There is provided a method comprising: receiving a graph comprising nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights when the mismatch is within the tolerance requirement.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: receiving a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing a plurality of desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement. 2. The method of claim 1, wherein randomly adjusting comprises randomly adjusting the variable weights of the directional edges within the respective range according to a function proportional to the amount of mismatch. 3. The method of claim 1, wherein an iterative reduction in the amount of mismatch reduces the amount of the random adjustment within the respective range. 4. The method of claim 1, wherein the network represents a chemical reaction, wherein each node represents a reactant, or an intermediate product, wherein each directed edge represents a reaction condition, wherein the desired global value represents a desired product, wherein the tolerance requirement represents a tolerance of the desired product. 5. The method of claim 1, wherein the network represents a gene regulatory network, wherein each directed edge represents the effect of one gene product on another gene through various mechanisms of regulation, wherein the desired global value represents a phenotype, the mismatch from the desired global value represents a global cellular stress signal, wherein the tolerance requirement represents a range of phenotypes compatible with a constraining demand. 6. The method of claim 1, wherein an initial set of the variable weights assigned to the directional edges are randomly determined within the respective range. 7. The method of claim 1, further comprising receiving an initial topological structure of the network, and converting the initial topological structure to an adapted topological structure of the graph including at least a scale-free out-degree distribution, wherein the initial topological structure and the adapted topological structure are statistically similar according to a graph similarity requirement. 8. The method of claim 7, wherein the in-degree distribution of the adapted topological structure of the graph is a member of the group consisting of: scale-free distribution, exponential distribution, and binomial distribution. 9. The method of claim 7, wherein converting comprises performing a graph transformation. 10. The method of claim 1, further comprising analyzing the topological structure of the graph representation of the network, and applying the acts of the computer implemented method when the topological structure is identified at least as including a scale-free out-degree distribution. 11. The method of claim 1, further comprising receiving an initial graph state of the network, analyzing the graph to identify a set of largest nodes with the largest number of edges, and transforming the initial graph state of the network to an adapted graph state of the network having an adapted set of the largest nodes with a reduced number of the largest number of edges. 12. The method of claim 1, wherein the number of nodes is at least 1000. 13. The method of claim 1, wherein the current global value is calculated by a linear or non-linear combination of the variable weights of the directional edges. 14. The method of claim 1, wherein the current global value is calculated as a linear or non-linear combination of coordinates corresponding to the variable weight parameters. 15. The method of claim 1, wherein the graph represents a dynamical system represented by at least one function that describes time dependence motion of at least one point in a geometrical space. 16. The method of claim 1, wherein the mismatch is calculated by a step function outside the tolerance requirement and a value of zero within the tolerance requirement. 17. The method of claim 1, wherein the determined values are outputted when at least one combination of variable weight parameters that give rise to at least one set of coordinates or which the global value falls stably within the tolerance requirement of the desired global value. 18. A system for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: a program store storing code; and a processor coupled to the program store for implementing the stored code, the code comprising: code to receive a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; code to calculate a current global value defined by a function of the variable weights of the directional edges, calculate a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement, and determine when the mismatch is within a tolerance requirement representing a plurality of desired global values; and one of: randomly adjust the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and output determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement. 19. A computer program product comprising a non-transitory computer readable storage medium storing program code thereon for implementation by a processor of a system for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: instructions to receive a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; instructions to calculate a current global value defined by a function of the variable weights of the directional edges; instructions to calculate a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; instructions to determine when the mismatch is within a tolerance requirement representing a plurality of desired global values; and instructions to perform one of: randomly adjust the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and output determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement.