In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

We describe a general computational method for designing proteins that self-assemble to a desired symmetric architecture. Protein building blocks are docked together symmetrically to identify complementary packing arrangements, and low-energy protein-protein interfaces are then designed between the building blocks in order to drive self-assembly. We used trimeric protein building blocks to design a 24-subunit, 13-nm diameter complex with octahedral symmetry and a 12-subunit, 11-nm diameter complex with tetrahedral symmetry. The designed proteins assembled to the desired oligomeric states in solution, and the crystal structures of the complexes revealed that the resulting materials closely match the design models. The method can be used to design a wide variety of self-assembling protein nanomaterials.

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.

Imaging large samples at the resolution offered by electron microscopy is typically achieved by sequentially recording overlapping tiles that are later combined to seamless mosaics. Mosaics of serial sections are aligned to reconstruct three-dimensional volumes. To achieve this, image distortions and artifacts as introduced during sample preparation or imaging need to be removed.

In this chapter, we will discuss typical sources of artifacts and distortion, and we will learn how to use the open source software TrakEM2 to correct them.

The endosomal sorting complex required for transport (ESCRT)-III complex, capable of polymerization and remodeling, participates in abscission of the intercellular membrane bridge connecting two daughter cells at the end of cytokinesis. Here, we integrate quantitative imaging of ESCRT-III during cytokinetic abscission with biophysical properties of ESCRT-III complexes to formulate and test a computational model for ESCRT-mediated cytokinetic abscission. We propose that cytokinetic abscission is driven by an ESCRT-III fission complex, which arises from ESCRT-III polymerization at the edge of the cytokinetic midbody structure, located at the center of the intercellular bridge. Formation of the fission complex is completed by remodeling and breakage of the ESCRT-III polymer assisted by VPS4. Subsequent spontaneous constriction of the fission complex generates bending deformation of the intercellular bridge membrane. The related membrane elastic force propels the fission complex along the intercellular bridge away from the midbody until it reaches an equilibrium position, determining the scission site. Membrane attachment to the dome-like end-cap of the fission complex drives membrane fission, completing the abscission process. We substantiate the model by theoretical analysis of the membrane elastic energy and by experimental verification of the major model assumptions.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. We used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. First, activation of CA1 neurons by the perforant path is limited, a result of the long distance between these inputs and the soma. Second, activation of CA1 neurons by the perforant path depends on the generation of dendritic spikes. Third, the forward propagation of these spikes is unreliable, but can be facilitated by modest activation of Schaffer-collateral synapses in the upper apical dendrites. This 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus.

The homeodomain-containing transcription factor NKX3.1 is a putative prostate tumor suppressor that is expressed in a largely prostate-specific and androgen-regulated manner. Loss of NKX3.1 protein expression is common in human prostate carcinomas and prostatic intraepithelial neoplasia (PIN) lesions and correlates with tumor progression. Disruption of the murine Nkx3.1 gene results in defects in prostate branching morphogenesis, secretions, and growth. To more closely mimic the pattern of NKX3.1 loss that occurs in human prostate tumors, we have used Cre- and loxP-mediated recombination to delete the Nkx3.1 gene in the prostates of adult transgenic mice. Conditional deletion of one or both alleles of Nkx3.1 leads to the development of preinvasive lesions that resemble PIN. The pattern of expression of several biomarkers (Ki-67, E-cadherin, and high-molecular-weight cytokeratins) in these PIN lesions resembled that observed in human cases of PIN. Furthermore, PIN foci in mice with conditional deletion of a single Nkx3.1 allele lose expression of the wild-type allele. Our results support the role of NKX3.1 as a prostate tumor suppressor and indicate a role for this gene in tumor initiation.