The Biochemical Abstract Machine (Biocham) is a modelling environment for systems biology, with some unique features for static analysis or for inferring unknown model parameters from temporal logic constraints. Biocham is mainly composed of : a rule-based language for modeling biochemical systems (compatible with SBML); several simulators (boolean, differential, stochastic), a temporal logic based language to formalize the temporal properties of a biological system and validate models with respect to such specifications, unique features for developing/correcting/completing/coupling models, including the inference of kinetic parameters in high dimension from temporal logic constraints. Biocham is a free software protected by the GNU General Public License GPL version 2. This is an Open Source license that allows free usage of this software. Feedback on the use of Biocham in applications, research or teaching are particularly welcomed.

References in zbMATH (referenced in 37 articles )

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  1. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2016)
  2. Ballarini, Paolo; Duflot, Marie: Applications of an expressive statistical model checking approach to the analysis of genetic circuits (2015)
  3. Bartocci, Ezio; Bortolussi, Luca; Nenzi, Laura; Sanguinetti, Guido: System design of stochastic models using robustness of temporal properties (2015)
  4. Chaves, Madalena; Carta, Alfonso: Attractor computation using interconnected Boolean networks: testing growth rate models in \itE. coli (2015)
  5. Fages, François; Gay, Steven; Soliman, Sylvain: Inferring reaction systems from ordinary differential equations (2015)
  6. Ito, Sohei; Ichinose, Takuma; Shimakawa, Masaya; Izumi, Naoko; Hagihara, Shigeki; Yonezaki, Naoki: Qualitative analysis of gene regulatory networks by temporal logic (2015)
  7. Pardini, Giovanni; Milazzo, Paolo; Maggiolo-Schettini, Andrea: Component identification in biochemical pathways (2015)
  8. Amar, Patrick; Paulevé, Loïc: HSIM: a hybrid stochastic simulation system for systems biology (2014)
  9. Chiarugi, Davide; Falaschi, Moreno; Hermith, Diana; Guzman, Michell; Olarte, Carlos: Simulating signalling pathways with bioways (2013)
  10. Degasperi, A.; Calder, M.: A process algebra framework for multi-scale modelling of biological systems (2013)
  11. Pardini, Giovanni; Milazzo, Paolo; Maggiolo-Schettini, Andrea: An algorithm for the identification of components in biochemical pathways (2013)
  12. Andrei, Oana; Calder, Muffy: Trend-based analysis of a population model of the AKAP scaffold protein (2012)
  13. De Maria, Elisabetta; Fages, François; Rizk, Aurélien; Soliman, Sylvain: Design, optimization and predictions of a coupled model of the cell cycle, circadian clock, DNA repair system, irinotecan metabolism and exposure control under temporal logic constraints (2011)
  14. Jha, Sumit Kumar; Langmead, Christopher James: Synthesis and infeasibility analysis for stochastic models of biochemical systems using statistical model checking and abstraction refinement (2011)
  15. Andrei, Oana; Calder, Muffy: A model and analysis of the AKAP scaffold (2010)
  16. Ballarini, Paolo; Guerriero, Maria Luisa: Query-based verification of qualitative trends and oscillations in biochemical systems (2010)
  17. Bortolussi, Luca; Policriti, Alberto: Hybrid dynamics of stochastic programs (2010)
  18. Degasperi, Andrea; Calder, Muffy: Process algebra with hooks for models of pattern formation (2010)
  19. Degasperi, Andrea; Calder, Muffy: Relating PDEs in cylindrical coordinates and CTMCs with levels of concentration (2010)
  20. Drábik, Peter; Maggiolo-Schettini, Andrea; Milazzo, Paolo: Modular verification of interactive systems with an application to biology (2010)

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