M. P. Stumpf, Estimating the size of the human interactome, Proc Natl Acad Sci, vol.105, pp.6959-64, 2008.

Y. Qi, Z. Bar-joseph, and J. Klein-seetharaman, Evaluation of different biological data and computational classification methods for use in protein interaction prediction, Proteins, vol.63, issue.3, pp.490-500, 2006.

G. T. Hart, A. K. Ramani, and E. M. Marcotte, How complete are current yeast and human proteininteraction networks?, Genome Biol, vol.7, issue.11, p.120, 2006.

S. Fields and O. Song, A novel genetic system to detect protein-protein interactions, Nature, vol.340, issue.6230, pp.245-251, 1989.

Y. Ho, Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature, vol.415, issue.6868, pp.180-183, 2002.

B. Settles, Active Learning Literature Survey, 2009.

H. Nguyen and A. Smeulders, Active Learning using Pre-clustering. International Conference on Machine Learning (ICML), pp.623-630, 2004.

C. Campbell, N. Cristianini, and A. Smola, Query Learning with Large Margin Classifiers, Proceedings of the Seventeenth International Conference on Machine Learning, 2000.

J. Pinar-donmez and P. Bennett, Dual Strategy Active Learning. Proceedings of the 18th European conference on Machine Learning, 2007.

T. Mohamed, J. Carbonell, and M. Ganapathiraju, Active learning for human protein-protein interaction prediction, BMC Bioinformatics, issue.11, p.57, 2010.

Y. Chen and D. Xu, Computational analyses of high-throughput protein-protein interaction data, Curr Protein Pept Sci, vol.4, issue.3, pp.159-81, 2003.

P. Donmez and J. G. Carbonell, Proactive learning: cost-sensitive active learning with multiple imperfect oracles, Proceedings of the 17th ACM conference on Information and knowledge management, 2008.

K. Prasad and T. S. , Human Protein Reference Database--2009 update, Nucleic Acids Res, vol.37, pp.767-72, 2009.

K. Venkatesan, An empirical framework for binary interactome mapping, Nat Methods, vol.6, issue.1, pp.83-90, 2009.

C. Elkan and K. Noto, Learning classifiers from only positive and unlabeled data, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008.

R. Jansen, A Bayesian networks approach for

S. Ouyang, W. Zhu, J. Hamilton, H. Lin, M. Campbell et al., The TIGR Rice Genome Annotation Resource: improvements and new features, Nucleic Acids Res, vol.35, pp.883-890, 2007.

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat Genet, vol.25, pp.25-34, 2000.

J. Schug, W. P. Schuller, C. Kappen, J. M. Salbaum, M. Bucan et al., Promoter features related to tissue specificity as measured by Shannon entropy, Genome Biol, vol.6, p.33, 2005.

L. B. Timothy and E. Charles, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp.28-36, 1994.

S. Gupta, J. A. Stamatoyannopoulos, T. L. Bailey, and W. S. Noble, Quantifying similarity between motifs, Genome Biol, vol.8, p.24, 2007.

G. E. Crooks, G. Hon, J. M. Chandonia, and S. E. Brenner, WebLogo: a sequence logo generator, Genome Res, vol.14, pp.1188-90, 2004.

P. Gonnet, S. Dimopoulos, L. Widmer, and J. Stelling, A specialized ODE integrator for the efficient computation of parameter sensitivities, BMC Systems Biology, vol.6, 2012.

R. P. Brent, Some long-period random number generators using shifts and xors, CoRR, 2010.

M. D. Morris, Factorial sampling plans for preliminary computational experiments, Technometrics, vol.33, issue.2, pp.161-174, 1991.

F. Campolongo, J. Cariboni, and A. Saltelli, An effective screening design for sensitivity analysis of large models, Environmental Modelling & Software, vol.22, issue.10, pp.1509-1518, 2007.

A. Eldar and M. B. Elowitz, Functional roles for noise in genetic circuits, Nat, vol.467, issue.9, pp.1-7, 2010.

V. Shahrezaei and P. S. Swain, Analytical distributions for stochastic gene expression, Proc. Natl. Acad. Sci. U S A, vol.105, issue.45, pp.17256-17261, 2008.

