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Presentation

Our research aims at deciphering the molecular determinants of cancer and make this knowledge available to improve patient management. It is based on high-dimensional and multi-level omics tumor profiles, and proceed by sophisticated machine learning approaches as well as biological network modelling. 

As a part of our research, we are developing software which is publicly available on our team GitHub repository : https://github.com/sysbio-curie.

At Institut Curie our situation is ideal to pursue these goals, since the choice of collaborations with biologists and clinicians, and the many technological core facilities of the institute offer many options to set up original and cutting-edge projects. We are also involved in many national and international (mainly European) projects with other laboratories in Spain, Germany, Italy, Norway, Netherlands, USA or Japan.

Four decades of cancer molecular biology research have led to the identification of many molecular determinants of this pathology, and showed that they are organized in pathways which in turn are tightly interconnected in a complex network of interactions which covers essential cell processes (proliferation, death, differentiation, DNA repair, immune response…). The existence of crosstalks, feedbacks and compensatory mechanisms invalidates simplistic reasoning and requires mathematical modeling to decipher how cell integrates signals to give rise to particular phenotypes, and to exploit this knowledge for rationalizing clinical treatments.

Another very important aspect of today’s biological and clinical cancer research lies in the high-throughput characterisation of tumours and their micro-environment, at the genome, transcriptome, proteome and epigenome levels (using technologies such as next-generation sequencing (NGS), single cell profiling, mass spectrometry, spatial transcriptomics…), as well as phenotypic level (using high-throughput imaging techniques). These large-scale technologies generate huge volumes of data, making cancer research a big data science.

The multi-dimensional nature of these data typically reaches tens of thousands if not millions of dimensions. This situation requires powerful machine learning approaches to disentangle the hidden signals and convert data into scientific knowledge. Another feature of our data if their multi-level essence, covering molecular, cellular, tissular and patient levels, and their diachronic nature, either through at molecular level or at tumor progression scale.

The volumes, completeness and resolution of the data makes it possible today to integrate them with our knowledge of the signalling circuitry of the cell to build personalized models of tumors which shed light on the functioning of the tumoral cell, how its fate is determined, and how to counteract deleterious phenotypes and propose personalized treatment, the so-called precision medicine approach. These goals have led us to propose new concepts and strategies falling within the field of computational systems biology of cancer.

The first step in the exploitation of high throughput molecular data consists in the extraction of a biological signal from measurements with a low experimental signal/noise ratio and conducted in parallel on thousands or millions of variables (genes, positions on the genome, etc.). This assumes that experimental artefacts be erased (data normalisation) and error rate controlled. We develop tools that achieve this goal in particular for NGS, taking into account the specificities of cancer when needed. Then the biological data must be converted into biological knowledge and e.g. any biological pathways involved in the disease identified. For this purpose, we also develop complexity reduction methods enabling the analysis of multi-dimensional data. These methods are based on mathematical theories like independent component analysis, elastic principal graphs, neural networks of many types, optimal transport, to name a few.

Understanding tumorigenesis and improving clinical strategies requires the detailed knowledge of the molecular interaction networks that control mechanisms of cell proliferation, death and differentiation. We construct an atlas of cell signaling of cancer (acsn.curie.fr), containing detailed map for cell cycle, DNA repair, EMT, programmed cell death and survival, immune response... We also develop computational tools for the analysis of networks, for example for integrating mutational and expression profiles of tumors, or for finding optimal intervention points for a particular tumor in a therapeutic perspective.

We then study the tumoral systems in a dynamic manner, building mathematical models of their molecular networks and proposing prediction of the effect of perturbations like mutations or drug compound. Through this approach, we are able to demonstrate essential system properties (typically global phenotype). Recent applications include finding new therapeutic target candidates in triple negative breast cancer, identifying synthetic interactions in DNA repair machinery, deciphering the modes of action of microRNAs, predicting cell fate decision to die or survive upon cell death receptor engagement, understanding the mechanisms of tumor invasion, proposing strategies for building relevant mouse models of colon cancer metastasis, or exploring the synergistic effects of immune checkpoint inhibitors.

Our mathematical models are then challenged with experimental data and iterative loops between modelling and experimentation allows model improvement and validation. In term of methodology, we use and develop innovative approaches based on literature analysis, complexity reduction, logical modelling, differential equation systems, robustness study, and multidimensional statistical analysis. We have developed a strong expertise in the abovementioned techniques, and put it in practice in many collaborative projects with biologists and clinicians, with a main focus on solid tumors (breast, bladder, uveal melanoma, colon, pediatric tumors…).

