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ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.
Osama Shiraz Shah1, Muhammad Faizyab Ali Chaudhary1, Hira Anees Awan1
1Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, 54792, Pakistan.
We developed ATLANTIS, a toolbox for analyzing biological networks to predict cell fates and screen cancer therapies. This method helps understand disease mechanisms and guide treatments by simulating network reprogramming.
Area of Science:
- Systems Biology
- Computational Biology
- Cancer Research
Background:
- Boolean modeling abstracts cellular biomolecular regulation.
- Understanding network states and cell fate is crucial for disease research, including cancer.
- Attractor landscape analysis visualizes complex network behavior and state propensities.
Purpose of the Study:
- To introduce the Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery and network reprogramming.
- To enable both deterministic and probabilistic analyses of biomolecular networks.
- To validate ATLANTIS using published case studies and screen therapeutic targets in cancer cell lines.
Main Methods:
- Boolean modeling of biological networks.
- Attractor landscape analysis to represent network states and propensities.
- Deterministic and probabilistic computational analyses within the ATLANTIS toolbox.
Main Results:
- ATLANTIS successfully reconstructed attractor landscapes in validation case studies.
- The toolbox predicted cell fate reprogramming upon simulated therapeutic interventions.
- Screening of the HCT-116 colorectal cancer cell line identified potential drug targets with validated efficacy.
Conclusions:
- ATLANTIS demonstrates significant potential for in silico cell fate prediction and therapeutic screening.
- The toolbox can guide single or combinatorial therapies to reprogram biomolecular networks and restore normal cell fates.
- ATLANTIS offers a valuable computational approach for advancing cancer research and personalized medicine.
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