Cancer research in the field of Computational Systems Biology attempts to address
questions that will advance current knowledge in the mechanisms of cancer progression or …

Publisher Summary This chapter discusses systems-based approaches to translational
medicine. This is made possible by the convergent acceleration of several disciplines …

Making decisions on how best to treat cancer patients requires the integration of different
data sets, including genomic profiles, tumour histopathology, radiological images, proteomic …

The traditional bench-to-bedside pipeline involves using model systems and patient
samples to provide insights into pathways deregulated in cancer. This discovery reveals …

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network
analysis of the human transcriptome. We generated an atlas of communities in gene co …

Simple Summary Omics technologies and targeted drugs are revolutionizing the clinical
oncology landscape, portending the promise of precision oncology. However, our abilities to …

Every patient and every disease is different. Each patient therefore requires a personalized
treatment approach. For technical reasons, a personalized approach is feasible for treatment …

An important challenge facing the field is how better to translate in vitro discoveries to the
clinic. Computational systems biology approaches that use omic data to predict biology …

Current and emerging biomedical science efforts are driven by determining how to improve
clinical outcomes for patients, but the roadmap to achieve this goal has been controversial …

To reach the full potential of multicellular systems biology, mathematical and computational
modelers must pool their efforts to share and curate biophysical measurements, create and …