C. Soussen, D. Brie, and J. , Duan are with the Centre de Recherche Nancy-University, CNRS) Boulevard des Aiguillettes, B.P. 70239, F-54506 Vandoeuvre-l` es

L. '´-etude-théorique-de-la-reconstruction-exacte-pour, Nous avons constaté empiriquement que sur certains types deprobì emes simulés, en l'absence de bruit, le support de la solution x obtenue en sortie de SBR co¨?ncideco¨?ncide avec celui de la vraie solution x * (` a partir de laquelle les donnees y = Ax * ontétéontété simulées) pourvu que ? soit suffisamment faible. Les simulations que nous avons effectuées sur unprobì eme de déconvolution impulsionnelle (séparation de deux signaux gaussiensàgaussiensà supports non disjoints) montrent que sans bruit, SBR conduitàconduità une reconstruction exacte d` es lors que la valeur de ? est suffisamment faible, quel que soit la distance entre les deux signaux gaussiens. Ces résultats pratiques appellent uné etude théorique (non encore réalisée), dans le but de confirmer que sous certaines hypothèses sur la matrice A et sur le signal x * ` a reconstruire

R. Wiesendanger, Scanning Probe Microscopy and Spectroscopy : Methods and Applications, 1994.
DOI : 10.1017/CBO9780511524356

H. Butt, B. Cappella, and M. Kappl, Force measurements with the atomic force microscope: Technique, interpretation and applications, Surface Science Reports, vol.59, issue.1-6, pp.1-6, 2005.
DOI : 10.1016/j.surfrep.2005.08.003

F. Gaboriaud and Y. F. Dufrêne, Atomic force microscopy of microbial cells: Application to nanomechanical properties, surface forces and molecular recognition forces, Colloids and Surfaces B: Biointerfaces, vol.54, issue.1, pp.10-19, 2007.
DOI : 10.1016/j.colsurfb.2006.09.014

F. Gaboriaud and J. Ehrhardt, Effects of different crystal faces on the surface charge of colloidal goethite (??-FeOOH) particles: an experimental and modeling study, Geochimica et Cosmochimica Acta, vol.67, issue.5, pp.967-983, 2003.
DOI : 10.1016/S0016-7037(02)00988-2

C. Soussen, D. Brie, F. Gaboriaud, and C. Kessler, Modeling of force-volume images in atomic force microscopy, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008.
DOI : 10.1109/ISBI.2008.4541319
URL : https://hal.archives-ouvertes.fr/hal-00335199

A. Blake, Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.1, pp.2-12, 1989.
DOI : 10.1109/34.23109

S. Alliney and S. A. Ruzinsky, An algorithm for the minimization of mixed l/sub 1/ and l/sub 2/ norms with application to Bayesian estimation, IEEE Transactions on Signal Processing, vol.42, issue.3, pp.618-627, 1994.
DOI : 10.1109/78.277854

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals Statist, vol.32, issue.2, pp.407-451, 2004.

P. J. Huber, Robust Statistics, 1981.

D. M. Malioutov, M. Cetin, and A. S. Willsky, Homotopy continuation for sparse signal representation, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.733-736, 2005.
DOI : 10.1109/ICASSP.2005.1416408

J. Fan and R. Li, Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001.
DOI : 10.1198/016214501753382273

B. K. Natarajan, Sparse Approximate Solutions to Linear Systems, SIAM Journal on Computing, vol.24, issue.2, pp.227-234, 1995.
DOI : 10.1137/S0097539792240406

F. Champagnat, Y. Goussard, and J. Idier, Unsupervised deconvolution of sparse spike trains using stochastic approximation, IEEE Transactions on Signal Processing, vol.44, issue.12, pp.2988-2998, 1996.
DOI : 10.1109/78.553473

F. Champagnat, Y. Goussard, and S. G. Idier, Déconvolution impulsionnelle " , in Approche bayésienne pour lesprobì emes inverses, pp.115-138, 2001.

A. J. Miller, Subset Selection in Regression, 2002.

A. Kriznik, M. Bouillot, J. Coulon, and F. Gaboriaud, Morphological specificity of yeast and filamentous Candida albicans forms on surface properties, Comptes Rendus Biologies, vol.328, issue.10-11, pp.928-935, 2005.
DOI : 10.1016/j.crvi.2005.05.011

B. Rivet, L. Girin, and C. Jutten, Solving the indeterminations of blind source separation of convolutive speech mixtures, Proc. IEEE ICASSP, pp.533-536, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00098153

P. Bofill, Underdetermined blind separation of delayed sound sources in the frequency domain, Neurocomputing, vol.55, issue.3-4, pp.627-641, 2003.
DOI : 10.1016/S0925-2312(02)00631-8

C. Soussen, D. Brie, F. Gaboriaud, and C. Kessler, Atomic force microscopy imaging and related issues in signal processing : a preliminary work, Institut franco-allemand pour les applications de la recherche, pp.1-12, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00196504

V. Mazet, D. Brie, and J. Idier, Decomposition of a Chemical Spectrum using a Marked Point Process and a Constant Dimension Model, AIP Conference Proceedings, 2006.
DOI : 10.1063/1.2423286
URL : https://hal.archives-ouvertes.fr/hal-00121593

J. Duan, C. Soussen, D. Brie, and J. Idier, A continuation approach to estimate a solution path of mixed L2-L0 minimization problems, Signal Processing with Adaptive Sparse Structured Representations (SPARS workshop), Saint-Malo, pp.1-6, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00363468

Y. Goussard, G. Demoment, and J. Idier, A new algorithm for iterative deconvolution of sparse spike trains, International Conference on Acoustics, Speech, and Signal Processing, pp.1547-1550, 1990.
DOI : 10.1109/ICASSP.1990.115707

S. Chen, S. A. Billings, and W. Luo, Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control, vol.10, issue.5, pp.1873-1896, 1989.
DOI : 10.2307/2284566

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

Y. Pati, R. Rezaiifar, and P. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

J. Duan, C. Soussen, D. Brie, and J. Idier, Détection conjointe de discontinuités d'ordres différents dans un signal par minimisation de critère L2-L0, Actes 22 e coll. GRETSI, 2009.

