The knowledge contained in such a map, whatever approach is used to obtain it, will mainly be used by the robot to gain the ability to navigate in a given environment.
In such a map, diseases should be staged by defining MLDMs at different time points.
In such a map, each goal location would have unique 3-component spatial coordinates (x, y and z), and/or each arm of the maze would be represented by a unique vector; animals could thus solve the task by visiting each of the relevant locations in the same way as they do on the standard radial maze.
While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names.
It certainly would have been nearly impossible to identify the NADP molecule in such a poor map, and the heme group would have been a stretch, even knowing the identity of the ligand.
Our interest in this approach lies not simply in describing this mapping, but rather in using such a mapping for developing tools for evaluation of therapeutic targets and designing personalized therapies.
In practice, such a mapping may be difficult to realize numerically and hence a class of estimators is proposed which ensures that the state estimate converges asymptotically to the real state of the system.
In principal, such a mapping is always connected with a lot of uncertainy: The detailed Kuhn length is not known, disallowing for a precise mapping on the short scale; as chromatin is organized much more complex than a linear chain, other parameters (looping, binding, heterogeneity) enter the calculations.
In such a case, the mapping potential can be written as a linear product of the two potentials: (7) ε m = 1 − λ m E REF + λ m E TARGET 0 ≤ λ m ≤ 1 where λ m is changed in fixed increments (λ m = 0/ m, 1/ m, …, m/ m) from 0 to 1.
