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Chen L, Kulasiri D, Samarasinghe S. A Novel Data-Driven Boolean Model for Genetic Regulatory Networks. Front Physiol 2018; 9:1328. [PMID: 30319440 PMCID: PMC6167558 DOI: 10.3389/fphys.2018.01328] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 09/03/2018] [Indexed: 11/30/2022] Open
Abstract
A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.
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Affiliation(s)
- Leshi Chen
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Don Kulasiri
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Sandhya Samarasinghe
- Integrated Systems Modelling Group, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
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Martins Conde PDR, Sauter T, Pfau T. Constraint Based Modeling Going Multicellular. Front Mol Biosci 2016; 3:3. [PMID: 26904548 PMCID: PMC4748834 DOI: 10.3389/fmolb.2016.00003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 01/25/2016] [Indexed: 12/31/2022] Open
Abstract
Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches.
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Affiliation(s)
- Patricia do Rosario Martins Conde
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of Luxembourg Luxembourg, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of Luxembourg Luxembourg, Luxembourg
| | - Thomas Pfau
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of LuxembourgLuxembourg, Luxembourg; Department of Physics, Institute of Complex Systems and Mathematical Biology, University of AberdeenAberdeen, UK
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Quasi-Steady-State Analysis based on Structural Modules and Timed Petri Net Predict System's Dynamics: The Life Cycle of the Insulin Receptor. Metabolites 2015; 5:766-93. [PMID: 26694479 PMCID: PMC4693194 DOI: 10.3390/metabo5040766] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 11/23/2015] [Accepted: 12/09/2015] [Indexed: 02/01/2023] Open
Abstract
The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states.
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Krumholz EW, Libourel IGL. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks. J Biol Chem 2015; 290:19197-207. [PMID: 26041773 DOI: 10.1074/jbc.m114.634121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Indexed: 11/06/2022] Open
Abstract
Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.
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Affiliation(s)
| | - Igor G L Libourel
- From the Department of Plant Biology and the Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota 55108
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Brandon M, Howard B, Lawrence C, Laubenbacher R. Iron acquisition and oxidative stress response in aspergillus fumigatus. BMC SYSTEMS BIOLOGY 2015; 9:19. [PMID: 25908096 PMCID: PMC4418068 DOI: 10.1186/s12918-015-0163-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 03/31/2015] [Indexed: 01/08/2023]
Abstract
BACKGROUND Aspergillus fumigatus is a ubiquitous airborne fungal pathogen that presents a life-threatening health risk to individuals with weakened immune systems. A. fumigatus pathogenicity depends on its ability to acquire iron from the host and to resist host-generated oxidative stress. Gaining a deeper understanding of the molecular mechanisms governing A. fumigatus iron acquisition and oxidative stress response may ultimately help to improve the diagnosis and treatment of invasive aspergillus infections. RESULTS This study follows a systems biology approach to investigate how adaptive behaviors emerge from molecular interactions underlying A. fumigatus iron regulation and oxidative stress response. We construct a Boolean network model from known interactions and simulate how changes in environmental iron and superoxide levels affect network dynamics. We propose rules for linking long term model behavior to qualitative estimates of cell growth and cell death. These rules are used to predict phenotypes of gene deletion strains. The model is validated on the basis of its ability to reproduce literature data not used in model generation. CONCLUSIONS The model reproduces gene expression patterns in experimental time course data when A. fumigatus is switched from a low iron to a high iron environment. In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions. Model simulations support the hypothesis that intracellular iron regulates A. fumigatus transcription factors, SreA and HapX, by a post-translational, rather than transcriptional, mechanism. Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress. This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.
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Affiliation(s)
- Madison Brandon
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, 400 Farmington Ave, Farmington, 06030, USA. .,Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, 06030, USA.
| | - Brad Howard
- Department of Biological Sciences, Virginia Tech, 1405 Perry Street, Blacksburg, 24061, USA. .,Virginia Bioinformatics Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg, 24061, US.
| | - Christopher Lawrence
- Department of Biological Sciences, Virginia Tech, 1405 Perry Street, Blacksburg, 24061, USA. .,Virginia Bioinformatics Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg, 24061, US.
| | - Reinhard Laubenbacher
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, 06030, USA. .,The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, 06030, USA. .,Department of Cell Biology, University of Connecticut Health Center, 263 Farmington Ave, Farmington, 06030, USA.
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Recent advances in the reconstruction of metabolic models and integration of omics data. Curr Opin Biotechnol 2014; 29:39-45. [DOI: 10.1016/j.copbio.2014.02.011] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 02/04/2014] [Accepted: 02/13/2014] [Indexed: 11/22/2022]
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