B. D. Rao, K. Engan, S. F. Cotter, J. Palmer, and K. Kreutz-delgado, Subset selection in noise based on diversity measure minimization, IEEE Transactions on Signal Processing, vol.51, issue.3, pp.760-770, 2003.
DOI : 10.1109/TSP.2002.808076

G. H. Mohimani, M. Babaie-zadeh, and C. Jutten, A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed <formula formulatype="inline"><tex Notation="TeX">$\ell ^{0}$</tex></formula> Norm, IEEE Transactions on Signal Processing, vol.57, issue.1, pp.289-301, 2009.
DOI : 10.1109/TSP.2008.2007606

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010

D. L. Donoho and Y. Tsaig, Fast Solution of <formula formulatype="inline"><tex>$\ell _{1}$</tex> </formula>-Norm Minimization Problems When the Solution May Be Sparse, IEEE Transactions on Information Theory, vol.54, issue.11, pp.4789-4812, 2008.
DOI : 10.1109/TIT.2008.929958

J. J. Fuchs, Recovery of Exact Sparse Representations in the Presence of Bounded Noise, IEEE Transactions on Information Theory, vol.51, issue.10, pp.3601-3608, 2005.
DOI : 10.1109/TIT.2005.855614

D. Needell and J. A. Tropp, CoSaMP, Communications of the ACM, vol.53, issue.12, pp.301-321, 2008.
DOI : 10.1145/1859204.1859229

T. Blumensath and M. E. Davies, Iterative Thresholding for Sparse Approximations, Journal of Fourier Analysis and Applications, vol.73, issue.10, pp.629-654, 2008.
DOI : 10.1007/s00041-008-9035-z

C. Couvreur and Y. Bresler, On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.3, pp.797-808, 2000.
DOI : 10.1137/S0895479898332928

D. Haugland, A Bidirectional Greedy Heuristic for the Subspace Selection Problem, Lecture Notes in Computer Science, vol.4638, pp.162-176, 2007.
DOI : 10.1007/978-3-540-74446-7_12

T. Zhang, Adaptive forward-backward greedy algorithm for sparse learning with linear models, NIPS, pp.1921-1928, 2008.

T. Blumensath and M. E. Davies, On the difference between orthogonal matching pursuit and orthogonal least squares, Tech. Rep, 2007.

J. J. Kormylo and J. M. , Maximum likelihood detection and estimation of Bernoulli - Gaussian processes, IEEE Transactions on Information Theory, vol.28, issue.3, pp.482-488, 1982.
DOI : 10.1109/TIT.1982.1056496

Q. Cheng, R. Chen, and T. Li, Simultaneous wavelet estimation and deconvolution of reflection seismic signals, IEEE Transactions on Geoscience and Remote Sensing, vol.34, issue.2, pp.377-384, 1996.
DOI : 10.1109/36.485115

I. F. Gorodnitsky and B. D. Rao, Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm, IEEE Transactions on Signal Processing, vol.45, issue.3, pp.600-616, 1997.
DOI : 10.1109/78.558475

S. Chen and J. Wigger, Fast orthogonal least squares algorithm for efficient subset model selection, IEEE Transactions on Signal Processing, vol.43, issue.7, pp.1713-1715, 1995.
DOI : 10.1109/78.398734

R. Horn and C. Johnson, Matrix analysis, 1985.

S. J. Reeves, An efficient implementation of the backward greedy algorithm for sparse signal reconstruction, IEEE Signal Processing Letters, vol.6, issue.10, pp.266-268, 1999.
DOI : 10.1109/97.789606

S. F. Cotter, J. Adler, B. D. Rao, and K. Kreutz-delgado, Forward sequential algorithms for best basis selection, IEE Proc. Vision, Image and Signal Processing, pp.235-244, 1999.
DOI : 10.1049/ip-vis:19990445

D. Ge, J. Idier, and E. L. Carpentier, Enhanced sampling schemes for MCMC based blind Bernoulli???Gaussian deconvolution, Signal Processing, vol.91, issue.4, 2009.
DOI : 10.1016/j.sigpro.2010.08.009
URL : https://hal.archives-ouvertes.fr/hal-00517861

P. E. Gill, G. H. Golub, W. Murray, and M. A. Saunders, Methods for modifying matrix factorizations, Mathematics of Computation, vol.28, issue.126, pp.505-535, 1974.
DOI : 10.1090/S0025-5718-1974-0343558-6

C. Y. Chi and J. M. , Improved maximum-likelihood detection and estimation of Bernoulli-Gaussian processes, IEEE Trans. Inf. Theory, vol.30, pp.429-435, 1984.