D. T. Gillespie, Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem, vol.81, issue.25, pp.2340-2361, 1977.

H. E. Samad, M. Khammash, L. Petzold, and D. Gillespie, Stochastic modelling of gene regulatory networks, Int. J. Robust Nonlinear Control, vol.15, issue.15, pp.691-711, 2005.

B. Munsky and M. Khammash, The finite state projection algorithm for the solution of the chemical master equation, J. Chem. Phys, vol.124, issue.4, p.44104, 2006.

S. Engblom, Computing the moments of high dimensional solutions of the master equation, Appl. Math. Comp, vol.180, pp.498-515, 2006.

J. P. Hespanha, Modeling and analysis of stochastic hybrid systems, IEE Proc. Control Theory & Applications, Special Issue on Hybrid Systems, vol.153, issue.5, pp.520-535, 2007.

J. Ruess, A. Milias, S. Summers, and J. Lygeros, Moment estimation for chemically reacting systems by extended Kalman filtering, J. Chem. Phys, vol.135, issue.165102, 2011.

P. Milner, C. S. Gillespie, and D. J. Wilkinson, Moment closure based parameter inference of stochastic kinetic models, Stat. Comp, 2012.

M. Mateescu, V. Wolf, F. Didier, and T. Henzinger, Fast adaptive uniformisation of the chemical master equation, IET. Syst. Biol, vol.4, issue.6, pp.441-452, 2010.

A. Singh and J. P. Hespanha, Approximate moment dynamics for chemically reacting systems, IEEE Trans. Autom. Control, vol.56, issue.2, pp.414-418, 2011.

J. Hasenauer, N. Radde, M. Doszczak, P. Scheurich, and F. Allgöwer, Parameter estimation for the CME from noisy binned snapshot data: Formulation as maximum likelihood problem, Extended abstract at Conf. of Stoch, 2011.

T. Nüesch, Finite state projection-based parameter estimation algorithms for stochastic chemical kinetics, 2010.

C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter et al., Moment-based inference predicts bimodality in transient gene expression, Proc. Nati. Acad. Sci. U S A, vol.109, issue.21, pp.8340-8345, 2012.

T. J. Diciccio and B. Efron, Bootstrap confidence intervals, Statist. Sci, vol.11, issue.3, pp.189-228, 1996.

A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling et al., Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood, Bioinf, vol.25, issue.25, pp.1923-1929, 2009.

W. Q. Meeker and L. A. Escobar, Teaching about approximate confidence regions based on maximum likelihood estimation, Am. Stat, vol.49, issue.1, pp.48-53, 1995.

B. Munsky, B. Trinh, and M. Khammash, Listening to the noise: random fluctuations reveal gene network parameters, Mol. Syst. Biol, vol.5, issue.318, 2009.

J. Hasenauer, V. Wolf, A. Kazeroonian, and F. J. Theis, Method of conditional moments (MCM) for the chemical master equation, Journal of Mathematical Biology, 2012.

C. Mora, D. P. Tittensor, S. Adl, A. G. Simpson, and B. Worm, How many species are there on earth and in the ocean, PLoS Biology, vol.9, 2011.

J. C. Avise, J. Arnold, R. M. Ball, E. Bermingham, T. Lamb et al., Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics, Ann. Rev. Ecol. Syst, vol.18, pp.489-522, 1987.

C. Moritz, T. E. Dowling, and W. M. Brown, Evolution of animal mitochondrial DNA: relevance for population biology and systematics, Ann. Rev. Ecol. Syst, vol.18, pp.269-292, 1987.

D. J. White, J. N. Wolff, M. Pierson, and N. J. Gemmell, Revealing the hidden complexities of mtDNA inheritance, Mol. Ecol, vol.17, pp.4925-4942, 2008.

M. Kimura, The neutral theory of molecular evolution, 1983.

W. M. Brown, M. George, J. , and A. C. Wilson, Rapid evolution of animal mitochondrial DNA, Proc. Natl. Acad. Sci. USA, vol.76, 1967.

N. Galtier, B. Nabholz, S. Glémin, and G. D. Hurst, Mitochondrial DNA as a marker of molecular diversity: a reappraisal, Mol. Ecol, vol.18, pp.4541-4550, 2009.

