CSTB team: Complex Systems and Translational Bioinformatics

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Biological systems are unique in the complexity of their functions and their regulation. As a consequnece, the integrated study of the multiple levels that contribute to the final behavior of these systems today represents a new challenge for the scientific community. Thanks to the ever increasing amounts of data that describe in detail each of the system components, new opportunities are available to develop descriptive and predictive modeling approaches. These developments are applicable to the entire field of complex systems science, from social networks to finance.
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<center><u><big>'''Theme : LBGI Bioinformatique et Génomique Intégratives'''</big></u></center>
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<center><u><big> [http://lbgi.fr lbgi.fr]</big></u></center><p>
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LBGI Bioinformatics and Integrating Genomics, led by [[Olivier Poch]] and [[Julie Thompson]], focuses on a thriving field of research in the field of health: translational bioinformatics. Our main objective is to develop a robust IT infrastructure capable of managing big data in order to extract relevant knowledge in a "bed-patient" approach. In this context, we are particularly interested in the study of rare genetic diseases and the understanding of the pathophysiological mechanisms involved in these diseases, which often have a potential interest in understanding altered biological processes in more common diseases, such as obesity, diabetes or cancer....
  
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In medicine, this 'systems' awareness has given rise to a new interdisciplinary research field: translational medicine. This domain aims to understand and exploit the diversity of the clinical and phenotypic manifestations of diseases in patients to better understand and model the emergence and evolution of disease. Ultimately, these developments should result in more optimized and personalized treatments.
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==Research topics==
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The LBGI is devoted to the development of robust, automated and integrated in silico approaches (analytical approaches, statistics, data integration and mining, extraction and representation of knowledge...) in order to study the evolution and behavior of complex biological systems ("Hyperstructures", networks, etc.) in humans and various animal models. Taking advantage of our integrated IT approaches and long-standing collaborations at the international, national and local levels, the LBGI participates in the analysis of complex systems involved in various human diseases, including the study of functional deficiencies related to retinal diseases or the brain, the identification of genetic variations related to ciliopathies and the characterization of the genomic and transcriptomic context in various cancers.
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==Operations==
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The work of the LBGI is organized around two main complementary axes:
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* "Translational IT" ([[Julie Thompson]]), to develop an IT infrastructure dedicated to the integrated analysis of the "big data" resulting from high-throughput studies of human genetic diseases. This includes the design and development of original data management systems (storage, quality control, heterogeneous data integration) and analysis tools dedicated to data mining and extraction of biomedical knowledge. An important aspect is the development of intuitive user interfaces to facilitate access by biologists and clinicians.
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* "Systems bioinformatics" ([[Olivier Poch]]/[[Odile Lecompte]]), to develop research in the emerging field of the analysis of complex biological systems, in order to understand genotype-phenotype relationships and to anwser questions related to human diseases. This includes integrated studies of evolutive, "omics" and patient data, particularly those concerning ciliopathies, and the development of a systemic approach to the relationships between mutations and biological networks in diseases.
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==Keywords==
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.........
  
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The "Complex Systems and Translational Bioinformatics" team therefore covers a broad spectrum of research in computer science, ranging from bioinformatics to artificial intelligence. The questions we address are focused firstly, on how to identify the critical points in a complex biological system, and secondly, on how to predict the impact of these perturbations (mutations, drugs, for example) on the stability and behavior of the system. This requires theoretical and multi-scale multi-modal modeling of the biological functions and their regulation underlying the observed phenotypes, while taking into account the context of their dynamic interactions with the environment.
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__NOTOC__
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<center><u><big>'''Theme : SONIC (Stochastic Optimisation and Nature Inspired Computing)'''</big></u></center>
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La thématique SONIC (Stochastic Optimisation and Nature Inspired Computing), <!-- Parallel Evolutionary Computation and ARtificial Intelligence (PECARI)--> led by [[Pierre Collet]], studies and uses techniques to tackle complex problems that are insoluble by exact methods. Nature-inspired methods are privileged for their robustness and their very good exploration of the search space. The team uses mainly:
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* evolutionary algorithms, including :
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** genetic algorithms (applied to discrete and combinatory problems),
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** evolutionary strategies (applied to continuous problems),
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** genetic programming (applied to learning and data mining problems),
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** multi-objective evolutionary optimisation (for all industrial problems that need to optimize several antagonistic criteria at the same time),
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* optimisation by ant colonies,
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* emerging approaches (BOIDS, optimisation by particle swarms).
  
