N. Angelopoulos and S. H. Muggleton, Machine learning metabolic pathway descriptions using a probabilistic relational representation, also in Proceedings of Machine Intelligence, 2002.

N. Angelopoulos and S. H. Muggleton, Slps for probabilistic pathways: Modeling and parameter estimation, 2002.

G. Bernot, J. P. Comet, A. Richard, and J. Guespin, Application of formal methods to biological regulatory networks: extending Thomas? asynchronous logical approach with temporal logic, Journal of Theoretical Biology, vol.229, issue.3, pp.339-347, 2004.
DOI : 10.1016/j.jtbi.2004.04.003

C. H. Bryant, S. H. Muggleton, S. G. Oliver, D. B. Kell, P. G. Reiser et al., Combining inductive logic programming, active learning and robotics to discover the function of genes, Electronic Transactions in Artificial Intelligence, vol.6, issue.12, 2001.

L. Calzone, N. Chabrier-rivier, F. Fages, and S. Soliman, Machine Learning Biochemical Networks from Temporal Logic Properties, Transactions on Computational Systems Biology VI Lecture Notes in BioInformatics, vol.4220, pp.68-94, 2006.
DOI : 10.1007/11880646_4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

K. C. Chen, L. Calzone, A. Csikász-nagy, F. R. Cross, B. Györffy et al., Integrative Analysis of Cell Cycle Control in Budding Yeast, Molecular Biology of the Cell, vol.15, issue.8, pp.3841-3862, 2004.
DOI : 10.1091/mbc.E03-11-0794

K. Deng, C. Bourke, S. D. Scott, J. Sunderman, and Y. Zheng, Bandit-Based Algorithms for Budgeted Learning, Seventh IEEE International Conference on Data Mining (ICDM 2007), p.ICDM, 2007.
DOI : 10.1109/ICDM.2007.91

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

K. Deng, Y. Zheng, C. Bourke, S. Scott, and J. Masciale, New algorithms for budgeted learning, Machine Learning, vol.1, issue.6, pp.10994-11006, 2013.
DOI : 10.2307/3001968

F. Fages, T. Martinez, D. Rosenblueth, and S. Soliman, Influence Systems vs Reaction Systems, CMSB'16: Proceedings of the fourteenth international conference on Computational Methods in Systems Biology, pp.98-115, 2016.
DOI : 10.1016/0022-5193(73)90247-6

URL : https://hal.archives-ouvertes.fr/hal-01378470

F. Fages and S. Soliman, Abstract interpretation and types for systems biology, Theoretical Computer Science, vol.403, issue.1, pp.52-70, 2008.
DOI : 10.1016/j.tcs.2008.04.024

URL : https://hal.archives-ouvertes.fr/hal-01431355

M. Gebser, B. Kaufmann, A. Neumann, and T. Schaub, clasp: A Conflict-Driven Answer Set Solver, Proc. LPNMR'07, pp.260-265, 2007.
DOI : 10.1007/978-3-540-72200-7_23

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Gebser, T. Schaub, S. Thiele, B. Usadel, and P. Veber, Detecting Inconsistencies in Large Biological Networks with Answer Set Programming, Proceedings of the 24th International Conference on Logic Programming, pp.130-144, 2008.
DOI : 10.1073/pnas.97.7.3364

URL : http://arxiv.org/abs/1007.0134

D. T. Gillespie, Exact stochastic simulation of coupled chemical reactions, The Journal of Physical Chemistry, vol.81, issue.25, pp.2340-2361, 1977.
DOI : 10.1021/j100540a008

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

A. D. Gordon, T. A. Henzinger, A. V. Nori, and S. K. Rajamani, Probabilistic programming, Proceedings of the on Future of Software Engineering, FOSE 2014, pp.167-181, 2014.
DOI : 10.1145/2593882.2593900

URL : https://hal.archives-ouvertes.fr/hal-01400890

S. M. Hill, L. M. Heiser, T. Cokelaer, M. Unger, N. K. Nesser et al., Inferring causal molecular networks: empirical assessment through a community-based effort, Nature Methods, vol.4, issue.4, pp.310-318, 2016.
DOI : 10.12688/f1000research.7118.1

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854847

A. Llamosi, A. Mezine, F. Buc, V. Letort, and M. Sebag, Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach, Machine Learning and Knowledge Discovery in Databases ECML PKKDD'14, pp.306-321, 2014.
DOI : 10.1007/978-3-662-44851-9_20

URL : https://hal.archives-ouvertes.fr/hal-01109775

L. Mendoza, A network model for the control of the differentiation process in Th cells, Biosystems, vol.84, issue.2, pp.101-114, 2006.
DOI : 10.1016/j.biosystems.2005.10.004

P. Meyer, T. Cokelaer, D. Chandran, K. H. Kim, P. R. Loh et al., Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach, BMC Systems Biology, vol.8, issue.1, pp.1-181752, 2014.
DOI : 10.1016/j.cell.2012.05.044

URL : http://doi.org/10.1186/1752-0509-8-13

S. H. Muggleton, Inverse entailment and progol, New Generation Computing, vol.12, issue.1, pp.245-286, 1995.
DOI : 10.1080/09528139408953784

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Ostrowski, L. Paulevé, T. Schaub, A. Siegel, and C. Guziolowski, Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming, Biosystems, vol.149, pp.139-153, 2016.
DOI : 10.1016/j.biosystems.2016.07.009

URL : https://hal.archives-ouvertes.fr/hal-01354075

E. Remy, P. Ruet, L. Mendoza, D. Thieffry, and C. Chaouiya, From Logical Regulatory Graphs to Standard Petri Nets: Dynamical Roles and Functionality of Feedback Circuits, pp.56-72, 2006.
DOI : 10.1007/11905455_3

R. Thomas, Boolean formalisation of genetic control circuits, Journal of Theoretical Biology, vol.42, pp.565-583, 1973.
DOI : 10.1016/0022-5193(74)90172-6

R. Thomas, Regulatory networks seen as asynchronous automata: A logical description, Journal of Theoretical Biology, vol.153, issue.1, pp.1-23, 1991.
DOI : 10.1016/S0022-5193(05)80350-9

L. Valiant, A theory of the learnable, Communications of the ACM, vol.27, issue.11, pp.1134-1142, 1984.
DOI : 10.1145/1968.1972

S. Videla, I. Konokotina, L. G. Alexopoulos, J. Saez-rodriguez, T. Schaub et al., Designing Experiments to Discriminate Families of Logic Models, Frontiers in Bioengineering and Biotechnology, vol.6, issue.131, 2015.
DOI : 10.1186/gb-2005-6-7-r62

URL : https://hal.archives-ouvertes.fr/hal-01196178