M. Allain and J. Idier, Efficient binary reconstruction for non-destructive evaluation using gammagraphy, Inverse Problems, vol.23, issue.4, pp.1371-1393, 2007.
DOI : 10.1088/0266-5611/23/4/002
URL : https://hal.archives-ouvertes.fr/hal-00147782

M. Vetterli, P. Marziliano, and T. Blu, Sampling signals with finite rate of innovation, IEEE Transactions on Signal Processing, vol.50, issue.6, pp.1417-1428, 2002.
DOI : 10.1109/TSP.2002.1003065

F. R. Gantmacher and M. G. Krein, Oscillation matrices and kernels and small vibrations of mechanical systems, 2002.
DOI : 10.1090/chel/345

F. Gaboriaud, B. S. Parcha, M. L. Gee, J. A. Holden, and R. A. Strugnell, Spatially resolved force spectroscopy of bacterial surfaces using force-volume imaging, Colloids and Surfaces B: Biointerfaces, vol.62, issue.2, pp.206-213, 2008.
DOI : 10.1016/j.colsurfb.2007.10.004

C. Soussen, J. Duan, D. Brie, P. Polyakov, G. Francius et al., Force curve segmentation by piecewise polynomial approximation : mathematical formulation and complete structure of the algorithm, Tech. Rep, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00508384

E. Van-den-berg, M. P. Friedlander, G. Hennenfent, F. J. Herrmann, R. Saab et al., Algorithm 890, ACM Transactions on Mathematical Software, vol.35, issue.4, pp.1-16, 2009.
DOI : 10.1145/1462173.1462178

E. J. Candès and M. B. Wakin, An Introduction To Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.21-30, 2008.
DOI : 10.1109/MSP.2007.914731

P. Hansen, Analysis of Discrete Ill-Posed Problems by Means of the L-Curve, SIAM Review, vol.34, issue.4, pp.561-580, 1992.
DOI : 10.1137/1034115

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

W. Paranchych and L. Frost, The Physiology and Biochemistry of Pili, Adv. Microb. Physiol, vol.29, pp.53-114, 1988.
DOI : 10.1016/S0065-2911(08)60346-X

R. Emerson and T. Camesano, Nanoscale Investigation of Pathogenic Microbial Adhesion to a Biomaterial, Applied and Environmental Microbiology, vol.70, issue.10, pp.6012-6022, 2004.
DOI : 10.1128/AEM.70.10.6012-6022.2004

P. N. Danese, L. A. Pratt, S. L. Dove, and R. Kolter, The outer membrane protein, Antigen 43, mediates cell-to-cell interactions within Escherichia coli biofilms, Molecular Microbiology, vol.65, issue.2, pp.424-432, 2000.
DOI : 10.1046/j.1365-2958.1999.01353.x

M. Klausen, A. Heydorn, P. Ragas, L. Lambertsen, A. Aaes-jøgensen et al., Biofilm formation by Pseudomonas aeruginosa wild type, flagella and type IV pili mutants, Molecular Microbiology, vol.413, issue.6, pp.1511-1524, 2003.
DOI : 10.1046/j.1365-2958.2003.03525.x

S. Da-re, B. L. Quéré, J. Ghigo, and C. Beloin, Tight Modulation of Escherichia coli Bacterial Biofilm Formation through Controlled Expression of Adhesion Factors, Applied and Environmental Microbiology, vol.73, issue.10, pp.3391-3403, 2007.
DOI : 10.1128/AEM.02625-06
URL : https://hal.archives-ouvertes.fr/pasteur-00331439

N. Ledeboer and B. Jones, Exopolysaccharide Sugars Contribute to Biofilm Formation by Salmonella enterica Serovar Typhimurium on HEp-2 Cells and Chicken Intestinal Epithelium, Journal of Bacteriology, vol.187, issue.9, pp.3214-3226, 2005.
DOI : 10.1128/JB.187.9.3214-3226.2005

K. Jonas, H. Tomenius, A. Kader, S. Normark, U. Römling et al., Roles of curli, cellulose and BapA in Salmonella biofilm morphology studied by atomic force microscopy, BMC Microbiology, vol.7, issue.1, p.9, 2007.
DOI : 10.1186/1471-2180-7-70

C. Beloin, A. Roux, and J. Ghigo, Escherichia coli Biofilms, Curr. Top. Microbiol, vol.322, p.249, 2008.
DOI : 10.1007/978-3-540-75418-3_12
URL : https://hal.archives-ouvertes.fr/pasteur-00473297

H. Sun, D. R. Zusman, and W. Shi, Type IV pilus of Myxococcus xanthus is a motility apparatus controlled by the frz chemosensory system, Current Biology, vol.10, issue.18, pp.1143-1146, 2000.
DOI : 10.1016/S0960-9822(00)00705-3

M. Mcbride, Bacterial Gliding Motility: Multiple Mechanisms for Cell Movement over Surfaces, Annual Review of Microbiology, vol.55, issue.1, pp.49-75, 2001.
DOI : 10.1146/annurev.micro.55.1.49

R. Harshey, Bacterial Motility on a Surface: Many Ways to a Common Goal, Annual Review of Microbiology, vol.57, issue.1, pp.249-273, 2003.
DOI : 10.1146/annurev.micro.57.030502.091014

L. Craig, M. E. Pique, and J. A. Tainer, Type IV pilus structure and bacterial pathogenicity, Nature Reviews Microbiology, vol.166, issue.5, pp.363-378, 2004.
DOI : 10.1084/jem.20031159

G. C. Ulett, A. N. Mabbett, K. C. Fung, R. I. Webb, and M. A. Schembri, The role of F9 fimbriae of uropathogenic Escherichia coli in biofilm formation, Microbiology, vol.153, issue.7, pp.2321-2331, 2007.
DOI : 10.1099/mic.0.2006/004648-0

A. N. Mabbett, G. C. Ulett, R. E. Watts, J. J. Tree, M. Totsika et al., Virulence properties of asymptomatic bacteriuria Escherichia coli, International Journal of Medical Microbiology, vol.299, issue.1, pp.53-63, 2009.
DOI : 10.1016/j.ijmm.2008.06.003

A. Alessandrini and P. Facci, AFM: a versatile tool in biophysics, Measurement Science and Technology, vol.16, issue.6, pp.65-92, 2005.
DOI : 10.1088/0957-0233/16/6/R01

P. Schar-zammaretti, Surface morphology, elasticity and interactions of lactic acid bacteria studied by atomic force microscopy, Abstr. Pap. Am. Chem. Soc, vol.224, pp.397-397, 2002.

