Computational Cell Biology

“The book recommends itself as an optimal starting point for understanding the current state of the art of data driven analyses, applied on high throughput datasets … . The layout of the chapters makes them accessible to a wide variety of audiences from undergraduates, graduates and established researchers from a wide spectrum of fields from mathematics, computer science to biology and medicine. … this book is a must have for the current research in this field.” (Irina Ioana Mohorianu, zbMATH 1417.92006, 2019)

• Rule-based Models and Applications in Biology.- Optimized Protein-protein Interaction Network usage with Context Filtering.- SignaLink: Multi-layered Regulatory Networks.- Interplay between Long Non-coding RNAs and microRNAs in Cancer.- Methods and Tools in Genome-wide Association Studies.- Identifying Differentially Expressed Genes using Fluorescence-Activated Cell Sorting (FACS) and RNA Sequencing from Low Input Samples.- Computational and Experimental Approaches to Predict Host-parasite Protein-protein Interactions.- An Integrative Approach to Virus-host Protein-protein Interactions.- The SQUAD Method for the Qualitative Modeling of Regulatory Networks.- miRNet - functional Analysis and Visual Exploration of miRNA-target Interactions in a Network Context.- Systems Biology Analysis to Understand Regulatory miRNA Networks in Lung Cancer.- Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks.- Toward Large Scale Computational Prediction of Protein Complexes.- Computational Models of Cell Cycle Transitions.- Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC.- Computational Network Analysis for Drug Toxicity Prediction.- Modeling the Epigenetic Landscape in Plant Development.- Developing Network Models of Multi-scale Host Responses Involved in Infections and Diseases.- Exploring Dynamics and Noise in Gonadotropin-Releasing Hormone (GnRH) Signaling.