Systems Biology of MicroRNAs in Cancer

lt;p>Ulf Schmitz's research focus is on RNA biology and systems medicine. He integrates in silico, in vitro and in vivo approaches in advanced interdisciplinary research. His team develops integrative systems medicine workflows along with tools and databases to identify and study interactions between RNAs and other molecules, mechanisms of gene regulation, and gene regulatory networks.

With the goal of achieving a deeper understanding of processes involved in disease emergence and progression, his projects focus on different cancer entities and possible avenues for therapeutic interventions and optimized treatment schedules.

Olaf Wolkenhauer's research combines data-driven modelling with model-driven experimentation, using a wide range of approaches, including machine learning, statistics, systems theory, and stochastic processes. He has extensive experience in developing systems theoretic concepts and developing algorithms, workflows, standards and software tools that support the analysis data with applications in biomedicine. A specific interest is in understanding the functioning of cells within a tissue and how such whole-part relationships can be used to identify principles underlying tissue (mal)functioning.

Julio Vera-González is a physicist working in medical systems biology since 2005. His expertise is in mathematical modelling, bioinformatics and network biology. He applies multi-criteria decision algorithms to patient classification and therapy assessment. His primary interest is the role of (non-coding) RNA regulation in the interplay between cancer and the immune system, as well as its impact on (immuno)therapy.

Chapter 1. The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective.- Chapter 2. Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies.- Chapter 3. Regulation of Immune Cells by MicroRNAs and MicroRNA-Based Cancer Immunotherapy.- Chapter 4. Machine-Learning-Based Methods and Best Practices of MicroRNA-Target Prediction and Validation.- Chapter 5. Turning Data to Knowledge: Online Tools, Databases and Resources in MicroRNA Research.- Chapter 6. Bioinformatics Methods for Modeling MicroRNA Regulatory Networks in Cancer.- Chapter 7. Analysis of the p53/MicroRNA Network in Cancer.- Chapter 8. Machine Learning Using Gene-Sets to Infer miRNA Function.- Chapter 9. miRNA:miRNA Interactions: A Novel Mode of miRNA Regulation and Its Effect on Disease.- Chapter 10. ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively on Tumor Molecular Subgroups.- Chapter 11. 3D Modeling of Non-coding RNA Interactions.