Yuishi Iwasaki

Evaluation of head movement periodicity and irregularity during locomotion of Caenorhabditis elegans

Caenorhabditis elegans is suitable for studying the nervous system, which controls behavior. C. elegans shows sinusoidal locomotion on an agar plate. The head moves not only sinusoidally but also more complexly, which reflects regulation of the head muscles by the nervous system. The head movement becomes more irregular with senescence. To date, the head movement complexity has not been quantitatively analyzed. We propose two simple methods for evaluation of the head movement regularity on an agar plate using image analysis. The methods calculate metrics that are a measure of how the head end movement is correlated with body movement. In the first method, the length along the trace of the head end on the agar plate between adjacent intersecting points of the head trace and the quasi-midline of the head trace, which was made by sliding an averaging window of 1/2 the body wavelength, was obtained. Histograms of the lengths showed periodic movement of the head and deviation from it. In the second method, the intersections between the trace of the head end and the trace of the 5 (near the pharynx) or 50% (the mid-body) point from the head end in the centerline length of the worm image were marked. The length of the head trace between adjacent intersections was measured, and a histogram of the lengths was produced. The histogram for the 5% point showed deviation of the head end movement from the movement near the pharynx. The histogram for the 50% point showed deviation of the head movement from the sinusoidal movement of the body center. Application of these methods to wild type and several mutant strains enabled evaluation of their head movement periodicity and irregularity, and revealed a difference in the age-dependence of head movement irregularity between the strains. A set of five parameters obtained from the histograms reliably identifies differences in head movement between strains.

Calcium dynamics regulating the timing of decision-making in C. elegans

Brains regulate behavioral responses with distinct timings. Here we investigate the cellular and molecular mechanisms underlying the timing of decision-making during olfactory navigation in Caenorhabditis elegans. We find that, based on subtle changes in odor concentrations, the animals appear to choose the appropriate migratory direction from multiple trials as a form of behavioral decision-making. Through optophysiological, mathematical and genetic analyses of neural activity under virtual odor gradients, we further find that odor concentration information is temporally integrated for a decision by a gradual increase in intracellular calcium concentration ([Ca2+]i), which occurs via L-type voltage-gated calcium channels in a pair of olfactory neurons. In contrast, for a reflex-like behavioral response, [Ca2+]i rapidly increases via multiple types of calcium channels in a pair of nociceptive neurons. Thus, the timing of neuronal responses is determined by cell type-dependent involvement of calcium channels, which may serve as a cellular basis for decision-making. DOI: http://dx.doi.org/10.7554/eLife.21629.001

Quantitative modeling of neuronal dynamics in C. elegans

We present a mathematical model to quantitatively describe the neuronal dynamics in Caenorhabditis elegans. Since calcium imaging is a popular technique to visualize the neuronal activity in C. elegans, the model includes the variable of the fluorescence intensity in addition to the membrane potential and the intracellular calcium concentration. The fluorescence intensity is a quantity which is comparable with the experimental data. The parameters in the model are determined to reproduce the neurophysiological experimental data. Our model exhibits good agreement with the data. We apply the model to a neural circuit for chemotaxis and find that the neuronal activity measured by the fluorescence intensity shows quantitatively different behavior from that measured by the membrane potential in some neurons. The difference is discussed from the viewpoint of neuronal mechanisms.

A simple model of asymmetric quasi-species: network sustained by emergent high mutation rates

Abstract A simple model of adaptive mutation rates is studied to understand asymmetric adaptive systems. In the model, two sources of asymmetries are introduced. One is a spin-glass-type energy function and gives an asymmetry of the fitness landscape. The other is the variable mutation rate associated with each gene and gives an asymmetry of the transition probabilities. A control parameter is a selection pressure rather than a mutation rate. We find that the model shows three results: (i) High mutation rates emerge in the iterative Darwinian selection process. (ii) Detailed balance is satisfied in the diverse system sustained by the emergent high mutation rates. (iii) A transition from the positive Darwinian selection (ordered state) to the nearly neutral selection (disordered state) takes place as the selection pressure decreases. Based on these results, we study the asymmetric network sustained by emergent high mutation rates.

An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging

Shifting from individual neuron analysis to whole-brain neural network analysis opens up new research opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worm’s head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions.

Numerical Study on Error Catastrophe of Adaptive Mutants and Their Stability

We study an error catastrophe of a self-replicating system, in which a number of bit strings with variable mutation rate compete to survive in a given rugged fitness landscape. The error catastrophe is known as a phase transition from an ordered state to a disordered state when a mutation rate increases. As a control parameter, we choose a selection pressure rather than a mutation rate. We find that our system shows the error catastrophe when the selection pressure decreases. An order parameter is introduced and the error catastrophe is studied for three types of mutant; (a) each bit has a fixed mutation rate, (b) each bit has a common variable mutation rate, and (c) each bit has a variable mutation rate. We also analyze stability of the system on the basis of the eigenvalue and the eigenvector of the transition matrix.