P. D. Hebert, Biological identifications through DNA barcodes, Proc. R. Soc. B: Biological Sciences, vol.270, pp.313-321, 2003.

C. Moritz and C. Cicero, DNA barcoding: promise and pitfalls, PLoS Biology, vol.2, pp.1529-1531, 2004.

H. R. Taylor and W. E. Harris, An emergent science on the brink of irrelevance: a review of the past 8 years of DNA barcoding, Mol. Ecol. Resour, vol.12, pp.377-388, 2012.

F. Austerlitz, O. David, B. Schaeffer, K. Bleakley, M. Olteanu et al., DNA barcode analysis: a comparison of phylogenetic and statistical classification methods, BMC Bioinformatics, vol.10, p.10, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00663565

A. L. Bazinet and M. P. Cummings, A comparative evaluation of sequence classification programs, BMC Bioinformatics, vol.13, p.92, 2012.

A. Zhang, C. Muster, H. Liang, C. Zhu, R. Crozier et al., A fuzzy-settheory-based approach to analyse species membership in DNA barcoding, Mol. Ecol, vol.21, pp.1848-1863, 2012.

R. Desalle, Species Discovery versus Species Identification in DNA Barcoding Efforts: Response to Rubinoff, Conservation Biology, vol.20, issue.5, pp.1545-1547

A. M. Feist, C. S. Henry, J. L. Reed, M. Krummenacker, A. R. Joyce et al., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260

, ORFs and thermodynamic information, Molecular Systems Biology, vol.3, p.121, 2007.

N. Mnatsakanyan, K. Bagramyan, and A. Trchounian, Hydrogenase 3 but not hydrogenase 4 is major in hydrogen gas production by Escherichia coli formate hydrogenlyase at acidic pH and in the presence of external formate, Cell Biochemistry and Biophysics, vol.41, pp.357-365, 2004.

J. D. Orth, I. Thiele, and B. Ø. Palsson, What is flux balance analysis?, Nature Biotechnology, vol.28, pp.245-248, 2010.

J. S. Edwards, R. U. Ibarra, and B. Ø. Palsson, In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data, Nature Biotechnology, vol.19, pp.125-130, 2001.

T. Baba, T. Ara, M. Hasegawa, Y. Takai, Y. Okumura et al., Construction of Escherichia coli K-12 in-frame, singlegene knockout mutants: the Keio collection, Molecular Systems Biology, issue.2, p.8, 2006.

J. J. Seppälä, J. A. Puhakka, O. Yli-harja, M. T. Karp, and V. Santala, Fermentative hydrogen production by Clostridium butyricum and Escherichia coli in pure and cocultures, International Journal of Hydrogen Energy, vol.36, pp.10701-10708, 2011.

J. J. Seppälä, A. Larjo, T. Aho, O. Yli-harja, M. T. Karp et al., Prospecting hydrogen production of Escherichia coli by metabolic network modeling

J. Soini, K. Ukkonen, and P. Neubauer, High cell density media for Escherichia coli are generally designed for aerobic cultivations -consequences for large-scale bioprocesses and shake flask cultures, Microbial Cell Factories, vol.7, p.26, 2008.

A. Kivistö, V. Santala, and M. Karp, Hydrogen production from glycerol using halophilic fermentative bacteria, Bioresource Technology, vol.101, pp.8671-8678, 2010.

S. A. Becker, A. M. Feist, M. L. Mo, G. Hannum, B. Ø. Palsson et al., Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox, Nature Protocols, vol.2, pp.727-738, 2007.

M. Inc, Matlab R2011b

D. A. Fell and J. R. Small, Fat synthesis in adipose tissue. An examination of stoichiometric constraints, Biochemical Journal, vol.238, pp.781-786, 1986.

M. R. Parikh, D. N. Greene, K. K. Woods, and I. Matsumura, Directed evolution of RuBisCO hypermorphs through genetic selection in engineered E. coli, Protein Engineering, Design and Selection, vol.19, pp.113-119, 2006.