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Our research axes are:
 
  
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* [[LBGI|LBGI: integrative bioinformatics and genomics]]
 
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LBGI has extensive experience in the analysis, annotation and mining of biomedical data. In particular, in the field of rare genetic diseases, the LBGI seeks to identify relations between genotype and phenotype and understand the patterns and trends in the data. The traditional methods, which have been successful in the study of simple systems, reach their limits when applied to complex dynamic systems, where the genetic background of each patient implies a large number of variations that interact with each other to produce effects from the atomic level up to the organism.
 
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* [[SONIC|SONIC: Stochastic Optimization and Native Inspired Complex Systems]]
 
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SONIC has a complementary expertise in complex systems modeling and nature-inspired optimization algorithms, with applications in various fields, ranging from the determination of crystallographic structures to the optimization of learning paths in e-learning or the management of digital medical records.
 
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The team [[People| members]] participate in [[Projects|research projects]], often involving [[Collaborations|collaborations with other laboratories or companies]]. Two [[Platforms|software platforms]] support this work.
 
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Team life consists of [[News|team meetings, visiting researchers, and other events]]. The team regularly offers [[Open_internship,_PhD_and_post-doc_positions|internships, PhD and post-doc positions]].
 
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The team is located at [http://icube.unistra.fr/en/acces/human-street-location/ the medical school in Strasbourg] and [http://icube.unistra.fr/en/acces/site-illkirch/ Télécom Physique at Illkirch].
 
  
 
[[fr:Accueil]]
 
[[fr:Accueil]]

Revision as of 11:44, 30 November 2016

Theme : LBGI Bioinformatique et Génomique Intégratives
lbgi.fr

LBGI Bioinformatics and Integrating Genomics, led by Olivier Poch and Julie Thompson, focuses on a thriving field of research in the field of health: translational bioinformatics. Our main objective is to develop a robust IT infrastructure capable of managing big data in order to extract relevant knowledge in a "bed-patient" approach. In this context, we are particularly interested in the study of rare genetic diseases and the understanding of the pathophysiological mechanisms involved in these diseases, which often have a potential interest in understanding altered biological processes in more common diseases, such as obesity, diabetes or cancer....

Research topics

The LBGI is devoted to the development of robust, automated and integrated in silico approaches (analytical approaches, statistics, data integration and mining, extraction and representation of knowledge...) in order to study the evolution and behavior of complex biological systems ("Hyperstructures", networks, etc.) in humans and various animal models. Taking advantage of our integrated IT approaches and long-standing collaborations at the international, national and local levels, the LBGI participates in the analysis of complex systems involved in various human diseases, including the study of functional deficiencies related to retinal diseases or the brain, the identification of genetic variations related to ciliopathies and the characterization of the genomic and transcriptomic context in various cancers.

Operations

The work of the LBGI is organized around two main complementary axes:

  • "Translational IT" (Julie Thompson), to develop an IT infrastructure dedicated to the integrated analysis of the "big data" resulting from high-throughput studies of human genetic diseases. This includes the design and development of original data management systems (storage, quality control, heterogeneous data integration) and analysis tools dedicated to data mining and extraction of biomedical knowledge. An important aspect is the development of intuitive user interfaces to facilitate access by biologists and clinicians.
  • "Systems bioinformatics" (Olivier Poch/Odile Lecompte), to develop research in the emerging field of the analysis of complex biological systems, in order to understand genotype-phenotype relationships and to anwser questions related to human diseases. This includes integrated studies of evolutive, "omics" and patient data, particularly those concerning ciliopathies, and the development of a systemic approach to the relationships between mutations and biological networks in diseases.

Keywords

.........


Theme : SONIC (Stochastic Optimisation and Nature Inspired Computing)

La thématique SONIC (Stochastic Optimisation and Nature Inspired Computing), led by Pierre Collet, studies and uses techniques to tackle complex problems that are insoluble by exact methods. Nature-inspired methods are privileged for their robustness and their very good exploration of the search space. The team uses mainly:

  • evolutionary algorithms, including :
    • genetic algorithms (applied to discrete and combinatory problems),
    • evolutionary strategies (applied to continuous problems),
    • genetic programming (applied to learning and data mining problems),
    • multi-objective evolutionary optimisation (for all industrial problems that need to optimize several antagonistic criteria at the same time),
  • optimisation by ant colonies,
  • emerging approaches (BOIDS, optimisation by particle swarms).
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