Journées INRA - INRIA

Les sciences de la vie et de

l'environnement

dans la stratégie de l'INRIA

Journées INRA - INRIA

Mai 2007

Journées INRA - INRIA 2

Les sciences de la vie et de l'environnement dans la stratégie de l'INRIA

Introduction

Quelques illustrations

Principales thématiques de recherche

Plan stratégique 2008 - 2012


INRIA

Research in Computer and Information Science and Engineering INRIA fosters a close integration of

research - development - transfer

It offers to associated universities and research partners Scientific and organizational leadership in its areas A vision, a strategic plan, and a research road-map Research facilities and support Strong industrial partnership for technology development &

transfer


INRIA

A total work force of 3600 persons 1100 researchers and faculty members 1000 doctoral candidates 1000 engineers, technicians and staff 500 post-docs and visiting scientists

2100 INRIA employees including 500 permanent scientists1500 partners employees

INRIA budget of 165 M€ Over 20% from grants and IP products Consolidated budget of INRIA activities : 250 M€


Research centers

LORIANancy

INRIA Rhône-AlpesGrenoble

INRIASophia Antipolis

IRISARennes

INRIA FUTURSBordeaux

Metz

INRIA FUTURS, Lille

Lannion

Marseille

Lyon

Montpellier

INRIA FUTURS Saclay

Nantes

Besançon

Strasbourg

INRIARocquencourt

Paris


Nancy

Grenoble

Rennes

Sophia Antipolis

Paris-Rocquencourt

Research Centers

Lille

Saclay

Bordeaux


Environment and life sciences at INRIA

Over 20 project-teams directly involved in this domains

Helix, Symbiose, Contraintes, Bang, ABS , Vista

Digiplants, Comore, VirtualPlants

Clime, Moïse, Mere, Bang, Ariana

Asclepios, Demar, Odyssee, Sysiphe, Visages, Reo

About as many groups contribute to the domain Apics, Coprin, Evasion, Anubis, Geometrica, Caiman, Opale, Orion, Smash, Omega, Tropics, Imedia, Dream Orpailleur, Cortex, Tao, Texmex, etc.



Introduction





MOISE : Modélisation, Observations, Identification en Sciences de l’Environnement

Mathematics and calculus for the direct and inverse modeling in direct geophysicsDesign and optimization of complexe systems complexes (several coupled models, data assimilation)Processing of heterogeneous informationUncertainty quantification

Understanding and predicting natural processes : meteorology, oceanography, hydrology, glaciology Social challenges: water resources, risk prevention and management, evolution of the climate, territory planning,


Visages :Vision, Action et informations de santéJoint team INRIA-INSERM (U746)

Neuroimaging and modeling

Multimodal sensors and

churgical actuators

E-science: biomarkers,

mining, certification in

pharmacology


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Efficience de l’eau

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Helix : Understanding Bacterial Stress Responses Bacteria have capacity to adapt to variety of environmental

stresses (lack of nutrients, heat shock, crowding) Bacterial stress responses are controlled by complex network of

molecular interactions A model of E. coli carbon starvation network using piecewise-

affine models of gene regulation has been designed Experimental verification by means of real-time measurements of

gene expression shows the quality of model prediction

Escherichia coli Carbon starvation network Gene expression measurements


Contraintes : cell and cyrcadien cycles

Biochemical model: Transition system (with continuous time) Biological property: Temporal Logic formula Biological validation: Model-checking

Models of cell cycle: over 800 reactions, 165 genes and proteinsParteners: Institut Curie and FP6 projects

Model

Boolean

Differential

Stochatisc

Temporal

BIOCHAM

Simulation

Query evaluation

Reaction rule learning

Parameter search

Biological properties

Temporal logic

Constraints



Introduction





Biomedical Imaging

Constant stream of better imaging & signal modalities

Provide complementary anatomical & functional info with ever increasing spatial & temporal resolution

From molecular to cellular to organ scales

Emerging new modalities and therapies

Emerging Large Databases

200 microns

Neuro-muscular junctionsMicrovessels & leukocytes

Bladder cells

Brain

Heart


Privileged Role of in vivo Biomedical Imaging

Visualize, analyze and quantify physiological processes and pathological modifications in living systems

Analyze and quantify genesis processes : organs, tumors, vessels, plasticity, etc.

Mark cellular populations and track their migration, phenotypic modifications, differentiation, apoptosis, etc.

Observe biological processes of synthesis, expression, translation, apoptosis, etc.


Virtual Physiological Human Organs

Build personalized virtual models of human physiological systems (e.g. cardiac, respiratory, digestive, nervous central and peripheral, reproductive, etc.) which can be used for

quantitative diagnosis,

prevention of diseases,

therapy planning and simulation


Virtual Neuronal Networks

Simulate feedforward and backward dynamically connected sets of very large populations of spiking neurons to emulate significant aspects of visual perception.

Explore the use of the processing of the signals generated by brain electrical sources to design new interactions between humans and their artifacts.


