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 45 articles )

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  1. Allart, Emilie; Niehren, Joachim; Versari, Cristian: Computing difference abstractions of linear equation systems (2021)
  2. Troják, Matej; Šafránek, David; Brim, Luboš; Šalagovič, Jakub; Červený, Jan: Executable biochemical space for specification and analysis of biochemical systems (2020)
  3. Wright, Thomas; Stark, Ian: Modelling patterns of gene regulation in the bond-calculus (2020)
  4. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2019)
  5. Álvarez-Buylla Roces, María Elena; Martínez-García, Juan Carlos; Dávila-Velderrain, José; Domínguez-Hüttinger, Elisa; Martínez-Sánchez, Mariana Esther: Modeling methods for medical systems biology. Regulatory dynamics underlying the emergence of disease processes (2018)
  6. Baudier, Adrien; Fages, François; Soliman, Sylvain: Graphical requirements for multistationarity in reaction networks and their verification in BioModels (2018)
  7. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2016)
  8. Fages, François; Martinez, Thierry; Rosenblueth, David A.; Soliman, Sylvain: Influence systems vs reaction systems (2016)
  9. Ballarini, Paolo; Duflot, Marie: Applications of an expressive statistical model checking approach to the analysis of genetic circuits (2015)
  10. Bartocci, Ezio; Bortolussi, Luca; Nenzi, Laura; Sanguinetti, Guido: System design of stochastic models using robustness of temporal properties (2015)
  11. Chaves, Madalena; Carta, Alfonso: Attractor computation using interconnected Boolean networks: testing growth rate models in \textitE. coli (2015)
  12. Chiarugi, Davide; Falaschi, Moreno; Olarte, Carlos; Palamidessi, Catuscia: A declarative view of signaling pathways (2015)
  13. Fages, François; Gay, Steven; Soliman, Sylvain: Inferring reaction systems from ordinary differential equations (2015)
  14. Ito, Sohei; Ichinose, Takuma; Shimakawa, Masaya; Izumi, Naoko; Hagihara, Shigeki; Yonezaki, Naoki: Qualitative analysis of gene regulatory networks by temporal logic (2015)
  15. Pardini, Giovanni; Milazzo, Paolo; Maggiolo-Schettini, Andrea: Component identification in biochemical pathways (2015)
  16. Amar, Patrick; Paulevé, Loïc: HSIM: a hybrid stochastic simulation system for systems biology (2014)
  17. Brim, Luboš; Češka, Milan; Šafránek, David: Model checking of biological systems (2013)
  18. Chiarugi, Davide; Falaschi, Moreno; Hermith, Diana; Guzman, Michell; Olarte, Carlos: Simulating signalling pathways with bioways (2013) ioport
  19. Degasperi, A.; Calder, M.: A process algebra framework for multi-scale modelling of biological systems (2013)
  20. Pardini, Giovanni; Milazzo, Paolo; Maggiolo-Schettini, Andrea: An algorithm for the identification of components in biochemical pathways (2013)

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