Mathematics has become essential in cancer biology. Recent developments in high-throughput molecular profiling techniques enable assessing molecular states of tumors in great detail. Cancer genome data are collected at a large scale in numerous clinical studies and in international consortia, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. Developing mathematical models that are consistent with and predictive of the true underlying biological mechanisms is a central goal of cancer biology. In this work, we used percolations and power-law models to study protein-protein interactions in cancer fusions. We used site-directed knockouts to understand the modular components of fusion protein-protein interaction networks, thereby providing models for target-based drug predictions.
|Title of host publication||Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings|
|Editors||Olga Valenzuela, Francisco Ortuño, Ignacio Rojas, Francisco Ortuño, Fernando Rojas|
|Number of pages||9|
|State||Published - 2019|
|Event||7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019 - Granada, Spain|
Duration: 8 May 2019 → 10 May 2019
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019|
|Period||8/05/19 → 10/05/19|
Bibliographical notePublisher Copyright:
© 2019, Springer Nature Switzerland AG.
- Fusion proteins
- Protein-protein interaction networks
- Site-directed percolations