In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy videos. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. Then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells’ appearance and serious overlapping and occlusion. For multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. The stability of the algorithm with different parameter values is also analyzed and discussed.

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ’bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

The antennal lobe (AL) is the primary structure in the Drosophila brain that relays odor information from the antennae to higher brain centers. The characterization of uniglomerular projection neurons (PNs) and some local interneurons has facilitated our understanding of olfaction; however, many other AL neurons remain unidentified. Because neuron types are mostly specified by lineage and temporal origins, we use the MARCM techniques with a set of enhancer-trap GAL4 lines to perform systematical lineage analysis to characterize neuron morphologies, lineage origin and birth timing in the three AL neuron lineages that contain GAL4-GH146-positive PNs: anterodorsal, lateral and ventral lineages. The results show that the anterodorsal lineage is composed of pure uniglomerular PNs that project through the inner antennocerebral tract. The ventral lineage produces uniglomerular and multiglomerular PNs that project through the middle antennocerebral tract. The lateral lineage generates multiple types of neurons, including uniglomeurlar PNs, diverse atypical PNs, various types of AL local interneurons and the neurons that make no connection within the ALs. Specific neuron types in all three lineages are produced in specific time windows, although multiple neuron types in the lateral lineage are made simultaneously. These systematic cell lineage analyses have not only filled gaps in the olfactory map, but have also exemplified additional strategies used in the brain to increase neuronal diversity.

A long-standing conjecture in neuroscience is that aspects of cognition depend on the brain’s ability to self-generate sequential neuronal activity. We found that reliably and continually changing cell assemblies in the rat hippocampus appeared not only during spatial navigation but also in the absence of changing environmental or body-derived inputs. During the delay period of a memory task, each moment in time was characterized by the activity of a particular assembly of neurons. Identical initial conditions triggered a similar assembly sequence, whereas different conditions gave rise to different sequences, thereby predicting behavioral choices, including errors. Such sequences were not formed in control (nonmemory) tasks. We hypothesize that neuronal representations, evolved for encoding distance in spatial navigation, also support episodic recall and the planning of action sequences.

The Honeybee Brain Atlas serves as 3D database and communicative platform to accumulate structural data, i.e. reconstructed neurons, derived from confocal scans (Brandt et al., 2005) (www.neurobiologie.fu-berlin.de/beebrain/) (1). Transforming neurons into the atlas requires manual segmentation of neuropils within confocal images, a time-consuming task requiring expertise in identifying biological structures which can result in different outcomes from various segmenters.

Aquaporins (AQPs) are a family of ubiquitous membrane channels that conduct water across cell membranes. AQPs form homotetramers containing four functional and independent water pores. Aquaporin-0 (AQP0) is expressed in the eye lens, where its water permeability is regulated by calmodulin (CaM). Here we use a combination of biochemical methods and NMR spectroscopy to probe the interaction between AQP0 and CaM. We show that CaM binds the AQP0 C-terminal domain in a calcium-dependent manner. We demonstrate that only two CaM molecules bind a single AQP0 tetramer in a noncanonical fashion, suggesting a form of cooperativity between AQP0 monomers. Based on these results, we derive a structural model of the AQP0/CaM complex, which suggests CaM may be inhibitory to channel permeability by capping the vestibules of two monomers within the AQP0 tetramer. Finally, phosphorylation within AQP0's CaM binding domain inhibits the AQP0/CaM interaction, suggesting a temporal regulatory mechanism for complex formation.

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

This paper formulates shape registration as an optimal coding problem. It employs a set of landmarks to establish the correspondence between shapes, and assumes that the best correspondence can be achieved when the polygons formed by the landmarks optimally code all the shape contours, i.e., obtain their minimum description length (MDL). This is different from previous MDL-based shape registration methods, which code the landmark locations. In this paper, each contour is discretized to be a set of points to make the coding feasible, and a number of strategies are adopted to tackle the difficult optimization problem involved. The resulting algorithm, called CAP, is able to yield statistical shape model with better quality in terms of model generalization error, which is demonstrated on both synthetic and biomedical shapes.

The RNA polymerase II core promoter is a structurally and functionally diverse transcriptional module. RNAi depletion and overexpression experiments revealed a genetic circuit that controls the balance of transcription from two core promoter motifs, the TATA box and the downstream core promoter element (DPE). In this circuit, TBP activates TATA-dependent transcription and represses DPE-dependent transcription, whereas Mot1 and NC2 block TBP function and thus repress TATA-dependent transcription and activate DPE-dependent transcription. This regulatory circuit is likely to be one means by which biological networks can transmit transcriptional signals, such as those from DPE-specific and TATA-specific enhancers, via distinct pathways.

Is genetic evolution predictable? Evolutionary developmental biologists have argued that, at least for morphological traits, the answer is a resounding yes. Most mutations causing morphological variation are expected to reside in the cis-regulatory, rather than the coding, regions of developmental genes. This "cis-regulatory hypothesis" has recently come under attack. In this review, we first describe and critique the arguments that have been proposed in support of the cis-regulatory hypothesis. We then test the empirical support for the cis-regulatory hypothesis with a comprehensive survey of mutations responsible for phenotypic evolution in multicellular organisms. Cis-regulatory mutations currently represent approximately 22% of 331 identified genetic changes although the number of cis-regulatory changes published annually is rapidly increasing. Above the species level, cis-regulatory mutations altering morphology are more common than coding changes. Also, above the species level cis-regulatory mutations predominate for genes not involved in terminal differentiation. These patterns imply that the simple question "Do coding or cis-regulatory mutations cause more phenotypic evolution?" hides more interesting phenomena. Evolution in different kinds of populations and over different durations may result in selection of different kinds of mutations. Predicting the genetic basis of evolution requires a comprehensive synthesis of molecular developmental biology and population genetics.