P. K. Bunch, F. Mat-jan, N. Lee, and D. P. Clark, The IdhA Gene Encoding the fermentative lactate dehydrogenase of Escherichia coli, Microbiology, vol.143, pp.187-195, 1997.

A. Yoshida, T. Nishimura, H. Kawaguchi, M. Inui, and H. Yukawa, Enhanced hydrogen production from glucose using ldh-and frd-inactivated Escherichia coli strains, Applied Microbiology and Biotechnology, vol.73, pp.67-72, 2006.

A. J. Wolfe, The Acetate Switch, Microbiology and Molecular Biology Reviews, vol.69, pp.12-50, 2005.

C. Hesslinger, S. A. Fairhurst, and G. Sawers, Novel keto acid formate-lyase and propionate kinase enzymes are components of an anaerobic pathway in Escherichia coli that degrades L-threonine to propionate, Molecular Microbiology, vol.27, pp.477-492, 1998.

I. Mizrahi, D. Biran, and E. Z. Ron, Requirement for the acetyl phosphate pathway in Escherichia coli ATPdependent proteolysis, Molecular Microbiology, vol.62, pp.201-212, 2006.

F. P. Bologna, V. A. Campos-bermudez, D. D. Saavedra, C. S. Andreo, and M. F. Drincovich, Characterization of Escherichia coli EutD: a phosphotransacetylase of the ethanolamine operon, The Journal of Microbiology, vol.48, pp.629-636, 2010.

A. Ferrández, J. L. Garciá, and E. Díaz, Genetic characterization and expression in heterologous hosts of the 3-(3-hydroxyphenyl)propionate catabolic pathway of Escherichia coli K-12, Journal of Bacteriology, vol.179, pp.2573-81, 1997.

M. Schurmann and G. A. Sprenger, Fructose-6-phosphate aldolase is a novel class I aldolase from Escherichia coli and is related to a novel group of bacterial transaldolases, The Journal of Biological Chemistry, vol.276, pp.11055-61, 2001.

I. M. Keseler, J. Collado-vides, A. Santos-zavaleta, M. Peralta-gil, S. Gama-castro et al., EcoCyc: a comprehensive database of Escherichia coli biology, Nucleic Acids Research, vol.39, pp.583-590, 2011.

L. Stols and M. I. Donnelly, Production of succinic acid through overexpression of NAD(+)-dependent malic enzyme in an Escherichia coli mutant, Applied and Environmental Microbiology, vol.63, pp.2695-701, 1997.

F. Mat-jan, C. R. Williams, and D. P. Clark, Anaerobic growth defects resulting from gene fusions affecting succinyl-CoA synthetase in Escherichia coli K12, Molecular and General Genetics, vol.215, pp.276-280, 1989.

H. Engl, M. Hanke, and A. Neubauer, Regularization of inverse problems, 1996.

J. Kaipio and E. Somersalo, Statistical and computational inverse problems, 2005.

M. Ashyraliyev, J. Jaeger, and J. Blom, Parameter estimation and determinability analysis applied to drosophila gap gene circuits, BMC Systems Biology, vol.2, p.83, 2008.

P. Kuegler, E. Gaubitzer, and S. Mueller, Parameter identification for chemical reaction systems using sparsity enforcing regularization -a case study for the chlorite -iodide reaction, Journal of Physical Chemistry A, vol.12, pp.2775-2785, 2009.

H. Engl, C. Flamm, P. Kuegler, J. Lu, S. Mueller et al., Inverse problems in systems biology, Inverse Problems, vol.25, issue.12, 2009.

P. W. Ingham, The molecular genetics of embryonic pattern formation in Drosophila, Nature, vol.335, pp.25-34, 1988.

M. Akam, The molecular basis for metameric pattern in the Drosophila embryo, Development, vol.101, pp.1-22, 1987.

C. Nusslein-volhard and E. Wieschaus, Mutations affecting segment number and polarity in Drosophila, Nature, vol.287, pp.795-801, 1980.

V. Foe and B. Alberts, Studies of nuclear and cytoplasmic behaviour during the five mitotic cycles that precede gastrulation in drosophila embryogenesis, The Journal of Cell Science, vol.61, pp.31-70, 1983.

