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Here we review the current and potential uses of RGNs to investigate genome function during development.
Through the regulated process of development, a single cell divides and differentiates into the multitude of specialized cells that compose a mature organism.
The dilemma that we face is that any entropy used prematurely will be lost, and any entropy which is kept unused will delay the recovery.
After developing our formal definitional framework for RNGs with inputs, we show how to construct a nearly optimal RNG which is secure in our model.
Our technique is inspired by the design of the Fortuna RNG (which is a heuristic RNG construction that is currently used by Windows and comes without any formal analysis), but we non-trivially adapt it to our much stronger adversarial setting.
Along the way, our formal treatment of Fortuna enables us to improve its entropy efficiency by almost a factor of two, and to show that our improved construction is essentially tight, by proving a rigorous lower bound on the possible efficiency of any recovery mechanism in our very general model of the problem.
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