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Chemical reaction networks (CRNs) are widely used tools in modeling biochemical systems such as intracellular processes, metabolic networks or cell signalling pathways. Being able to produce all the biologically and chemically important qualitative dynamical features, CRNs have attracted significant attention in the systems biology community. It is well-known that the reliable inference of CRN models  generally requires thorough identifiability and distinguishability analysis together with carefully selected prior modeling assumptions. Here we present a software toolbox CRNreals that supports the distinguishability analysis of CRN models using recently published optimization-based procedures.

Main features
  • Algorithmically building the so-called canonical CRN mechanism from kinetic polynomial ordinary differential equations. 
  • Finding dense and sparse realizations containing the maximal and minimal nonzero reaction rate coefficients. (We note that dense realizations give a unique super-structure with a fixed complex set, and that the CRN structure is unique if and only if the structures of the dense and sparse realizations are identical.) 
  • Finding reversible and weakly reversible realizations. The existence of such realizations has a key effect on the boundedness of solutions and on the robust stability of the system (depending also on the deficiency).
  • Finding detailed balanced and complex balanced realizations if they exist. These properties have also important stability implications.
  • Computing dynamically equivalent realizations with the minimal and maximal number of chemical complexes from a previously defined complex set.
  • Finding the so-called `core' and `non-core' reactions of the CRN. Core reactions are present in any dynamically equivalent CRN realization and thus they are indispensable components of the system, while non-core reactions can (at least mathematically) be substituted by others.