Large models of Cells, Plants & Ecosystems Build, simulate, analyze and optimize such large models to

explain the emergence of global properties from microscopic interactions.


Computational Structural Biology

Investigate the relationship between the structure of macro-molecules (DNA, RNA, Proteins) and their function.


Computational Physiology

Reproduce Functional properties of living systems at various scales molecules, proteins, cells

cells, tissues, organs, systems, body, etc.

Personalization requires to choose the right level of complexity (observations) and a limited number of parameters

Model normal physiology and physiopathologyINRIA in silico electro-

mechanical cardiac model

nano

micro

meso

macro

ATP

sarcomeres

fibers

organ


Computational Anatomy

Statistics on Anatom

Build standard computational models

Establish plausible variations around standards

Constrain Model Personalization

Detect abnormal Deviations


Biomedical Image and Signal Analysis

New tools to extract and fuse pertinent information from complex multimodal, multidimensional and multiscale signals

segmentation, registration, tracking, deformation, etc.

mining, indexing, learning, etc.

across time, modalities, scales, individuals, populations… Design multi-layered advanced image processing algorithms

Geometrical,

Statistical

Physical,

Physiological


Data Assimilation Techniques

Innovative methods to solve very large inverse problems

Identification of large number of parameters from huge quantity of measurements

Iterative vs. variational methods

Time constraints


Scientific Computing

Importance of scientific computing for direct simulationLarge scale/dimension problemsMultiscale/heterogeneous problemsUncertainties modelingRobustness of optimization

Computational GeometryComputational PhysicsComputational ChemistryComputational Molecular BiologyComputational Structural Biology


Exploring Biological Information

Collect, structure huge amounts of biological information

Add semantics

Represent and Analyze Large Biological Networks

Model their dynamics


Exploit massively parallel computing

Very large data

Heterogeneous data

Distributed data

Confidentiality constraints

Time constraints

Grid computing

Parallel computing

Semantics grids

technologies

Semantic web

Dedicated computing

platforms



Introduction





INRIA Strategic Plan for 2008 - 2012

Research Areas

Algorithmic of Biology & Medicine

Ubiquitous Information, Computation & Communication

Interacting with Real & Virtual Worlds

Modeling, Simulating, & Optimizing Complex Systems

Guarantied & Secure Computing

Computational Sciences

Computational Engineering : Embedded Systems


Algorithmic of Biology and Medicine

Design and develop computational models of living systems

matching biomedical images/signals/measurements

to better understand the living systems under study

to better predict their natural normal or pathological evolution

to better plan and simulate the potential effects of an

interaction

to better control them and repair their possible dysfunctions



Computational Models

of human body

Medical Images

and Signals

Interpretation(diagnosis)

Identification(personalization)

Prediction ofevolution

Therapysimulation

Therapy planning

GeometryStatisticsPhysics

Physiology



Computational Physiology : reproduce personalized functional

properties of living systems at various scales

Computational Anatomy: standard and abnormal models

Biomedical Image and Signal Analysis

Data Assimilation Techniques: very large inverse problems

Scientific Computing : computational geometry, physics,

chemistry, molecular biology, structural biology

Exploring biological Information

Exploiting massively parallel computing


Numerical Sciences

Digital cells

Digital plants

Digital ecology

Digital biosphere and environment

Digital material


Digital Cell

Computational structural biology Relationship between structure and action of complex

molecular machinery Functional genomic, genes - protein expression and regulation

networks Assembly and mechanical functions of the cytoskeleton in the

cell motility and dynamic behavior


Digital ecology

Heterogeneous representation, modeling and integration

Differential models for low trophic levels

Structured population models

Individual-based models for higher trophic species

taking into account the geophysical environment, biotope, interaction between species, etc.

Integration of data from sensor networks, satellites and geo-referenced images

Prediction, visualization, conservation planning


Platforms and systems for

Monitoring, forecasting, risk management at local and global scales

Integrating models and data

Measured evolution of the biosphere

To assess the landscape modifications on earth, the diffusion of

a pollutant in a river, the plankton composition in the oceans

To predict the future evolution of the biotope

Digital Environment


Direct modeling: mathematical and numerical methods, scientific

computing, probabilistic modeling

Forecasting error assessment: modeling uncertainty by

deterministic or stochastic methods, forecasting of extreme events

Inverse modeling: data assimilation, optimal control, filtering

Sensor networks

Large-scale issues

Digital Environment


Automatic image indexing, retrieval and analysis Data representation and processing: sensors fusion, Geographic

Information Systems, decision support systems, etc. 3D visualization: visualization of forecast results, use of virtual

environments for what-if scenario, CAD Software engineering: management of complex and evolving

systems Grid computing: access to distributed computing and data

resources, parallel computing, real-time and security issues

Digital Environment


Inria Strategic Plan

Digital Sciences

Digital engineering

Algorithmic of Biology & Medicine

Information, Computation & Communication Everywhere

Interacting with Real & Virtual Worlds

Modeling, Simulating, & Optimizing Complex Systems

Guarantied & Secure Computing

Documents

Journées INRA - INRIA