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The mutations of a complex systemic disease like cancer can be modeled asstuck-at faults in the Boolean system paradigm. For a class of multiple faults,the fault identification is exceptionally significant under the incompleteaccess of all the underlying proteins of the system. A comprehensive linearframework has been developed in this manuscript to identify the class of faultsunder a set of homeostatic input conditions. An algorithm is developed todesign new reporters to improve the observability. The other aspect of thismanuscript lies in controlling the manifestation of the mutations, which is theessential objective of systems medicine research. The primary goal is tosynthesize a cocktail of drug molecules (combination therapy) from a set ofexisting targeted drugs. The controllability results are included in this paperto understand the problem formally. An improvement of controllability algorithmis discussed to design new target drugs if the available drugs fail toaccommodate the underlying fault set. The results are presented for Booleanmaps and Boolean control networks. Biological examples are given to highlightthe relevant results.

A Linear Approach to Fault Analysis and Intervention in Boolean Systems