Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.

The spatial association between fluorescently tagged biomolecules in situ provides valuable insight into their biological relationship. Within the limits of diffraction, such association can be measured using either Pearson's Correlation Coefficient (PCC) or Spearman's Rank Coefficient (SRC), which are designed to measure linear and monotonic correlations, respectively. However, the relationship between real biological signals is often more complex than these measures assume, rendering their results difficult to interpret. Here, we have adapted methods from the field of information theory to measure the association between two probes' concentrations based on their statistical dependence. Our approach is mathematically more general than PCC or SRC, making no assumptions about the type of relationship between the probes. We show that when applied to biological images, our measures provide more intuitive results that are also more robust to outliers and the presence of multiple relationships than PCC or SRC. We also devise a display technique to highlight regions in the input images where the probes' association is higher versus lower. We expect that our methods will allow biologists to more accurately and robustly quantify and visualize the association between two probes in a pair of fluorescence images. © 2018 International Society for Advancement of Cytometry.

Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales-from biochemical networks within individual cells to spatially structured cellular populations. Here we present a family of "multiscale" models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cyclic adenosine monophosphate (cAMP) used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various preprocessing modules. We use this family of models to study spatially unstructured populations of "fixed" cells by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then briefly discuss an extension of our model that includes spatial structure and show how this naturally gives rise to spiral waves. Our models exhibit a wide range of novel phenomena. including a density-dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

Transcriptional regulation is a complex process that requires cooperation between specific DNA sequence elements, the DNA-binding proteins that bind to these sequences, the general transcriptional machinery, and chromatin. Oocyte microinjection offers a technique to study the integrated transcription process while still providing the opportunity to experimentally perturb this process. We describe here techniques for manipulating DNA templates and the protein complement of the oocyte to study multiple facets of transcriptional regulation. We present sample results showing that the GAL4-VP16 fusion activator is sensitive to distance in constructs containing only a minimal promoter, but can activate transcription at greater distances when proximal promoter elements are present.

The weak pixel counts surrounding the Bragg spots in a diffraction image are important for establishing a model of the background underneath the peak and estimating the reliability of the integrated intensities. Under certain circumstances, particularly with equipment not optimized for low-intensity measurements, these pixel values may be corrupted by corrections applied to the raw image. This can lead to truncation of low pixel counts, resulting in anomalies in the integrated Bragg intensities, such as systematically higher signal-to-noise ratios. A correction for this effect can be approximated by a three-parameter lognormal distribution fitted to the weakly positive-valued pixels at similar scattering angles. The procedure is validated by the improved refinement of an atomic model against structure factor amplitudes derived from corrected micro-electron diffraction (MicroED) images.

Protein design is a useful strategy to interrogate the protein structure‐function relationship. We demonstrate using a highly modular 3‐stranded coiled coil (TRI‐peptide system) that a functional type 2 copper center exhibiting copper nitrite reductase (NiR) activity exhibits the highest homogeneous catalytic efficiency under aqueous conditions for the reduction of nitrite to NO and H2O. Modification of the amino acids in the second coordination sphere of the copper center increases the nitrite reductase activity up to 75‐fold compared to previously reported systems. We find also that steric bulk can be used to enforce a three‐coordinate CuI in a site, which tends toward two‐coordination with decreased steric bulk. This study demonstrates the importance of the second coordination sphere environment both for controlling metal‐center ligation and enhancing the catalytic efficiency of metalloenzymes and their analogues.

Modular synthesis and substrate stereocontrol were combined to furnish 18,000 diverse 1,3-dioxanes whose distribution in chemical space rivals that of a reference set of over 2,000 bioactive small molecules. Library quality was assessed at key synthetic stages, culminating in a detailed postsynthesis analysis of purity, yield, and structural characterizability, and the resynthesis of library subsets that did not meet quality standards. The importance of this analysis-resynthesis process is highlighted by the discovery of new biological probes through organismal and protein binding assays, and by determination of the building block and stereochemical basis for their bioactivity. This evaluation of a portion of the 1,3-dioxane library suggests that many additional probes for chemical genetics will be identified as the entire library becomes biologically annotated.