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FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods
Fatemeh Mansoori1, Maseud Rahgozar2, Kaveh Kavousi3
1Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Background:
Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.
Results:
In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes.
Conclusions:
The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.
Insights
Formal model checking based pathway analysis (FoPA) offers a novel approach to identify perturbed signaling pathways. FoPA demonstrates improved accuracy and reduced false positives compared to existing methods, highlighting the Renin-angiotensin system
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Accurate identification of perturbed signaling pathways is crucial for understanding diseases and identifying drug targets.
- Current pathway analysis methods often use graph-based models that limit the description of complex biological interactions.
- Existing models may struggle to capture the full spectrum of dependencies within biological pathways, potentially leading to higher false-positive rates.
Purpose of the Study:
- To introduce a novel approach for pathway analysis using formal modeling and model checking.
- To develop a tool, Formal model checking based pathway analysis (FoPA), for more accurate pathway perturbation identification.
- To compare FoPA's performance against established methods using benchmark datasets and a target pathway technique.
Main Methods:
- Signaling pathways are formally modeled using mathematical formalisms.
- Model checking tools are employed to assess the likelihood of pathway perturbation under specific conditions.
- FoPA is validated against PADOG, CePa, and SPIA using 36 GEO datasets and simulated negative controls to evaluate accuracy and false-positive rates.
Main Results:
- FoPA demonstrates comparable or superior performance in prioritizing target pathways against PADOG, CePa, and SPIA.
- The proposed method exhibits a lower false-positive rate in identifying significant pathways.
- FoPA successfully identified more consistent relevant pathways across various disease datasets.
Conclusions:
- FoPA provides a more accurate and reliable method for signaling pathway analysis.
- The tool effectively reduces false positives and enhances the detection of biologically relevant pathways.
- Application to autism-related genes suggests a potential role for the Renin-angiotensin system pathway in autism development, warranting further investigation.
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