirchhoff
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Robin Szekely
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Ramona Diels
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Bence Sipos
- Institute of Pathology, University of Tübingen, 72076 Tübingen, Germany; (B.S.); (J.S.)
| | - Jan Sperveslage
- Institute of Pathology, University of Tübingen, 72076 Tübingen, Germany; (B.S.); (J.S.)
| | - Thomas M. Gress
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Malte Buchholz
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
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7
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Ikonomi N, Kühlwein SD, Schwab JD, Kestler HA. Awakening the HSC: Dynamic Modeling of HSC Maintenance Unravels Regulation of the TP53 Pathway and Quiescence. Front Physiol 2020; 11:848. [PMID: 32848827 PMCID: PMC7411231 DOI: 10.3389/fphys.2020.00848] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/24/2020] [Indexed: 12/22/2022] Open
Abstract
Hematopoietic stem cells (HSCs) provide all types of blood cells during the entire life of the organism. HSCs are mainly quiescent and can eventually enter the cell cycle to differentiate. HSCs are maintained and tightly regulated in a particular environment. The stem cell niche regulates dormancy and awakening. Deregulations of this interplay can lead to hematopoietic failure and diseases. In this paper, we present a Boolean network model that recapitulates HSC regulation in virtue of external signals coming from the niche. This Boolean network integrates and summarizes the current knowledge of HSC regulation and is based on extensive literature research. Furthermore, dynamic simulations suggest a novel systemic regulation of TP53 in homeostasis. Thereby, our model indicates that TP53 activity is balanced depending on external stimulations, engaging a regulatory mechanism involving ROS regulators and RAS activated transcription factors. Finally, we investigated different mouse models and compared them to in silico knockout simulations. Here, the model could recapitulate in vivo observed behaviors and thus sustains our results.
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Affiliation(s)
- Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Silke D Kühlwein
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
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8
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Schwab JD, Kühlwein SD, Ikonomi N, Kühl M, Kestler HA. Concepts in Boolean network modeling: What do they all mean? Comput Struct Biotechnol J 2020; 18:571-582. [PMID: 32257043 PMCID: PMC7096748 DOI: 10.1016/j.csbj.2020.03.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/27/2020] [Accepted: 03/01/2020] [Indexed: 12/02/2022] Open
Abstract
Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models.
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Affiliation(s)
- Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Silke D Kühlwein
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Michael Kühl
- Institute of Biochemistry and Molecular Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
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9
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Groß A, Kracher B, Kraus JM, Kühlwein SD, Pfister AS, Wiese S, Luckert K, Pötz O, Joos T, Van Daele D, De Raedt L, Kühl M, Kestler HA. Representing dynamic biological networks with multi-scale probabilistic models. Commun Biol 2019; 2:21. [PMID: 30675519 PMCID: PMC6336720 DOI: 10.1038/s42003-018-0268-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 12/07/2018] [Indexed: 12/26/2022] Open
Abstract
Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.
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Affiliation(s)
- Alexander Groß
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Barbara Kracher
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Johann M. Kraus
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Silke D. Kühlwein
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Astrid S. Pfister
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Sebastian Wiese
- Core Unit Mass Spectrometry and Proteomics, Ulm University, 89081 Ulm, Germany
| | - Katrin Luckert
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Oliver Pötz
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Thomas Joos
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Dries Van Daele
- Department of Computer Science, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Luc De Raedt
- Department of Computer Science, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Michael Kühl
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
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10
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Schwab JD, Kestler HA. Automatic Screening for Perturbations in Boolean Networks. Front Physiol 2018; 9:431. [PMID: 29740342 PMCID: PMC5928136 DOI: 10.3389/fphys.2018.00431] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 04/06/2018] [Indexed: 12/15/2022] Open
Abstract
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior—so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.
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Affiliation(s)
- Julian D Schwab
- Medical Faculty, Institute of Medical Systems Biology Ulm University, Ulm, Germany.,International Graduate School of Molecular Medicine Ulm University, Ulm, Germany
| | - Hans A Kestler
- Medical Faculty, Institute of Medical Systems Biology Ulm University, Ulm, Germany
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11
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A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells. PLoS One 2018; 13:e0195126. [PMID: 29596489 PMCID: PMC5875862 DOI: 10.1371/journal.pone.0195126] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 03/16/2018] [Indexed: 12/29/2022] Open
Abstract
Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.
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12
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ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming. Sci Rep 2018; 8:3554. [PMID: 29476134 PMCID: PMC5824948 DOI: 10.1038/s41598-018-22031-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 02/15/2018] [Indexed: 12/14/2022] Open
Abstract
Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.
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Schwab JD, Siegle L, Kühlwein SD, Kühl M, Kestler HA. Stability of Signaling Pathways during Aging-A Boolean Network Approach. BIOLOGY 2017; 6:E46. [PMID: 29258225 PMCID: PMC5745451 DOI: 10.3390/biology6040046] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/10/2017] [Accepted: 12/14/2017] [Indexed: 12/11/2022]
Abstract
Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF- κ B signaling is a crucial pathway in the process of aging. The NF- κ B system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes-for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF- κ B signaling in aged organisms in comparison to young ones.
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Affiliation(s)
- Julian Daniel Schwab
- Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.
- International Graduate School of Molecular Medicine, Ulm University, 89069 Ulm, Germany.
| | - Lea Siegle
- Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.
- International Graduate School of Molecular Medicine, Ulm University, 89069 Ulm, Germany.
| | - Silke Daniela Kühlwein
- Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.
- International Graduate School of Molecular Medicine, Ulm University, 89069 Ulm, Germany.
| | - Michael Kühl
- Institute of Biochemistry and Molecular Biology, Ulm University, 89069 Ulm, Germany.
| | - Hans Armin Kestler
- Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.
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Yeheskel A, Reiter A, Pasmanik-Chor M, Rubinstein A. Simulation and visualization of multiple KEGG pathways using BioNSi. F1000Res 2017; 6:2120. [PMID: 29946422 PMCID: PMC6008849 DOI: 10.12688/f1000research.13254.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/04/2018] [Indexed: 12/18/2022] Open
Abstract
Motivation: Many biologists are discouraged from using network simulation tools because these require manual, often tedious network construction. This situation calls for building new tools or extending existing ones with the ability to import biological pathways previously deposited in databases and analyze them, in order to produce novel biological insights at the pathway level. Results: We have extended a network simulation tool (BioNSi), which now allows merging of multiple pathways from the KEGG pathway database into a single, coherent network, and visualizing its properties. Furthermore, the enhanced tool enables loading experimental expression data into the network and simulating its dynamics under various biological conditions or perturbations. As a proof of concept, we tested two sets of published experimental data, one related to inflammatory bowel disease condition and the other to breast cancer treatment. We predict some of the major observations obtained following these laboratory experiments, and provide new insights that may shed additional light on these results. Tool requirements: Cytoscape 3.x, JAVA 8 Availability: The tool 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)
- Adva Yeheskel
- Bioinformatics unit, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Adam Reiter
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | | | - Amir Rubinstein
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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