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Research projects



MIXED-UP, Targeting pathogenesis and engineering cell factories: by developing mixed regulatory metabolic genomic models in yeasts

The overall goal of the project is to use Systems Biology tools to improve the therapeutic control of pathogenesis in Candida albicans and C. glabrata, and to improve the productivity of S. cerevisiae as a cell factory. To do so, genome-scale Boolean transcription regulatory models and stoichiometric metabolic models will be gathered for the three species, leading to the development of unified combined regulatory-metabolic genome-scale models for these three species. Based on the use of these mixed regulatory-metabolic models, this project aims to provide as outcomes promising new drug-targets and new drugs for the treatment of candidiasis and improved S. cerevisiae cell factories towards a bio-based economy. Additionally, these platforms, as proof-of-concept, and the methods created for their development, are expected to have a huge impact in the development of similar systems biology tools for other organisms.
The project is led by M. Teixeira at iBB-IST, University of Lisbon. Our group is responsible for Task 1, Construction of full genome regulatory models for S. cerevisiae, C. albicans and C. glabrata


PTDC/BEX-BCB/0772/2014 project web site
CoMEDy, A COmputational Modelling platform for Epithelial DYnamics to explore the role of epithelial to mesenchymal transition and stemness acquisition in cancer recurrence.

For most cancers, which stem from epithelial cells, tumour recurrence and metastasis are the major causes of mortality. Recent studies point towards the necessity to take into account intra-tumour heterogeneity to design effective therapies. In particular, a few cells undergo Epithelial to Mesenchymal Transition (EMT), a process by which epithelial cells lose their adhesive properties and are able to migrate. EMT is thus believed to contribute to metastasis. Moreover, the identification of rare populations of tumour initiating cancer cells, Cancer Stem Cells (CSCs), are associated to metastasis and tumour relapse. While key regulatory pathways defining EMT are mostly known, it has not been clearly established how clonal populations of cancer cells cooperate to drive EMT and to favour the emergence of cells with stemlike features. Besides, the mechanisms explaining the association between EMT induction and the emergence of CSCs have yet to be clarified.

Complex intertwined signalling and transcriptional networks govern cell fate decisions. Moreover, both intra and intercellular processes are at play in the aforementioned cooperation between distinct clonal populations within a tumour, not to mention influence of the microenvironment, e.g. Extracellular Matrix (ECM). This makes the path from tumour initiation to metastasis exceedingly complex. Computational modelling shows promising avenue to handle this complexity and to improve our understanding of cancer.
This proposal relies upon the premise that computational models combining both cellular and microenvironment levels are required to tackle our main goal: to clarify, in the context of carcinomas, the emergence of EMT and CSCs as well as their relationship.

See the project website for further details.


CANCEL STEM Tackling cancer stem cells: a challenge and an opportunity to advance in anti-cancer therapy. This research program gathers 18 research teams from 3 top Portuguese institutes (i3s - Porto, CNC.IBILI - Coimbra and IGC - Oeiras). This consortium aims to identify the molecular, cellular and extracellular tumour events that contribute to the acquisition of Cancer Stem Cells (CSC) properties within a neoplastic tissue. It funded by Fundação para a Ciência e a Tecnologia (FCT), through national funds (PIDDAC) and co-financed through the European Regional Development Fund (FEDER).

Our group is involved in WP3 CSC plasticity: EMT and stem cell state generation, in collaboration with Carla Oliveira (i3s - Porto).


ERGODiC Formal methods for the analysis of modular gEnetic ReGulatOry network DynamiCs is a research project led by P. Monteiro at INESC-ID, Lisbon. Our group is involved in Tasks 3, 4 & 5.
Briefly, the project aims to develop efficient means based on SAT approaches to analyse the attractors and their basins of attraction in logical models.


CANTROL Deciphering the mechanisms of transcriptional regulation that control antifungal drug resistance in the pathogenic yeast Candida glabrata: aiming the development of improved diagnosis and therapeutic approaches.
This project is led by M. Teixeira at IST-ID, Lisbon. Our group is involved in Task 3.



project web site
Modular modelling and Analysis of Large biological Interacting Networks, funded by the Fundação para a Ciência e a Tecnologia (FCT, Project PTDC/EIA-CCO/099229/2008).

Until very recently, most mathematical models for concrete molecular networks have been defined as a unique whole, considering networks of a limited size (up to few dozens of components). This approach is not scalable and has to be modified as networks are increasing in size and complexity. The purpose of this project is to develop efficient computational methods to represent and analyse the qualitative behaviours of large regulatory networks. For this, we mainly rely on the concept of network modularity, aiming at defining an appropriate compositional modelling framework.
Importantly, the computational developments envisioned in this project will be confronted and validated with two challenging concrete biological case studies. The first application relates to cells communicating within a structured developing tissue. More precisely, we aim at modelling regulatory processes underlying the dorsal appendage morphogenesis in the Drosophila egg. The second case study corresponds to circulating immune cells, which are activated upon their encounters within lymph nodes. Here, we want to develop a comprehensive model for T lymphocytes differentiation, which play a crucial role in the adaptive immune system. In this system, we deal with a highly complex regulatory process within a constantly renewed population.


Compositional modelling and Analysis of LArge MoleculAr Regulatory networks - application to the control of human cell proliferation. Funded by the French National Research Agency (Agence Nationale de la Recherche), ANR-08-SYSC-003.

Appropriate tools for the dynamical modelling, analysis and simulation are required to delineate the functionning of large regulatory networks. In this respect, different formalisms can be considered, from logical (qualitative) models to differential (quantitative) models. This project intends to develop novel methods to efficiently represent and analyse the behaviour of large regulatory networks. This challenge will be addressed through the conception of efficient computational methods for network reduction and (de)composition (yet keeping track of essential dynamical properties). Formal relationships between qualitative and quantitative models will be generated by the application of dedicated abstraction techniques. These generic methodological developments will be systematically confronted with a reference application, namely the analysis of a comprehensive map of the RB/E2F regulatory network, which plays a key role in the control of human cell proliferation. Functional genomic data and literature mining tools will be combined to complete this generic map and to instantiate it for normal or transformed epithelium, particularly the urothelium cell types and for T lymphocytes. Modularity, (de)composition and abstraction approaches will be applied to this complex network in order to access critical dynamical properties. Network and modules models will be progressively amended and refined by confronting their dynamical behaviours with reported characteristics in the wild-type situation, as well as for documented perturbations. Once stabilised, these models will be used to perform extensive in silico experiments, in order to select novel informative or counter-intuitive situations, which will induce the design of a limited number of validation in vitro or in vivo experiments.