Coupled networks can be used to represent a wide-range of problems including bio-molecular processes in cells. One of the major goals of network analyses is to design methods to control such systems to pre-specified target states. For example, we may inquire if consequences of genetic mutations or chromosomal rearrangements can be mitigated through external intervention. Unfortunately, modeling such networks is virtually impossible because precise quantitative forms of interactions between biomolecules are currently unavailable, and are unlikely to be available in the near future.
The model-independent approach proposed here relies on representing the “state” of a cell through its gene-expression profile; i.e., the levels of mRNA within a cell. The data can be extracted using techniques such as RNASeq. Under this assumption, key ingredients needed for control are “response surfaces,” each of which expresses how the gene expression profile responds to a specific external perturbation. The number of control nodes, i.e., nodes whose levels are to be externally controlled, can be systematically increased in order to reach the target state. Importantly, the most appropriate control node to be added at a given level is determined computationally from prior data.
Interestingly, analyses of synthetic models and (experiments on) nonlinear electrical circuits show that the target state can be typically reached with a few (3 or 4) control genes. We propose experimental validations of the approach in the sleep-deprivation network and in an addiction network in Drosophila.