Cancer Systems Biology: Current Achievements in – Omics Data Analysis, Network Reconstruction and Mathematical Modelling


Systems biology is a multidisciplinary methodology in which quantitative biological experimental data are dissected using mathematical modelling and other computational and network biology tools, aiming at understanding the structure, function and dynamical regulation of biochemical networks. Systems biology will play a major role in the future molecular and clinical oncology because (a) it can be used for the analysis of cancer‐relevant high‐throughput data, (b) it provides tools for the reconstruction of the large multilevel regulatory networks that govern critical cancer phenotypes and (c) it is necessary when investigating cancer‐relevant networks holding multiple overlapping nonlinear regulatory motifs like feedback and feedforward loops. Here, the value of the systems biology approach in handling cancer genomics and transcriptomics data, the reconstruction of cancer networks and the use of mathematical modelling in the elucidation of cancer networks as well as in the design of anticancer therapies are discusses. Some recent case studies as proof of principle are highlighted.

Key Concepts:

  • Systems biology is a multidisciplinary approach that involves the analysis of quantitative experimental data via mathematical modelling and computational and network biology.

  • Modelling of cancer‐related networks is necessary because these networks display multiple nonlinear regulatory motifs like feedback loops.

  • Mathematical models have been used to elucidate the structure, dynamics, dysregulation and crosstalk of cancer pathways like the JAK/STAT, p53, MAPK, NFkB and intrinsinc/extrinsic apoptosis signalling pathways, as well as in microRNA cancer regulation.

  • Multi‐scale mathematical models can accurately describe the spatial configuration of tumours, their crosstalk with the surrounding microenvironment and their role in physiological mechanisms like angiogenesis and immune system response.

  • Mathematical modelling has been used to assess and personalise conventional anticancer therapies, but it is also used in the detection of new anticancer drug targets and the development of combined or immune therapies.

  • Cancer bioinformatics facilitates efficient analyses and integration of diverse types of biomedical high‐throughput data for the elucidation of causes of tumour initiation, progression and metastasis but also for the management, storage and exchange of experimental and clinical data.

  • Cancer systems biology integrates algorithms, methodologies, software tools and web resources into customised analysis workflows to explore and understand the molecular mechanisms underlying this complex disease.

  • Data integration and analysis is used to identify novel robust and precise biomarkers, to identify network biomarkers and to design individual patient‐tailored therapeutic interventions.

Keywords: anticancer therapies; biochemical regulatory networks; feedback and feedforward loops; drug target detection; multi‐scale cancer modelling; miRNA regulation; cancer genomics; cancer transcriptomics; patient stratification

Figure 1.

Sketch of the standard workflow followed for mathematical modelling in cancer systems biology.

Figure 2.

Depicted is an exemplary workflow in cancer bioinformatics. It illustrates that the integration of a wide range of approaches and resources is necessary to better understand the molecular mechanisms underlying the complex disease of cancer as well as for the diagnosis, prognosis and the design of personalised therapies.



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Vera, Julio, Wolkenhauer, Olaf, and Schmitz, Ulf(Aug 2014) Cancer Systems Biology: Current Achievements in – Omics Data Analysis, Network Reconstruction and Mathematical Modelling. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0025237]