Publications

Life of the team

Computational Computational Systems Biology for Complex Human Disease - 21 avril 2024
Computational Computational Systems Biology for Complex Human Disease: from static to dynamic representations of disease mechanisms
This exciting course will focus on applied, functional analysis and interpretation of disease data using computational modelling tools.

The week-long programme will cover the construction and analysis of both static and dynamic mechanistic networks of disease mechanisms concerning complex human diseases such as cancers, autoimmune, inflammatory diseases and others.

 

This course is aimed at PhD students, postdocs and clinicians/healthcare professionals who are interested in using systems biology approaches and computational modelling to tackle biological and biomedical problems concerning human disease.

More information : https://coursesandconferences.wellcomeconnectingscience.org/event/computational-systems-biology-for-complex-human-disease-from-static-to-dynamic-representations-of-disease-mechanisms-20240421/

EMBO Workshop - 26 novembre 2023
Computational models of life: From molecular biology to digital twins
Single-cell and spatial omics have increased the data granularity available for understanding biological mechanisms as well as exponentially increasing the amount of biomedical data available. Modelling has helped biomedical projects by uncovering mechanisms behind drug synergies and personalizing drug treatments while allowing for the study of all scales, from molecules to cells to tissues to organs and even to whole bodies. High-Performance Computing (HPC) environments have enabled the scaling up of scientific computing allowing it to reach an unprecedented scope. HPC has become an essential tool in many fields due to its ability to power up data analysis, storage and large-scale processing.

This in-person 5-day EMBO Workshop will gather early-career researchers and field leaders at the crossroads of single-cell omics, modelling and HPC to pave the way for realistic, scalable human digital twins of cells, tissues and organs. Presentations and discussions around these rapidly-evolving topics will push the field forward by training researchers with techniques from neighbouring fields and allowing them to tackle problems using combined approaches.

More information : https://meetings.embo.org/event/23-comp-models-life

Models of data, data for models - 25 septembre 2023
International Course on Computational Systems Biology of Cancer
The diversity across tumors from different patients and even across cancer cells from the same patient makes the picture very complex. The idea of ‘personalized’ or ‘precision’ medicine has been suggested, aiming to find tailored treatment regimen for each patient according to the individual genetic background and tumor molecular profile. This attempt is achievable thanks to sufficient molecular characterization of cancers accumulated using high-throughput technologies and advanced imaging technologies. However, despite availability of cancer multi-scale data, they are not fully exploited to provide the clue on deregulated mechanisms that would guide better patients stratification and to specific treatment in cancer.

The objective of the course is to promote better use of computational approaches into biological labs and to clinics. We aim to help participants to improve interpretation of various types of data accumulated in the labs using multi-modal data integration approaches.

More Info : https://training.institut-curie.org/courses/sysbiocancer2023

Workshop at BC2 conference in Basel - 11 septembre 2023
Mechanistic and AI digital twins in personalized medicine - two sides of the same coin
Digital twins are an emergent concept in Computational Systems Biology and personalized medicine. Many initiatives manifest an interest of creating computational models of increasing complexity that could be used to represent virtual patients and help in the decision-making regarding the appropriate treatment. Having robust and reliable computational models spanning all biological layers such as gene expression, signalling and metabolism to name a few, could revolutionize the way we treat Big Data for the benefit of precision medicine and improved medical care - tailored to the needs of each patient. As the number of computational models rapidly increases, the production of data is ever growing and the approaches, both mechanistic and AI-based, are rapidly developing, discussions about challenges and best practices are needed more than ever.

The purpose of the workshop is to bring together researchers working in the field of digital twins in computational systems biology who use various formalisms to address challenges of data integration and model personalization. The focus will be on presenting the state of the art in the field and how mechanistic computational modelling can upscale benefiting from AI-based methodological advances.

More information : https://bc2.ch/tutorials-workshops

From pathway modelling tools to cell-level simulations - 25 juin 2023
PerMedCoE Summer School 2023
The PerMedCoE summer school will take place on the 25 – 30 June 2023 at the Hotel Montanyà in Seva, less than an hour away from Barcelona.

The course will combine lectures and hands-on exercises to:

- Introduce attendees to key practicalities of working with high-performance computing (HPC) clusters

- Build first-hand experience in using PerMedCoE modelling tools (CellNOpt, CARNIVAL, COBREXA, MaBoSS, PhysiCell and PhysiBoSS)

- Develop a practical appreciation of using workflow managers and containerised PerMedCoE modelling tools to execute biomedical workflows

 

You can access the material we presented there on our sysbio GitHub: https://github.com/sysbio-curie/Curie-PerMedCoE-Summer-School-2023

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