| Grant number: | 23/14618-8 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | April 01, 2024 |
| End date: | January 31, 2025 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Ronaldo Fumio Hashimoto |
| Grantee: | Shantanu Gupta |
| Host Institution: | Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Abstract The main objective of current systems biology research is to understand biology at the systems level, systematically classifying all molecules and their interactions within a living cell. Network modeling is characterized by viewing cells at many different levels of detail in terms of their underlying network structure, which is the foundation of systems biology. For example, the modeling of multifactorial diseases, such as cancer, from the perspective of interaction networks of their components (biological system). In fact, cellular systems are disrupted during both cancer initiation and development, and changes in the behavior of tumor cells usually require a wide range of dynamic modifications. To an extent, recent developments in high-throughput technologies have reduced the difficulty of monitoring systemic change. Technological advances in microarray (or RNA-Seq, and currently single-cell RNA sequencing, scRNA-seq) and mass spectrometry, at both the transcriptomic and proteomic levels, along with computational simulations, have aided in the progress of identifying disease-associated genes. At the systems level, computational approaches developed for network analysis are becoming particularly useful for providing insight into the mechanisms behind tumor growth and metastasis. Therefore, in this work, we will propose machine learning and systems biology approaches such as dynamic Boolean network modeling and Markov chains. The main objective of this project is to understand and analyze the dynamics of the molecular levels of cells in a systematic and mechanistic way of the cells. In this way, we can capture some hidden dynamics, or signaling pathways, that are responsible for disease or may help fight disease. Interestingly, we will be using public databases such as GO, String, Kegg as well as PubMed. In this way, this project would help to uncover some hidden molecular mechanisms or signaling pathways related to cancer. (AU) | |
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