Y. F. Dufrêne, C. Boonaert, H. C. Van-der-mei, H. J. Busscher, and P. G. Rouxhet, Probing molecular interactions and mechanical properties of microbial cell surfaces by atomic force microscopy, Ultramicroscopy, vol.86, issue.1-2, pp.113-120, 2001.
DOI : 10.1016/S0304-3991(00)00079-6

M. Radmacher, Measuring the Elastic Properties of Living Cells by the Atomic Force Microscope, Method Cell Biol, vol.68, pp.67-90, 2002.
DOI : 10.1016/S0091-679X(02)68005-7

A. Engel and D. Muller, Observing single biomolecules at work with the atomic force microscope, Nature Structural Biology, vol.7, issue.9, pp.715-718, 2000.
DOI : 10.1038/78929

G. Francius, B. Tesson, E. Dague, V. Martin-jézéquel, and Y. Dufrêne, Nanostructure and nanomechanics of live Phaeodactylum tricornutum morphotypes, Environmental Microbiology, vol.26, issue.5, pp.1344-1356, 2008.
DOI : 10.1038/149331a0

G. Francius, S. Lebeer, D. Alsteens, L. Wildling, H. J. Gruber et al., Detection, Localization, and Conformational Analysis of Single Polysaccharide Molecules on Live Bacteria, ACS Nano, vol.2, issue.9, pp.1921-1929, 2008.
DOI : 10.1021/nn800341b

G. Francius, O. Domenech, M. Mingeot-leclercq, and Y. Dufrêne, Direct Observation of Staphylococcus aureus Cell Wall Digestion by Lysostaphin, Journal of Bacteriology, vol.190, issue.24, pp.7904-7909, 2008.
DOI : 10.1128/JB.01116-08

S. Velegol and B. Logan, Contributions of Bacterial Surface Polymers, Electrostatics, and Cell Elasticity to the Shape of AFM Force Curves, Langmuir, vol.18, issue.13, pp.5256-5262, 2002.
DOI : 10.1021/la011818g

H. Clausen-schaumann, M. Seitz, R. Krautbauer, and H. Gaub, Force spectroscopy with single bio-molecules, Current Opinion in Chemical Biology, vol.4, issue.5, pp.524-530, 2000.
DOI : 10.1016/S1367-5931(00)00126-5

T. E. Fisher, P. E. Marszalek, and J. M. Fernandez, Stretching single molecules into novel conformations using the atomic force microscope, Nat. Struct. Biol, vol.7, pp.719-724, 2000.

H. Janovjak, A. Kedrov, D. A. Cisneros, K. T. Sapra, J. Struckmeier et al., Imaging and detecting molecular interactions of single transmembrane proteins, Neurobiology of Aging, vol.27, issue.4, pp.546-561, 2006.
DOI : 10.1016/j.neurobiolaging.2005.03.031

P. Hinterdorfer and Y. Dufrêne, Detection and localization of single molecular recognition events using atomic force microscopy, Nature Methods, vol.34, issue.5, pp.347-355, 2006.
DOI : 10.1038/nmeth871

Y. Dufrêne, Towards nanomicrobiology using atomic force microscopy, Nature Reviews Microbiology, vol.456, issue.9, pp.674-680, 2008.
DOI : 10.1038/nrmicro1948

N. J. Tao, S. M. Lindsay, and S. Lees, Measuring the microelastic properties of biological material, Biophysical Journal, vol.63, issue.4, pp.1165-1169, 1992.
DOI : 10.1016/S0006-3495(92)81692-2

A. Vinckier and G. Semenza, Measuring elasticity of biological materials by atomic force microscopy, FEBS Letters, vol.18, issue.1-2, pp.12-16, 1998.
DOI : 10.1016/S0014-5793(98)00592-4

D. Ebenstein and L. Pruitt, Nanoindentation of biological materials, Nano Today, vol.1, issue.3, pp.26-33, 2006.
DOI : 10.1016/S1748-0132(06)70077-9

J. Hoh and C. Schoenenberger, Surface-Morphology and Mechanical-Properties of Mdck Monolayers by Atomic-Force Microscopy, J. Cell Sci, vol.107, pp.1105-1114, 1994.

M. Radmacher, M. Fritz, C. M. Kacher, J. P. Cleveland, and P. K. Hansma, Measuring the viscoelastic properties of human platelets with the atomic force microscope, Biophysical Journal, vol.70, issue.1, pp.556-567, 1996.
DOI : 10.1016/S0006-3495(96)79602-9

A. Engler, F. Rehfeldt, S. Sen, and D. Discher, Microtissue Elasticity: Measurements by Atomic Force Microscopy and Its Influence on Cell Differentiation, Method Cell Biol, vol.83, pp.521-545, 2007.
DOI : 10.1016/S0091-679X(07)83022-6

S. Park, D. Koch, R. Cardenas, J. Käs, and C. K. Shih, Cell Motility and Local Viscoelasticity of Fibroblasts, Biophysical Journal, vol.89, issue.6, pp.4330-4342, 2005.
DOI : 10.1529/biophysj.104.053462

J. Domke, S. Dannöhl, W. J. Parak, O. Müller, W. K. Aicher et al., Substrate dependent differences in morphology and elasticity of living osteoblasts investigated by atomic force microscopy, Colloids and Surfaces B: Biointerfaces, vol.19, issue.4, pp.367-379, 2000.
DOI : 10.1016/S0927-7765(00)00145-4

V. Vadillo-rodriguez, T. J. Beveridge, and J. R. Dutcher, Surface Viscoelasticity of Individual Gram-Negative Bacterial Cells Measured Using Atomic Force Microscopy, Journal of Bacteriology, vol.190, issue.12, pp.4225-4232, 2008.
DOI : 10.1128/JB.00132-08

