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Barbosa S, Niebel B, Wolf S, Mauch K, Takors R. A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints. Biosystems 2018; 174:37-48. [PMID: 30312740 DOI: 10.1016/j.biosystems.2018.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 10/05/2018] [Accepted: 10/08/2018] [Indexed: 02/07/2023]
Abstract
The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
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Affiliation(s)
- Sara Barbosa
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany.
| | - Bastian Niebel
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Sebastian Wolf
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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Paroni A, Graudenzi A, Caravagna G, Damiani C, Mauri G, Antoniotti M. CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks. BMC Bioinformatics 2016; 17:64. [PMID: 26846964 PMCID: PMC4743236 DOI: 10.1186/s12859-016-0914-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 01/27/2016] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing. RESULTS We here introduce CABERNET, a Cytoscape app for the generation, simulation and analysis of Boolean models of GRNs, specifically focused on their augmentation when a only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABERNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science. CONCLUSIONS CABERNET offers a series of innovative simulation and modeling functions and tools, including (but not being limited to) the dynamical characterization of the gene activation patterns ruling cell types and differentiation fates, and sophisticated robustness assessments, as in the case of gene knockouts. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABERNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist. An example application concerning the analysis of an augmented T-helper cell GRN is provided.
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Affiliation(s)
- Andrea Paroni
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy
| | - Alex Graudenzi
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Via F.lli Cervi, 93, Segrate, I-20090, (MI), Italy.
| | - Giulio Caravagna
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK.
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy, Viale Sarca 336, Milan, I-20126, Italy.
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Via F.lli Cervi, 93, Segrate, I-20090, (MI), Italy. .,SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy, Viale Sarca 336, Milan, I-20126, Italy.
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Milan Center for Neuroscience, University of Milan-Bicocca, Milan, Italy.
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Graudenzi A, Caravagna G, De Matteis G, Antoniotti M. Investigating the relation between stochastic differentiation, homeostasis and clonal expansion in intestinal crypts via multiscale modeling. PLoS One 2014; 9:e97272. [PMID: 24869488 PMCID: PMC4037186 DOI: 10.1371/journal.pone.0097272] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Accepted: 04/16/2014] [Indexed: 12/24/2022] Open
Abstract
Colorectal tumors originate and develop within intestinal crypts. Even though some of the essential phenomena that characterize crypt structure and dynamics have been effectively described in the past, the relation between the differentiation process and the overall crypt homeostasis is still only partially understood. We here investigate this relation and other important biological phenomena by introducing a novel multiscale model that combines a morphological description of the crypt with a gene regulation model: the emergent dynamical behavior of the underlying gene regulatory network drives cell growth and differentiation processes, linking the two distinct spatio-temporal levels. The model relies on a few a priori assumptions, yet accounting for several key processes related to crypt functioning, such as: dynamic gene activation patterns, stochastic differentiation, signaling pathways ruling cell adhesion properties, cell displacement, cell growth, mitosis, apoptosis and the presence of biological noise. We show that this modeling approach captures the major dynamical phenomena that characterize the regular physiology of crypts, such as cell sorting, coordinate migration, dynamic turnover, stem cell niche correct positioning and clonal expansion. All in all, the model suggests that the process of stochastic differentiation might be sufficient to drive the crypt to homeostasis, under certain crypt configurations. Besides, our approach allows to make precise quantitative inferences that, when possible, were matched to the current biological knowledge and it permits to investigate the role of gene-level perturbations, with reference to cancer development. We also remark the theoretical framework is general and may be applied to different tissues, organs or organisms.
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Affiliation(s)
- Alex Graudenzi
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
- * E-mail:
| | - Giulio Caravagna
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
| | - Giovanni De Matteis
- Department of Mathematics and Information Sciences, Northumbria University, Newcastle, United Kingdom
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
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