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  "Package": "ReDirection",
  "Type": "Package",
  "Title": "Predict Dominant Direction of Reactions of a Biochemical Network",
  "Version": "1.0.1",
  "Author": "Siddhartha Kundu <2021: Manuscript Under Preparation>",
  "Maintainer": "Siddhartha Kundu <siddhartha_kundu@aiims.edu>",
  "Description": "Biologically relevant, yet mathematically sound\nconstraints are used to compute the propensity and thence infer\nthe dominant direction of reactions of a generic biochemical\nnetwork. The reactions must be unique and their number must\nexceed that of the reactants,i.e., reactions >= reactants + 2.\n'ReDirection', computes the null space of a user-defined\nstoichiometry matrix. The spanning non-zero and unique reaction\nvectors (RVs) are combinatorially summed to generate one or\nmore subspaces recursively. Every reaction is represented as a\nsequence of identical components across all RVs of a particular\nsubspace. The terms are evaluated with (biologically relevant\nbounds, linear maps, tests of convergence, descriptive\nstatistics, vector norms) and the terms are classified into\nforward-, reverse- and equivalent-subsets. Since, these are\nmutually exclusive the probability of occurrence is binary\n(all, 1; none, 0). The combined propensity of a reaction is the\np1-norm of the sub-propensities, i.e., sum of the products of\nthe probability and maximum numeric value of a subset (least\nupper bound, greatest lower bound). This, if strictly positive\nis the probable rate constant, is used to infer dominant\ndirection and annotate a reaction as \"Forward (f)\", \"Reverse\n(b)\" or \"Equivalent (e)\". The inherent computational complexity\n(NP-hard) per iteration suggests that a suitable value for the\nnumber of reactions is around 20. Three functions comprise\nReDirection. These are check_matrix() and reaction_vector()\nwhich are internal, and calculate_reaction_vector() which is\nexternal.",
  "License": "GPL-3",
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  "Date/Publication": "2021-10-04 07:30:09 UTC",
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