A. Touhami, B. Nysten, and Y. F. Dufrêne, Nanoscale Mapping of the Elasticity of Microbial Cells by Atomic Force Microscopy, Langmuir, vol.19, issue.11, pp.4539-4543, 2003.
DOI : 10.1021/la034136x

M. Beckmann, S. Venkataraman, M. Doktycz, J. Nataro, C. Sullivan et al., Measuring cell surface elasticity on enteroaggregative Escherichia coli wild type and dispersin mutant by AFM, Ultramicroscopy, vol.106, issue.8-9, pp.695-702, 2006.
DOI : 10.1016/j.ultramic.2006.02.006

E. K. Dimitriadis, F. Horkay, J. Maresca, B. Kachar, and R. S. Chadwick, Determination of Elastic Moduli of Thin Layers of Soft Material Using the Atomic Force Microscope, Biophysical Journal, vol.82, issue.5, pp.2798-2810, 2002.
DOI : 10.1016/S0006-3495(02)75620-8

G. Francius, J. Hemmerlé, J. Ohayon, P. Schaaf, J. Voegel et al., Effect of crosslinking on the elasticity of polyelectrolyte multilayer films measured by colloidal probe AFM, Microscopy Research and Technique, vol.32, issue.211, pp.84-92, 2006.
DOI : 10.1002/jemt.20275
URL : https://hal.archives-ouvertes.fr/hal-00846392

J. F. Duval, G. Francius, P. Polyakov, J. Merlin, Y. Abe et al., Bacterial surface appendages strongly impact nanomechanical and electrokinetic properties of Escherichia coli cells subjected to osmotic stress, Prog. Biophys. Mol. Biol, 2010.
URL : https://hal.archives-ouvertes.fr/pasteur-01393507

M. Mustata, K. Ritchie, and H. A. Mcnally, Neuronal elasticity as measured by atomic force microscopy, Journal of Neuroscience Methods, vol.186, issue.1, pp.35-41, 2010.
DOI : 10.1016/j.jneumeth.2009.10.021

X. Chen, L. Feng, H. Jin, S. Feng, and Y. Yu, Quantification of the erythrocyte deformability using atomic force microscopy : Correlation study of the erythrocyte deformability with atomic force microscopy and hemorheology, Clin. Hemorheol. Microcirc, vol.43, pp.241-249, 2009.

S. E. Cross, Y. Jin, J. Rao, and J. K. Gimzewski, Nanomechanical analysis of cells from cancer patients, Nature Nanotechnology, vol.86, issue.12, pp.780-783, 2007.
DOI : 10.1038/nnano.2007.388

S. E. Cross, Y. Jin, J. Tondre, R. Wong, J. Rao et al., AFM-based analysis of human metastatic cancer cells, Nanotechnology, vol.19, issue.38, 2008.
DOI : 10.1088/0957-4484/19/38/384003

B. Bhushan and N. Chen, AFM studies of environmental effects on nanomechanical properties and cellular structure of human hair, Ultramicroscopy, vol.106, issue.8-9, pp.755-764, 2006.
DOI : 10.1016/j.ultramic.2005.12.010

R. La and M. Arnsdorf, Multidimensional atomic force microscopy for drug discovery : A versatile tool for defining targets, designing therapeutics and monitoring their efficacy, Life Sci, vol.86, pp.545-562, 2010.

F. Malfatti and F. Azam, Atomic force microscopy reveals microscale networks and possible symbioses among pelagic marine bacteria, Aquatic Microbial Ecology, vol.58, pp.1-14, 2009.
DOI : 10.3354/ame01355

W. Tang and B. Bhushan, Adhesion, friction and wear characterization of skin and skin cream using atomic force microscope, Colloids and Surfaces B: Biointerfaces, vol.76, issue.1, pp.1-15, 2010.
DOI : 10.1016/j.colsurfb.2009.09.039

S. Ramachandran, A. P. Quist, S. Kumar, and R. Lal, Cisplatin Nanoliposomes for Cancer Therapy:?? AFM and Fluorescence Imaging of Cisplatin Encapsulation, Stability, Cellular Uptake, and Toxicity, Langmuir, vol.22, issue.19, pp.8156-8162, 2006.
DOI : 10.1021/la0607499

S. Sen and S. Kumar, Combining mechanical and optical approaches to dissect cellular mechanobiology, Journal of Biomechanics, vol.43, issue.1, pp.45-54, 2010.
DOI : 10.1016/j.jbiomech.2009.09.008

M. Lekka and P. Laidler, Applicability of AFM in cancer detection, Nature Nanotechnology, vol.92, issue.2, pp.72-72, 2009.
DOI : 10.1038/nnano.2009.004

M. Lekka and J. Wiltowska-zuber, Biomedical applications of AFM, 2nd National Conference on Nanotechnology, pp.12023-12023, 2008.
DOI : 10.1088/1742-6596/146/1/012023

H. Jin, X. Xing, H. Zhao, Y. Chen, X. Huang et al., Detection of erythrocytes influenced by aging and type 2 diabetes using atomic force microscope, Biochemical and Biophysical Research Communications, vol.391, issue.4, pp.1698-1702, 2010.
DOI : 10.1016/j.bbrc.2009.12.133

S. E. Cross, Y. Jin, J. Rao, and J. K. Gimzewski, Applicability of AFM in cancer detection, Nature Nanotechnology, vol.2, issue.2, pp.72-73, 2009.
DOI : 10.1038/nnano.2009.036

C. K. Fung, K. Seiffert-sinha, K. W. Lai, R. Yang, D. Panyard et al., Investigation of human keratinocyte cell adhesion using atomic force microscopy, Nanomedicine: Nanotechnology, Biology and Medicine, vol.6, issue.1, pp.191-200, 2010.
DOI : 10.1016/j.nano.2009.05.008

