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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells
Marco S Nobile1,2,3, Giuseppina Votta2,4, Roberta Palorini2,4
1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano 20126, Italy.
Motivation:
The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy.
Results:
We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments.
Availability And Implementation:
The source code of FUMOSO is available under the GPL 2.0 license on GitHub at the following URL: https://github.com/aresio/FUMOSO.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces a computational method to predict cellular system behaviors and identify key components for therapeutic intervention. The approach aids in understanding cancer cell responses to glucose depletion and suggests potential protective roles for protein kinase A.
Area of Science:
- Computational biology
- Systems biology
- Bioinformatics
Background:
- Elucidating dysfunctional cellular processes in disease is experimentally and computationally challenging.
- Understanding complex biological systems requires methods that can handle heterogeneity and parameter uncertainty.
Purpose of the Study:
- To develop a novel computational method combining fuzzy logic and global optimization.
- To predict system dynamics and identify critical components for targeted interventions.
- To facilitate experimental design for therapeutic strategies.
Main Methods:
- Coupling fuzzy logic modeling with a global optimization algorithm.
- Predicting emergent dynamical behaviors of heterogeneous systems.
- Determining minimal sets of components for desired system responses.
Main Results:
- Applied the method to K-ras-induced cancer cells exhibiting the Warburg effect under glucose depletion.
- Identified novel stimulus combinations to maximize pro-apoptotic processes.
- Provided evidence for a protective role of protein kinase A in cancer cells under stress.
Conclusions:
- The computational method can predict responses of complex systems to perturbations like drugs or mutations.
- Findings suggest potential therapeutic avenues by modulating identified pathways.
- The approach facilitates the development of novel therapeutic treatments in medicine and pharmacology.
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