BORIS SAULNIER 

(Updated : Aug. 2008) Welcome on my research page.

My interests cover : Information theory and entropies - Scaling theories - Thermodynamics of scaling laws in biology - Statistical inference - Genetic regulation - Cognition - Epistemology - Information geometry - Relations and organisational closure.

News : I defended my PhD on Sept. 15th 2006. The title is "Multiscales aspects of information : from physics to biology".
See here for more informations (summary, table of contents, introduction, slides, etc.).

Sept 2003 to Sept. 2006 : PhD in computer science (ENS, Paris 7).
Sept. to Dec. 2006 : visitor in Leiden University, Institute of biology, Section of theoretical biology.
Feb. 2007 to Mar. 2009 : quantitative analyst in rates derivatives (Merril Lynch, London).
Since Jul.09 : quantitative analyst in rates derivatives (Thomson Reuters, Paris).

Contact: boris (dot) saulnier (at)free (dot) fr
- Equipe Complexité et Information Morphologiques (CIM), Département d'Informatique (DI), Ecole Normale Supérieure (ENS). 45, rue d'Ulm  (Etage 3), 75230 Paris Cedex 05 - France - Tel +33 1 44 32 21 85 - Fax +33 1 44 32 21 51
- Equipe Systémoscope, Functional Genomics and Systems Biology for Health, CNRS UMR 7091 (www). 7 rue Guy Môquet - 94801 Villejuif Cedex.

My research : I'm a interested in biology, complexity and cognition. I'm aiming at a theoretical approach of the biological organization and biological function, in terms of information, critical transitions, and symmetries. I investigate scaling aspects of information in biology. A better theoretical characterization of underling probability distributions is needed when facing scale coupling phenomena. I also investigate scaling laws in biology (metabolism scales as a 3/4 power law of adult mass, and characteristic times scale as a 1/4 power law of adult mass).

Keywords : autonomy, biology, category theory, complexity, criticality, degeneracy, dynamics, emergence, entropy, function, information theory, measure, morphogenesis, organizational closure, relationism, scaling, stability, statistical analysis, symmetries, system.

CV : PhD in Computer Science (ENS/Paris 7) ; graduated from Ecole Nationale Supérieure des Télécommunications (ENST) ; master in Theoretical computer science (Paris 7, web) ; master in Philosophy of sciences (Paris 1/IHPST, web).
 See here for papers and communications.
Some links.

PhD - Multiscales aspects of information : from physics to biology

PhD in Computer science (Ecole doctorale Sciences Mathématiques de Paris Centre, Paris 7 Denis Diderot).
I did my PhD in the Computer science lab. of ENS (Equipe CIM, Dpt. d'Informatique DI, Ecole Normale Supérieure ENS).
My advisors were Giuseppe LONGO (Computer science) and Francis BAILLY (Physics).
 Summary : In a first part, this thesis contributes to a theoretical analysis of the foundations of dynamics and states inference in biology. We use different entropies to give a precise mathematical form to the information quantity associated to a measurement process, and develop a unified theoretical framework, from the static or dynamical point of view. We make explicit the causal completeness hypothesis that underlies the use of interaction networks in biocomputing. Existing works do not make this hypothesis explicit, and do not justify it. We show that this hypothesis can not be justified from the analysis of histograms produced experimentally. Actually, a theory is needed to guarantee the reproducibility of an experiment, control the experimental variability, and allow the reconstruction of a probability distribution from the measurement of frequencies. To escape this reproducibility pitfall, the dynamical approach allows to deal with data produced by one unique experiment. We show, within symbolic dynamics, that the different information quantities one can associate to a data source are equivalent. But their effective calculation needs infinite data sequences. Therefore, rebuilding a dynamics needs a prior theoretical characterization.

In the second part of the thesis, we then use methods from statistical physics, to show that entropy, seen as a missing information, is relative to a given scale. Therefore, one deals with hierarchies of entropies. If scales are separable, then a phenomenological description, like interaction networks, with few variables and autonomous equations, is possible. If not, the statistical treatment of fluctuations must be adapted. In this last case, we show that only the relation between scales is objective. To develop this idea, we build a model, related to scaling laws which govern biological thermodynamics. We show that, if scaling laws exist in biology, then, within an organism, the massic enthalpy equals the product of the characteristic groth time and of the metabolism scaling coefficient. This result is experimentally confirmed, so that the model contributes to a theoretical justification of scaling laws observed in biology.
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