S. Kasas and G. Dietler, Probing nanomechanical properties from biomolecules to living cells, Pfl??gers Archiv - European Journal of Physiology, vol.21, issue.1, pp.13-27, 2008.
DOI : 10.1007/s00424-008-0448-y

D. Lin, E. Dimitriadis, and F. Horkay, Robust Strategies for Automated AFM Force Curve Analysis???I. Non-adhesive Indentation of Soft, Inhomogeneous Materials, Journal of Biomechanical Engineering, vol.129, issue.3, pp.430-440, 2007.
DOI : 10.1115/1.2720924

D. Lin, E. Dimitriadis, and F. Horkay, Robust Strategies for Automated AFM Force Curve Analysis???II: Adhesion-Influenced Indentation of Soft, Elastic Materials, Journal of Biomechanical Engineering, vol.129, issue.6, pp.904-912, 2007.
DOI : 10.1115/1.2800826

D. Rudoy, S. G. Yuen, R. D. Howe, and P. J. Wolfe, Bayesian changepoint analysis for atomic force microscopy and soft material indentation, J Roy Stat Soc C-App, vol.59, pp.573-593, 2010.

S. Sen, S. Subramanian, and D. E. Discher, Indentation and Adhesive Probing of a Cell Membrane with AFM: Theoretical Model and Experiments, Biophysical Journal, vol.89, issue.5, pp.3203-3213, 2005.
DOI : 10.1529/biophysj.105.063826

M. W. Rutland, J. G. Tyrrell, and P. Attard, Analysis of atomic force microscopy data for deformable materials, Journal of Adhesion Science and Technology, vol.71, issue.10, pp.1199-1215, 2004.
DOI : 10.1163/156856102760136445

P. Attard and J. Parker, Deformation and adhesion of elastic bodies in contact, Physical Review A, vol.46, issue.12, pp.7959-7971, 1992.
DOI : 10.1103/PhysRevA.46.7959

J. Song, D. Tranchida, and G. J. Vancso, Contact Mechanics of UV/Ozone-Treated PDMS by AFM and JKR Testing: Mechanical Performance from Nano- to Micrometer Length Scales, Macromolecules, vol.41, issue.18, pp.6757-6762, 2008.
DOI : 10.1021/ma800536y

D. C. Lin, E. K. Dimitriadis, and F. Horkay, Elasticity of rubber-like materials measured by AFM nanoindentation, Express Polymer Letters, vol.1, issue.9, pp.576-584, 2007.
DOI : 10.3144/expresspolymlett.2007.79

S. G. Yuen, D. Rudoy, R. D. Howe, and P. J. Wolfe, Bayesian Changepoint Detection Through Switching Regressions: Contact Point Determination in Material Indentation Experiments, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, pp.104-108, 2007.
DOI : 10.1109/SSP.2007.4301227

S. Cui, Y. Yu, and Z. Lin, Modeling single chain elasticity of single-stranded DNA: A comparison of three models, Polymer, vol.50, issue.3, pp.930-935, 2009.
DOI : 10.1016/j.polymer.2008.12.012

T. Camesano and N. Abu, Heterogeneity in Bacterial Surface Polysaccharides, Probed on a Single-Molecule Basis, Biomacromolecules, vol.3, issue.4, pp.661-667, 2002.
DOI : 10.1021/bm015648y

F. Gaboriaud, M. L. Gee, R. Strugnell, and J. F. Duval, Coupled Electrostatic, Hydrodynamic, and Mechanical Properties of Bacterial Interfaces in Aqueous Media, Langmuir, vol.24, issue.19, pp.10988-10995, 2008.
DOI : 10.1021/la800258n

H. Ohshima, Electrostatic interaction between soft particles, Journal of Colloid and Interface Science, vol.328, issue.1, pp.3-9, 2008.
DOI : 10.1016/j.jcis.2008.08.009

J. Lyklema, Fundamentals of Interface and Colloid Science, 2005.

M. Giesbers, Surface forces studied with colloidal probe atomic force microscopy, 2001.

H. Hertz, Ueber die Berührung fester elastischer Körper, J Reine Angew Math, vol.92, pp.156-171

X. Yao, J. Walter, S. Burke, S. Stewart, M. H. Jericho et al., Atomic force microscopy and theoretical considerations of surface properties and turgor pressures of bacteria, Colloids and Surfaces B: Biointerfaces, vol.23, issue.2-3, pp.213-230, 2002.
DOI : 10.1016/S0927-7765(01)00249-1

C. Ortiz and G. Hadziioannou, Entropic Elasticity of Single Polymer Chains of Poly(methacrylic acid) Measured by Atomic Force Microscopy, Macromolecules, vol.32, issue.3, pp.780-787, 1999.
DOI : 10.1021/ma981245n

A. Janshoff, M. Neitzert, Y. Oberdörfer, and H. Fuchs, Force Spectroscopy of Molecular Systems???Single Molecule Spectroscopy of Polymers and Biomolecules, Angewandte Chemie, vol.339, issue.18, pp.3212-3237, 2000.
DOI : 10.1002/1521-3773(20000915)39:18<3212::AID-ANIE3212>3.0.CO;2-X

N. Abu-lail and T. Camesano, Polysaccharide properties probed with atomic force microscopy, Journal of Microscopy, vol.212, issue.3, pp.217-238, 2003.
DOI : 10.1111/j.1365-2818.2003.01261.x

J. J. Heinisch, V. Dupres, D. Alsteens, and Y. F. Dufrêne, Measurement of the mechanical behavior of yeast membrane sensors using single-molecule atomic force microscopy, Nature Protocols, vol.440, issue.4, pp.670-677, 2010.
DOI : 10.1038/nprot.2010.19

Y. F. Gruber and . Dufrêne, Stretching polysaccharides on live cells using single molecule force spectroscopy, Nat. Protoc, vol.4, pp.939-946, 2009.

