| Reference : Applications of Boolean modelling to study and stratify dynamics of a complex disease |
| Dissertations and theses : Doctoral thesis | |||
| Life sciences : Multidisciplinary, general & others | |||
| Systems Biomedicine | |||
| http://hdl.handle.net/10993/54360 | |||
| Applications of Boolean modelling to study and stratify dynamics of a complex disease | |
| English | |
Hemedan, Ahmed  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >] | |
| 30-Jan-2023 | |
| Ahmed Hemedan, Esch-sur-Alzette, Luxembourg | |
| DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN BIOLOGIE | |
| 181 | |
| Schneider, Reinhard | |
| [en] Boolean modelling ; Modelling formats ; Systems Biology standards | |
| [en] Interpretation of omics data is needed to form meaningful hypotheses about
disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into their complexity. Similarly to pathway databases, mathematical models are stored and shared on dedicated platforms. Moreover, community-driven initiatives such as disease maps encode disease-specific mechanisms in both computable and diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms, computationally readable content can be used as a scaffold for building dynamic models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. In this study, Parkinson’s disease (PD) is analyzed as an example of a complex disorder. PD is associated with complex genetic, environmental causes and comorbidities that need to be analysed in a systematic way to better understand the progression of different disease subtypes. Studying PD as a multifactorial disease requires deconvoluting the multiple and overlapping changes to identify the driving neurodegenerative mechanisms. Integrated systems analysis and modelling can enable us to study different aspects of a disease such as progression, diagnosis, and response to therapeutics. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. Modelling such complex processes depends on the scope and it may vary depending on the nature of the process (e.g. signalling vs metabolic). Experimental design and the resulting data also influence model structure and analysis. Boolean modelling is proposed to analyse the complexity of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative and do not require detailed kinetic information such as Petri nets or Ordinary Differential equations (ODEs). Boolean modelling represents a logical formalism where available variables have binary values of one (ON) or zero (OFF), making it a plausible approach in cases where quantitative details and kinetic parameters 9 are not available. Boolean modelling is well validated in clinical and translational medicine research. In this project, the PD map was translated into BMs in an automated fashion using different methods. Therefore, the complexity of disease pathways can be analysed by simulating the effect of genomic burden on omics data. In order to make sure that BMs accurately represent the biological system, validation was performed by simulating models at different scales of complexity. The behaviour of the models was compared with expected behavior based on validated biological knowledge. The TCA cycle was used as an example of a well-studied simple network. Different scales of complex signalling networks were used including the Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched and mismatched behaviours were identified, allowing the models to be modified to better represent disease mechanisms. The BMs were stratified by integrating omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and specific responses to molecular perturbations. The results can support research hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in general were discussed, as well as those specific to the current study. Based on the results, there are different ways to improve Boolean modelling applications. Modellers can perform exploratory investigations, gathering the associated information about the model from literature and data resources. The missing details can be inferred by integrating omics data, which identifies missing components and optimises model accuracy. Accurate and computable models improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of models in easily interoperable formats are also important. | |
| Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) | |
| This work was supported by the funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 733100: SYSCID - A systems medicine approach to chronic inflammatory diseases. | |
| Boolean modelling as a logic-based dynamic approach in systems medicine | |
| Researchers ; Professionals ; Students ; General public ; Others | |
| http://hdl.handle.net/10993/54360 |
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