J. Reinitz and D. Sharp, Mechanism of eve stripe formation, Mech. Dev, vol.49, pp.133-158, 1995.

J. Jaeger, M. Blagov, K. Kozlov, E. Myasnikova, S. Surkova et al., Dynamic analyses of regulatory interactions in the gap gene system of drosophila melanogaster, Genetics, vol.167, pp.1721-1737, 2004.

J. Jaeger, S. Surkova, M. Blagov, H. Janssens, D. Kossman et al., Dynamic control of positional information in the early drosophila embryo, Nature, vol.430, pp.368-371, 2004.

J. Lu, S. Muller, R. Machne, and F. C. , Smbl ode solver library: Extensions for inverse problems, Proceedings of the fifth workshop of on computational systems Biology, 2008.

M. Grasmair, Well-posedness and convergence rates for sparse regularization with sublinear l q penalty term, Inverse Probl. Imaging, vol.3, issue.3, pp.383-387, 2009.

H. Haario, M. Laine, and A. Mira, Dram: Efficient adaptive mcmc, Statistics and Computing, vol.16, issue.4, pp.339-354, 2006.

J. Jaeger, The gap gene network, Cell Mol. Life Sci, vol.68, pp.243-274, 2011.

K. Becker, A quantitative study of translational regulation in drosophila segment determination, 2013.

S. H. Strogatz, Exploring complex networks, Nature, vol.410, pp.268-276, 2001.

S. A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, Journal of Theoretical Biology, vol.22, pp.437-467, 1969.

E. Dubrova, M. Teslenko, and A. Martinelli, Kauffman networks: analysis and applications, Computer-Aided Design, 2005.

, IEEE/ACM International Conference on, pp.479-484, 2005.

B. Derrida and Y. Pomeau, Random networks of automata: A simple annealed approximation, Europhysics Letters, vol.1, pp.45-49, 1986.

U. Alon, Network motifs: theory and experimental approaches, Nature Reviews Genetics, vol.8, pp.450-461, 2007.

V. Picard, Modeling of topological effects in biological networks

, Accessed, 2013.

B. Derrida and G. Weisbuch, Evolution of overlaps between configurations in random Boolean networks, Journal de Physique, vol.47, pp.1297-1303, 1986.
URL : https://hal.archives-ouvertes.fr/jpa-00210321

D. J. Murphy and I. Cummins, Seed oil-bodies: Isolation, composition and role of oil-body apolipoproteins, Phytochemistry, vol.28, issue.8, pp.2063-2069, 1989.

A. Huang, Oleosins and oil bodies in seeds and other organs, Plant Physiology, vol.110, issue.4, pp.1055-1061, 1996.

H. Yang, A. Galea, V. Sytnyk, and M. Crossley, Controlling the size of lipid droplets: lipid and protein factors, Current Opinion in Cell Biology, vol.24, issue.4, pp.509-516, 2012.

P. Jolivet, E. Roux, S. Dandrea, M. Davanture, L. Negroni et al., Protein composition of oil bodies in arabidopsis thaliana ecotype WS, Plant Physiology and Biochemistry, vol.42, issue.6, pp.501-509, 2004.
URL : https://hal.archives-ouvertes.fr/hal-02680055

R. M. Siloto, K. Findlay, A. Lopez-villalobos, E. C. Yeung, C. L. Nykiforuk et al., The accumulation of oleosins determines the size of seed oilbodies in arabidopsis, The Plant Cell Online, vol.18, issue.8, pp.1961-1974, 2006.

R. Koenker and G. Bassett, Regression quantiles, Econometrica, vol.46, issue.1, p.33, 1978.

J. Levin, For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement, Empirical Economics, vol.26, issue.1, pp.221-246, 2001.

