3Moons: Modular, Multiscale, Oncology Networks

Project Summary

Cancer is a multifactorial disease with a striking heterogeneity due to genetic, epigenetic and transcriptional changes involving a myriad of genes and proteins. While these factors are relevant to clinical prognosis and medical treatment of patients, a system’s approach is needed to unravel the complexities underlying intertwining carcinogenesis mechanisms. Given accurate experimental measurements, the presence at multiple scales of stochastic dynamics involved in gene regulation and protein‐protein interactions (PPI) requires that both the analysis of differential (cancer versus normal) conditions and the treatment of the associated uncertainty are taken at combined omics‐scale levels. In particular, networks allow for the straightforward integration between molecular, genetic, clinical and topological features in a unifying context. Models can then be built from network‐embedded measurable values to assess the variation significantly affecting the cellular mechanisms involved in cancer. By treating cancer as a systems disease, powerful computational instruments become available, and especially network‐based inference can drive the translation of systems biology to systems medicine by shifting the focus on the clinical impact.

3Moons is designed to deal with critical network aspects summarized as follows:

1. Integrative Networks: A probabilistic approach—integrated with the next generation sequencing genome and transcriptome data—to uncover cancer‐coordinated activity at modular level and determine change‐of‐state in proteins. Impacts: Preventive medicine and therapy through the analysis of protein pathways to identify candidate markers and possible targets for drug development.

2. Multiscale Networks: A spectral approach to overcome the limited resolution allowed by most algorithms designed to discover modularity according to functionally coordinated genes or proteins.  Impacts: Elucidation of cancer signaling by examining temporal heterogeneity, i.e. permanent vs transient interactions due to slow (long lasting) vs fast (vanishing) dynamics, and propagation of fluctuations from small to increasingly larger scales.

3. Non‐extensive Networks: An entropy approach to deal with the complex multiomic dependencies and the related uncertainty arising from data non‐stationarities (pattern discontinuities or local clustering). Impacts: Identification of dysregulative and interactive cancer‐induced patterns through network‐adapted uncertainty and complexity measures such as non‐extensive ensemble entropies.

The budgeted cost for the project for the 2 years is a total of AED 398,000. There are many reasons for the success of the proposed project which include the collaboration of three very well established centers in the fields of bioinformatics and computational biology [United Arab Emirates University, College of Information Technology CIT‐UAEU (pictured below), CCS‐University of Miami, and Laboratory of Integrated Systems Medicine LISM‐CNR, Pisa, Italy]; The experience of the PI and the Co‐PIs in the field of cancer and bioinformatics; the availability of data, domain experts, and excellent computational facilities at the CIT-UAEU.

The project has important significance to UAE. The visibility of the project will put UAE at the forefront of bioinformatics innovation, driving standardization of the technologies and methods. The project will create specific cancer markers database which UAEU will leverage in future projects with a vision to make significant contributions to medicine and healthcare.


United Arab Emirates University College of Information Technology

Skip to toolbar