D. Alsteens, V. Dupres, S. A. Klotz, N. K. Gaur, P. N. Lipke et al., Unfolding Individual Als5p Adhesion Proteins on Live Cells, ACS Nano, vol.3, issue.7, pp.1677-1682, 2009.
DOI : 10.1021/nn900078p

F. Ribas, J. Perramon, A. Terradillos, J. Frias, and F. Lucena, The Pseudomonas group as an indicator of potential regrowth in water distribution systems, Journal of Applied Microbiology, vol.48, issue.4, pp.704-710, 2000.
DOI : 10.1046/j.1365-2672.2000.01021.x

G. O. Toole and R. Kolter, Initiation of biofilm formation in Pseudomonas fluorescens WCS365 proceeds via multiple, convergent signalling pathways: a genetic analysis, Molecular Microbiology, vol.28, issue.3, pp.449-461, 1998.
DOI : 10.1016/S0168-6496(96)00078-5

D. Allison, B. Ruiz, C. Sanjose, A. Jaspe, and P. Gilbert, biofilms, FEMS Microbiology Letters, vol.167, issue.2, pp.179-184, 1998.
DOI : 10.1111/j.1574-6968.1998.tb13225.x

R. Lévy and M. Maaloum, Measuring the spring constant of atomic force microscope cantilevers: thermal fluctuations and other methods, Nanotechnology, vol.13, issue.1, pp.33-37, 2002.
DOI : 10.1088/0957-4484/13/1/307

C. Novotny, J. Carnahan, and C. C. Brinton, Mechanical removal of F pili type-I pili and flagella from Hfr and RTF donor cells and kinetics of their reappearance, J. Bacteriol, vol.98, pp.1294-1306, 1969.

V. Vadillo-rodríguez, H. J. Busscher, W. Norde, J. De-vries, R. J. Dijkstra et al., Comparison of Atomic Force Microscopy Interaction Forces between Bacteria and Silicon Nitride Substrata for Three Commonly Used Immobilization Methods, Applied and Environmental Microbiology, vol.70, issue.9, pp.5441-5446, 2004.
DOI : 10.1128/AEM.70.9.5441-5446.2004

A. Méndez-vilas, A. M. Gallardo-moreno, and M. L. González-martín, Nano-mechanical exploration of the surface and sub-surface of hydrated cells of Staphylococcus epidermidis, Antonie van Leeuwenhoek, vol.21, issue.22, pp.373-386, 2006.
DOI : 10.1007/s10482-005-9041-y

L. Zhao, D. Schaefer, H. Xu, S. Modi, W. Lacourse et al., Elastic Properties of the Cell Wall of Aspergillus nidulans Studied with Atomic Force Microscopy, Biotechnology Progress, vol.23, issue.1, pp.292-299, 2005.
DOI : 10.1021/bp0497233

P. Schar-zammaretti and J. Ubbink, The Cell Wall of Lactic Acid Bacteria: Surface Constituents and Macromolecular Conformations, Biophysical Journal, vol.85, issue.6, pp.4076-4092, 2003.
DOI : 10.1016/S0006-3495(03)74820-6

S. D. Bentley, D. M. Aanensen, A. Mavroidi, D. Saunders, .. M. Ester-rabbinowitsch et al., Genetic Analysis of the Capsular Biosynthetic Locus from All 90 Pneumococcal Serotypes, PLoS Genetics, vol.338, issue.3, pp.262-269, 2006.
DOI : 10.1371/journal.pgen.0020031.st004

C. Landersjö, Z. Yang, E. Huttunen, and G. Widmalm, strain GG (ATCC 53103), Biomacromolecules, vol.3, issue.4, pp.880-884, 2002.
DOI : 10.1021/bm020040q

E. Meléndez-hevia, T. G. Waddell, and E. D. Shelton, Optimization of molecular design in the evolution of metabolism: the glycogen molecule, Biochemical Journal, vol.295, issue.2, pp.477-483, 1993.
DOI : 10.1042/bj2950477

R. Meléndez, E. Meléndez-hevia, and E. I. Canela, The Fractal Structure of Glycogen: A Clever Solution to Optimize Cell Metabolism, Biophysical Journal, vol.77, issue.3, pp.1327-1332, 1999.
DOI : 10.1016/S0006-3495(99)76982-1

Y. Dufrêne and P. Hinterdorfer, Recent progress in AFM molecular recognition studies, Pfl??gers Archiv - European Journal of Physiology, vol.2, issue.1, pp.237-245, 2008.
DOI : 10.1007/s00424-007-0413-1

N. Saitô, K. Takahashi, and Y. Yunoki, The Statistical Mechanical Theory of Stiff Chains, Journal of the Physical Society of Japan, vol.22, issue.1, pp.219-226, 1967.
DOI : 10.1143/JPSJ.22.219

W. Wang, K. A. Kistler, K. Sadeghipour, and G. Baran, Molecular dynamics simulation of AFM studies of a single polymer chain, Physics Letters A, vol.372, issue.47, pp.7007-7010, 2008.
DOI : 10.1016/j.physleta.2008.10.041

M. Kuhn, H. Janovjak, M. Hubain, and D. Müller, Automated alignment and pattern recognition of single-molecule force spectroscopy data, Journal of Microscopy, vol.75, issue.2, pp.125-132, 2005.
DOI : 10.1186/gb-2003-4-5-r34

M. Sullivan, F. Vilaplana, R. Cave, D. Stapleton, A. Gray-weale et al., Nature of ?? and ?? Particles in Glycogen Using Molecular Size Distributions, Biomacromolecules, vol.11, issue.4, pp.1094-1100, 2010.
DOI : 10.1021/bm100074p