D. Makowski, T. Dor, and H. Monod, A new method to analyse relationships between yield components with boundary lines, Agronomy for Sustainable Development, vol.27, pp.119-128, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00886363

J. Boulanger, C. Kervrann, P. Bouthemy, P. Elbau, J. Sibarita et al., Patch-based nonlocal functional for denoising fluorescence microscopy image sequences, IEEE transactions on medical imaging, vol.29, issue.2, pp.442-454, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00541082

F. M. White, Viscous Fluid Flow, 1991.

J. Cisonni, A. Van-hirtum, X. Pelorson, and J. Willems, Theoretical simulation and experimental validation of inverse quasi one-dimensional steady and unsteady glottal flow models, J. Acoust. Soc. Am, vol.124, pp.535-545, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00321308

B. Wu, A. Van-hirtum, and X. Y. Luo, Pressure driven steady flow in constricted channels of different cross section shapes, Int. J. Applied Mechanics
URL : https://hal.archives-ouvertes.fr/hal-00809367

H. Schlichting and K. Gersten, Boundary Layer Theory, 2000.

P. R. Adhikari and J. Hollmén, Multiresolution Mixture Modeling using Merging of Mixture Components, Proceedings of the Fourth Asian Conference on Machine Learning, vol.25, pp.17-32, 2012.

P. R. Adhikari and J. Hollmén, Fast Progressive Training of Mixture Models for Model Selection, Proceedings of Fifteenth International Conference on Discovery Science, vol.7569, pp.194-208, 2012.

, FI-20520 Turku, Finland 4 National Doctoral Programme in Informational and Structural Biology

F. Espoo,

, USA & Department of Cellular and Molecular Medicine, 92093.

J. Small, T. Stradal, E. Vignal, and K. Rottner, The lamellipodium: where motility begins, Trends Cell Biol, vol.12, pp.112-132, 2002.

D. Oelz, C. Schmeiser, and J. Small, Modelling of the actin-cytoskeleton in symmetric lamellipodial fragments, Cell Adhesion and Migration, pp.117-126, 2008.

D. Oelz and C. Schmeiser, How do cells move? mathematical modelling of cytoskeleton dynamics and cell migration, 2009.

T. M. Cover and J. A. Thomas, Elements of Information Theory, 2006.

S. P. Lloyd, Least squares quantization in PCM, IEEE Trans. Inform. Theory, vol.28, issue.2, pp.129-137, 1982.

A. G. Stefani, J. B. Huber, C. Jardin, and H. Sticht, Confidence intervals for the mutual information

M. Smith and J. , Evolution and the theory of games, 1982.

M. Smith, J. Price, and G. R. , The Logic of Animal Conflict, Nature, pp.16-18, 1973.

J. Von-neuman, Theory of self reproducing automata, 1966.

M. A. Nowak and R. M. May, Evolutionary games and spatial chaos, Nature, vol.18, pp.826-829, 1992.

J. Hofbauer and K. Sigmund, Evolutionary game dynamics, Bull. Amer. Math. Soc, vol.40, pp.479-519, 2003.

I. P. Tomlinson and W. F. Bodmer, Modeling the consequences of interactions between tumour cells, British Journal of Cancer, vol.75, pp.157-180, 1997.

A. Raj and A. Van-oudenaarden, Annu. Rev. Biophys, vol.38, pp.255-270, 2009.

D. T. Gillespie, Journal of Computational Physics, vol.22, issue.4, pp.403-434, 1976.

J. Pahle, Briefings in Bioinformatics, vol.10, issue.1, pp.53-64, 2009.

C. Zimmer and S. Sahle, Journal of Computer Science & Systems Biology, vol.6, pp.11-021, 2012.

T. S. Gardner, C. R. Cantor, and J. J. Collins, Letters to Nature, vol.403, pp.339-342, 2000.

B. Munsky and M. Khammash, IET Systems Biology, vol.4, issue.6, pp.356-366, 2010.

C. S. Gillespie and A. Golightly, Applied Statistics, vol.59, issue.2, pp.341-357, 2009.

T. Tian, S. Xu, J. Gao, and K. Burrage, DERIVATIVE PROCESSES FOR MODELLING METABOLIC FLUXES, vol.23, pp.84-91, 2007.

, Recently proposed methods for the analysis of metabolic pathways, for example dynamic flux estimation, can only provide estimates of the underlying fluxesin a point-wise fashion at discrete time-points (mostly, in fact, just a single time-point) but fail to capture the complete temporal behaviour. In order to describe the dynamic variation of the fluxes we additionally require the assumption of specific functional forms that can capture the temporal behaviour. Here we propose a novel approach to modelling metabolic fluxes

F. Tampere and . Isbn,