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Computational prediction of RNA structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA structure formation, taking into account also the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of metastable conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.

Barcoded DNA polony amplification techniques provide a means to impart a unique sequence identity onto specific locations of a surface wafer or chip. We describe a method whereby micro-scale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without the use of conventional optics. The principle is based on the pair-wise association of uniquely tagged and spatially adjacenct polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from a set of edges derived from pairs of barcodes associated with each other during a polony crosslinking phase, the sequences of which could be determined by isolation of the DNA and next-gen sequencing. We developed a mathematical and computational framework for this principle called Polony Adjacency Reconstruction for Spatial Inference and Topology and show that Euclidean spatial data may be partially stored and transmitted in the form of untethered graph topology. This effect may be exploited to form images by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical red, green, and blue molecular markers. The theory developed here could serve as a guide for a highly automated, multiplexable, and potentially super resolution imaging method based on molecular information encoding and transmission.