Menu
MapConnectionsTopicsBulgariaThe Night (K-TRIO)Marie Skłodowska-CurieFor studentsFor teachersMethodologySurprise me
H2020Staff exchange2017–2022

INFERNET · New algorithms for inference and optimization from large-scale biological data

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2017-03-01 → 2022-12-31
EU contribution
€900,000
Participants
6
Scheme
MSCA-RISE

Lines connect the coordinator with its partners.

Results in brief

New algorithms for inference and optimization from large-scale biological data

Issues addressed The perimeter of the research activity has been divided into: A Research Theme characterized by the toolbox and methods developed (i) the inference of interaction networks from data (ii) the analysis of static and dynamical processes on networks B Application Domains divided into four main areas (i) the inference and modeling of multi-scale biological networks (ii) the rational design of biological molecules (iii) the quantitative study of cell energetics in proliferative regimes (iv) the characterization of functional states of large-scale regulatory networks Relevance for society INFERNET application domains and research themes lie at the heart of current trends and emerging paradigms in science and technology. The main activity has been the development of new conceptual tools to organize vast amounts of heterogeneous biological data and to unveil its hidden large-scale relational order. Overall objectives The focus is finding an optimal strategy for learning noisy data sets. To do so we aim at: (i) Setting up a coherent and effective theoretical framework for the statistical mechanics of inverse problems (ii) Exploiting this framework for the development of efficient and distributed algorithms (iii) Analysing the intrinsic limits of the techniques developed in terms of theoretical bounds on the statistical relevance of the inferred results as a function of both quality and quantity of available data

Data: CORDIS, © European Union

Project objective

The tremendous technological advance underlying the ongoing genomic revolution in the life sciences has profoundly transformed biological research over the last 10-15 years. The grand challenge ahead is to leverage experimental progress like high-throughput sequencing by developing effective tools for large scale data-based inference and optimization. The goals of INFERNET rely on the transfer of ideas and methods developed in recent years at the boundary between statistical physics and information theory into the world of quantitative biology. We aim at setting up a consortium characterized by a proven track-record of high-quality research in order to implement a highly integrated program leading from the design of new algorithms to concrete biological applications. The consortium will provide the optimal environment to nurture a generation of researchers that will drive new developments at the forefront of these challenging fields.The perimeter of each individual research activity will be delimited by: (a) the research themes characterized by the toolbox and methods developed and shared within INFERNET, (b) the choice of the application domains. Principal research themes covered by the consortium will be: (i) the inference of interaction networks from data, (ii) the analysis of static and dynamical processes on networks. Application domains can be broken down into four main areas: (i) the inference and modeling of multi-scale biological networks, (ii) the rational design of biological molecules, (iii) the quantitative study of cell energetics in proliferative regimes, (iv) the characterization of functional states of large-scale regulatory networks. Each individual research project will be a puzzle piece of the wider research project, as well as tailored to suit each researcher's scientific and professional development.

Original text from CORDIS.

Participants

  • IIGM FOUNDATION · TorinoCoordinatorItaly
  • BEATSON INSTITUTE FOR CANCER RESEARCH LBG · BearsdenUnited Kingdom
  • ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE · LausanneSwitzerland
  • SORBONNE UNIVERSITE · ParisFrance
  • UNIVERSIDAD DE LA HABANA · PLAZACuba
  • Universidad Nacional de General Sarmiento · Los Polvorines - Provincia De Buenos AiresArgentina

Links

Data: CORDIS, © European Union