The INFERNET project welcome you to the official website  

Welcome to the INFERNET project:
new algorithms for inference and optimization from large-scale biological data

Transfer ideas from statistical inference, optimization techniques and high-performance computing methods
into the world of quantitative biology

Co-funded by the European Union’s H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 734439

A consortium characterized by proven track-record of high-quality research

The project relies on a secondments plan to enhance the potential and future career perspectives of the staff members
Researchers involved will receive systematic training in powerful methodologies that are being developed at the interface between statistical physics, applied mathematics, computer science, and systems biology, molecular biology, metabolomics, by working within high profile research groups.
The following figure maps all the possible exchange of researchers between the INFERNET partners

The INFERNET project in a nutshell

Implementing a highly integrated research program leading from the design of new algorithms to concrete biological applications

Two main research themes will be covered by the consortium

Research Field

INFERENCE OF INTERACTION NETWORKS FROM DATA

A major challenge in computational biology is to use data to unveil the interrelations between biological processes and the molecules contributing to them in terms of regulatory networks. The analysis will produce topological description (who is directly coupled with whom?), and quantitative functional description (how things interact?).

Research Field

ANALYSIS OF STATIC AND DYNAMICAL PROCESSES ON NETWORKS

INFERNET aim at developing distributed algorithmic techniques for a few key inference problems in molecular systems biology such as large scale models of proliferative metabolism, and large scale models of post-transcriptional microRNA mediated regulation.

Application domains can be broken down into four main areas

Application Domain

INFERENCE AND MODELING OF MULTI-SCALE BIOLOGICAL NETWORKS

INFERNET research will be applied to inference from scarce, noisy data sets where a central question is the design of adequate null models to assign statistical significance values to the inferred model parameters, to the sparse graphical model learning, and to the taming with the algorithmic complexity.

Application Domain

RATIONAL DESIGN OF BIOLOGICAL MOLECULES

The project will exploit the co-evolutionary information gathered either from existing databases of thousands of functionally active mutants, to build reliable multivariate models of the observed sequence variability to predict new functional molecules.

Application Domain

QUANTITATIVE STUDY OF CELL ENERGETICS IN PROLIFERATIVE REGIMES

Constraint-based models are powerful mathematical tools that are used to examine metabolic networks at genome scale, and in particular to formulate testable hypotheses on aberrant regimes such as proliferative cancer.

Application Domain

FUNCTIONAL STATES OF LARGE-SCALE REGULATORY NETWORKS

Characterization of regulatory mechanisms in post-transcriptional microRNA mediated networks to: (i) unveil their physical origin, magnitude and dependence on kinetic parameters and (ii) under which conditions it performs other regulatory mechanisms.

Pippo

INFERNET kick-off meeting 04 APRIL, 2016 Turin, Hugef premises Header Image: Designed by Peoplecreations / Freepik Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis...
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INFERNET Kick off Meeting

INFERNET kick-off meeting 04 APRIL, 2017 Turin, Hugef premises Header Image: Designed by Peoplecreations / Freepik The INFERNET Kick-off meeting took place in Turin, at HuGeF’s premises on April 4th 2017. Prof. Andrea Pagnani, the Scientific Responsible of the project...
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New algorithms for inference and optimization from large-scale biological data

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The INFERNET project is co-funded by the European Union’s H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 734439