G. A. Morris, S. Ang, S. E. Hill, S. Lewis, B. Schäfer et al., Molar mass and solution conformation of branched ??(1???4), ??(1???6) Glucans. Part I: Glycogens in water, Carbohydrate Polymers, vol.71, issue.1, pp.101-108, 2008.
DOI : 10.1016/j.carbpol.2007.05.029

P. D. O-'grady, B. A. Pearlmutter, and S. T. Rickard, Survey of sparse and non-sparse methods in source separation, Tech., special issue on Blind Source Separation and De-convolution in Imaging and Image Processing, pp.18-33, 2005.
DOI : 10.1002/ima.20035

C. E. Cherry, Some Experiments on the Recognition of Speech, with One and with Two Ears, The Journal of the Acoustical Society of America, vol.25, issue.5, pp.975-979, 1953.
DOI : 10.1121/1.1907229

S. Moussaoui, Séparation de sources non-négatives Application au traitement des signaux de spectroscopie, 2005.

P. Comon, Independent component analysis, A new concept?, Signal Processing, vol.36, issue.3, pp.287-314, 1994.
DOI : 10.1016/0165-1684(94)90029-9
URL : https://hal.archives-ouvertes.fr/hal-00417283

A. Hyvärinen, Survey on independent component analysis, Neural Computing Surveys, vol.2, pp.94-128, 1999.

J. Cardoso and A. Souloumiac, Blind beamforming for non-gaussian signals, IEE Proceedings F Radar and Signal Processing, vol.140, issue.6, pp.362-370, 1993.
DOI : 10.1049/ip-f-2.1993.0054

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

R. Gribonval and S. Lesage, A survey of sparse component analysis for blind source separation : principles, perspectives, and new challenges, ESANN, pp.323-330, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544897

P. Comon and C. Jutten, Handbook of Blind Source Separation, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00460653

S. Makino, H. Sawada, R. Mukai, and S. Araki, Blind Source Separation of Convolutive Mixtures of Speech in Frequency Domain, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.88, issue.7, pp.1640-1655, 2005.
DOI : 10.1093/ietfec/e88-a.7.1640

D. D. Lee and H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, issue.6755, pp.788-791, 1999.

P. O. Hoyer, Non-negative sparse coding, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pp.557-565, 2002.
DOI : 10.1109/NNSP.2002.1030067

K. Torkkola, Blind separation of convolved sources based on information maximization, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop, pp.423-432, 1996.
DOI : 10.1109/NNSP.1996.548372

P. Bofill and M. Zibulevsky, Underdetermined blind source separation using sparse representations, Signal Processing, vol.81, issue.11, pp.2353-2362, 2001.
DOI : 10.1016/S0165-1684(01)00120-7

O. Yilmaz and S. Rickard, Blind Separation of Speech Mixtures via Time-Frequency Masking, IEEE Transactions on Signal Processing, vol.52, issue.7, pp.1830-1847, 2004.
DOI : 10.1109/TSP.2004.828896

R. Lambert, Multichannel blind deconvolution : FIR matrix algebra and separation of multipath mixtures, 1996.

M. S. Pedersen, J. Larsen, U. Kjems, and L. C. Parra, A survey of convolutive blind source separation methods, 2007.

M. G. Jafari, E. Vincent, S. A. Abdallah, M. D. Plumbley, and M. E. Davies, An adaptive stereo basis method for convolutive blind audio source separation, Neurocomputing, vol.71, issue.10-12, pp.10-12, 2008.
DOI : 10.1016/j.neucom.2007.08.029
URL : https://hal.archives-ouvertes.fr/inria-00544258

S. Moussaoui, H. Hauksdóttir, F. Schmidt, C. Jutten, J. Chanussot et al., On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation, Neurocomputing, vol.71, issue.10-12, pp.10-12, 2008.
DOI : 10.1016/j.neucom.2007.07.034
URL : https://hal.archives-ouvertes.fr/hal-00272349

N. Bali and A. Mohammad-djafari, Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis, IEEE Transactions on Image Processing, vol.17, issue.2, pp.217-225, 2008.
DOI : 10.1109/TIP.2007.914227
URL : https://hal.archives-ouvertes.fr/hal-00449832

N. Cho and C. J. Kuo, Underdetermined audio source separation from anechoic mixtures with long time delay, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1557-1560, 2009.
DOI : 10.1109/ICASSP.2009.4959894

]. R. Saab, ¨. O. Yilmaz, M. J. Mckeown, and R. Abugharbieh, Underdetermined Anechoic Blind Source Separation via <formula formulatype="inline"> <tex>$\ell^{q}$</tex></formula>-Basis-Pursuit With <formula formulatype="inline"> <tex>$q&#x226A;1$</tex></formula>, IEEE Transactions on Signal Processing, vol.55, issue.8, pp.4004-4017, 2007.
DOI : 10.1109/TSP.2007.895998

P. Dierckx, Curve and surface fitting with splines, Monographs on Numerical Analysis, 1995.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

S. Verdu, Multiuser Detection, 1998.

. Du-point-de-vue-méthodologique, des algorithmes sous-optimaux sont proposés pour leprobì eme de l'approximation parcimonieuse basée sur la pseudo-norme 0 : l'algorithme Single Best Replacement (SBR) est un algorithme itératif de type " ajout-retrait " inspiré d'algorithmes existants pour la restauration de signaux Bernoulli-gaussiens. L'algorithme Continuation Single Best Replacement (CSBR) est un algorithme permettant de fournir des approximationsàapproximationsà des degrés de parcimonie variables. Nous proposons aussi un algorithme de séparation de sources parcimonieusesàparcimonieusesà partir de mélanges avec retards, basé sur l'application préalable de l'algorithme CSBR sur chacun des mélanges