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DNA-PAINT is a powerful and flexible implementation of Stochastic Reconstruction Microscopy (STORM), a super resolution technique that enables researchers to produce images with subresolution accuracy1,2. In its most rudimentary implementation, this imaging system requires two DNA strands: a fluorophore containing imager strand and a docking strand which is anchored to a substrate of interest and is complimentary to the imager strand. The strands are designed in such a manner that they spontaneously hybridize and dehybridize. In the seminal DNA-PAINT publication, it was demonstrated that the rate of detected localizations is directly related to the concentration of the imager strand and independent of the length of the hybridization3. These rates of localizations in turn determine the ‘on-time’ of a localization which is an important parameter to control in order to avoid overlaps. Currently, Picasso is the primary DNA-PAINT simulator that allows one to input custom kinetic parameters such as kon and dark time2. While important parameters to be sure, we hypothesize that these parameters can be computed from the sequences that are to be used as the imager and the docking strands when the problem is articulated in a statistical mechanical framework: What is the probability of observing the micro-state in which the imager and docking strands are hybridized? The Boltzmann distribution is a powerful tool when computing macroscale thermodynamic parameters of chemical systems from its molecular components. Certain formulations of the distribution use three parameters: the number of lattice sites and ligands denoted as Ω and L respectively, and the free energy of a microstate ΔG4. The ΔG of hybridization can be computed using the NUPACK software, while Ω and L can be set by the user5–7. In systems such as DNA-PAINT, Ω >> L as the concentration of the imager strand is dilute. The Boltzmann distribution parameterized by these three parameters can output a probability that in turn parameterizes a Monte Carlo model that simulates an observed localization of the imager strand. Our initial simulations using this sequence informed framework demonstrate that the frequency of localizations and consecutive localizations, indicated by a broad peak in the time-intensity trace diagram, is directly proportional to L when the sequences are complimentary to one another. This is consistent with expected experimental results as STORM necessitates a trace amount of the fluorescent molecule to promote sparse localizations to prevent overlap of adjacent signals. 

ABSTRACT Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral microbiome is essential to the health of coral reefs and sensitive to environmental changes. Here, we develop a dynamic model to determine the microbial community structure associated with the surface mucus layer (SML) of corals using temperature as an extrinsic factor and microbial network as an intrinsic factor. The model was validated by comparing the predicted relative abundances of microbial taxa to the relative abundances of microbial taxa from the sample data. The SML microbiome from Pseudodiploria strigosa was collected across reef zones in Bermuda, where inner and outer reefs are exposed to distinct thermal profiles. A shotgun metagenomics approach was used to describe the taxonomic composition and the microbial network of the coral SML microbiome. By simulating the annual temperature fluctuations at each reef zone, the model output is statistically identical to the observed data. The model was further applied to six scenarios that combined different profiles of temperature and microbial network to investigate the influence of each of these two factors on the model accuracy. The SML microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of the microbial network profile. Our model shows that the SML microbiome of P. strigosa in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver. IMPORTANCE Coral microbiome dysbiosis (i.e., shifts in the microbial community structure or complete loss of microbial symbionts) caused by environmental changes is a key player in the decline of coral health worldwide. Multiple factors in the water column and the surrounding biological community influence the dynamics of the coral microbiome. However, by including only temperature as an external factor, our model proved to be successful in describing the microbial community associated with the surface mucus layer (SML) of the coral P. strigosa . The dynamic model developed and validated in this study is a potential tool to predict the coral microbiome under different temperature conditions.