The group Networks and Systems Biology for Diseases focuses on untangling the genotype-phenotype relationships of human disorders. Disease complexity is the consequence of the intricate cellular functioning of genes and proteins. Indeed, biological molecules do not act alone, but rather interact with each other to perform their functions in protein complexes, signalling pathways or metabolic reactions. The disease phenotypes are thus resulting not from perturbations of isolated genes or proteins, but of complex networks of molecular interactions.

Systems Biology, which consider biological systems as networks of interactions between biological components, offer a new approach to link the genotypes (e.g., the genes and the proteins, as the components of the systems) to the phenotypes (the merging properties of the systems), and in particular the diseased phenotypes. Our group is active in the fields of systems biology with both the development of mathematical and computational tools grounded on network theory and their application to biological questions, with the ultimate goal of untangling the genotype-phenotype relationships of human disorders.

Our group works in close collaboration with the MABIOS team from the Marseille Mathematics Institute.

Axis 1 Methodological developments: Systems Biology and Network Theory

Large-Scale Interactomes and Multiplex Networks

Thanks to the scaling of the experimental techniques allowing interaction discovery, recent years have witnessed the accumulation of thousands of physical and functional interactions of various nature. These interaction data are usually represented as networks, but as biological interaction sources are diverse, some networks contain few interactions of high relevance, while others, prone to noise, contain thousands or even millions of interactions. Overall, -omics interaction data offer unprecedented opportunities to study disease-associated cellular perturbations. However, while their production is becoming easier and more cost-effective, interpretation and integration of these diverse data still face major challenges.

We are involved for many years in the development of tools to extract knowledge from large-scale interactomes. We develop in particular clustering algorithms for community identification. This leading network approach, revealing subnetwork communities of tightly linked genes and proteins, provides access to the landscape of cellular functions. Recently, we extended these approaches to multiplex networks, i.e., networks composed of many layers of physical or functional interactions between genes and proteins.
We are also working on guilt-by-association strategies to study gene and protein functions. In particular, we are now extending the random walk with restart algorithm to multiplex networks.

Integrative Biology: -omics data integration
One of the goals of the group is to develop tools to use networks as scaffold to integrate other type of large-scale -omics data. We aim at combining heterogeneous datasets, such as interactions, mutation and expression. In particular, we work to contextualize networks, and to predict the activity status of cellular biological processes in the different tissues.
Dynamical Network Modeling
We are also involved in the development of mathematical models to study human diseases. To build a model, we start by constructing a regulatory graph encompassing activating and inhibiting interactions between genes and proteins of interest (e.g., genes mutated or pathway implicated in diseases). The logical formalism then allows creating explanatory and predictive models from the regulatory graph, to study the dynamics of biological systems behaviors in healthy or diseased contexts.

Axis 2 Applications to untangle human disorders


Thanks to network modeling and large-scale interactome and -omics mining, our group study genes and proteins functioning in their cellular contexts, towards a better understanding of their perturbations in diseases.

Common diseases
In common diseases, such as cancers, many factors are jointly contributing to the disease emergence and phenotype. These diseases are then often studied thanks to networks, to understand the combined effect of genes, proteins and their interactions. We are involved for many years in the application of network approaches to study the functions of the genes and proteins implicated in cancer.


Rare diseases
Rare diseases are also associated to a very high heterogeneity and complexity. In this context, we study the network vicinity of disease genes to identify modifier genes and understand the influence of the genetic background. We are also interested in developing diagnosis tools based on network approaches, for instance to rank variants according to their proximities with genes whose mutations lead to diseases with similar phenotypes. Finally, the group is working on drug repurposing strategies, leveraging networks to integrate the many-to-many relationships between drugs and targets.


Disease relationships and comorbidities
We are also interested in the investigation of disease-disease molecular and comorbidity relationships. The group will develop analyses of disease-disease relationships in the context of the rare and common diseases. Indeed, higher-than-expected association of rare diseases with apparently common multifactorial diseases is often observed, as well as, in some cases, lower-than-expected associations.

Valdeolivas, A.  et al. 2018

Random Walk with Restart on Multiplex and Heterogeneous Biological Networks

Motivation: Recentyears have witnessed anexponentialgrowthin thenumberof identified interactions between biological molecules. These interactions are usually represented as large and complex networks,...
Bioinformatics - issue: - volume: - pages: .

Sánchez-Valle, J.  et al. 2017

A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the...
Sci Rep - issue: 1 - volume: 7 - pages: 4474.

Tabarés-Seisdedos, R.  et al. 2016

Editorial: Direct and Inverse Comorbidities Between Complex Disorders


Front Physiol - issue: - volume: 7 - pages: 117.

Didier, G.  et al. 2015

Identifying communities from multiplex biological networks

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is...
PeerJ - issue: - volume: 3 - pages: e1525.

Flobak, A.  et al. 2015

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug...
PLoS Comput. Biol. - issue: 8 - volume: 11 - pages: e1004426.

Tripathi, S.  et al. 2015

The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms

BACKGROUND: The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator...
BMC Syst Biol - issue: - volume: 9 - pages: 40.

Ibáñez, K.  et al. 2014

Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses

There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than expected probability of developing some types of Cancer. We tested here the...
PLoS Genet. - issue: 2 - volume: 10 - pages: e1004173.