The power of science lies in its ability to infer and predict the

existence of objects from which no direct information can be obtained

experimentally or observationally. A well known example is to

ascertain the existence of black holes of various masses in different

parts of the universe from indirect evidence, such as X-ray emissions.

In the field of complex networks, the problem of detecting

hidden nodes can be stated, as follows. Consider a network whose

topology is completely unknown but whose nodes consist of two types:

one accessible and another inaccessible from the outside world. The

accessible nodes can be observed or monitored, and it is assumed that time

series are available from each node in this group. The inaccessible

nodes are shielded from the outside and they are essentially

``hidden.'' The question is, based solely on the

available time series from the accessible nodes, can the existence and

locations of the hidden nodes be inferred? A completely data-driven,

compressive-sensing based method is developed to address this issue by utilizing

complex weighted networks of nonlinear oscillators, evolutionary game

and geospatial networks.

Both microbes and multicellular organisms actively regulate their cell

fate determination to cope with changing environments or to ensure

proper development. Here, the synthetic biology approaches are used to

engineer bistable gene networks to demonstrate that stochastic and

permanent cell fate determination can be achieved through initializing

gene regulatory networks (GRNs) at the boundary between dynamic

attractors. This is experimentally realized by linking a synthetic GRN

to a natural output of galactose metabolism regulation in yeast.

Combining mathematical modeling and flow cytometry, the

engineered systems are shown to be bistable and that inherent gene expression

stochasticity does not induce spontaneous state transitioning at

steady state. By interfacing rationally designed synthetic

GRNs with background gene regulation mechanisms, this work

investigates intricate properties of networks that illuminate possible

regulatory mechanisms for cell differentiation and development that

can be initiated from points of instability.