Tomáš Gedeon

Encapsulation of an Enzyme Cascade within the Bacteriophage P22 Virus-Like Particle

Developing methods for investigating coupled enzyme systems under conditions that mimic the cellular environment remains a significant challenge. Here we describe a biomimetic approach for constructing densely packed and confined multienzyme systems through the co-encapsulation of 2 and 3 enzymes within a virus-like particle (VLP) that perform a coupled cascade of reactions, creating a synthetic metabolon. Enzymes are efficiently encapsulated in vivo with known stoichiometries, and the kinetic parameters of the individual and coupled activities are characterized. From the results we develop and validate a mathematical model for predicting the expected kinetics for coupled reactions under co-localized conditions.

Dejittered Spike-Conditioned Stimulus Waveforms Yield Improved Estimates of Neuronal Feature Selectivity and Spike-Timing Precision of Sensory Interneurons

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of “stimulus-reconstruction” kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic “jitter” in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. “Dejittered” estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was ∼5 ms.

Effects of stimulus transformations on estimates of sensory neuron selectivity

Stimulus selectivity of sensory systems is often characterized by analyzing response-conditioned stimulus ensembles. However, in many cases these response-triggered stimulus sets have structure that is more complex than assumed. If not taken into account, when present it will bias the estimates of many simple statistics, and distort the estimated stimulus selectivity of a neural sensory system. We present an approach that mitigates these problems by modeling some of the response-conditioned stimulus structure as being generated by a set of transformations acting on a simple stimulus distribution. This approach corrects the estimates of key statistics and counters biases introduced by the transformations. In cases involving temporal spike jitter or spatial jitter of images, the main observed effects of transformations are blurring of the conditional mean and introduction of artefacts in the spectral decomposition of the conditional covariance matrix. We illustrate this approach by analyzing and correcting a set of model stimuli perturbed by temporal and spatial jitter. We apply the approach to neurophysiological data from the cricket cercal sensory system to correct the effects of temporal jitter.

Structure of the global attractor of cyclic feedback systems

We characterize the dynamics on global attractors of cyclic feedback systems. Under mild restrictions the description is given in terms of a semiconjugacy to a simple model system which possesses Morse-Smale dynamics. However, for the completely general case, no simple model system is feasible and hence we introduce a weaker notion of equivalence, namely, topological semiequivalency. We then prove that the global attractor of a cyclic feedback system is topologically semiequivalent to the original model flow. Main ingredients in the proof are the discrete Lyapunov function introduced by Mallet-Paret and Smith and the Conley index theory.

Modeling arthropod filiform hair motion using the penalty immersed boundary method

Crickets are able to sense their surrounding environment through about 2000 filiform hairs located on a pair of abdominal cerci. The mechanism by which the cricket is able to sense a wide range of input signals using these filiform hairs of different length and orientation is of great interest. Most of the previous filiform hair models have focused on a single, rigid hair in an idealized air field. Here, we present a model of the cercus and filiform hairs that are mechanically coupled to the surrounding air, and the model equations are based on the penalty immersed boundary method. The key difference between the penalty immersed boundary method and the traditional immersed boundary method is the addition of forces to account for density differences between the immersed solid (the filiform hairs) and the surrounding fluid (air). The model is validated by comparing the model predictions to experimental results, and then the model is used to examine the interactions between multiple hairs. With multiple hairs, there is little interaction when the hairs are separated by more than 1mm, and, as they move closer, they interact through viscous coupling, which reduces the deflection of the hairs due to the air movement. We also examine the computational scalability of the algorithm and show that the computational costs grow linearly with the number of hairs being modeled.

Quantitative Characterization of the Filiform Mechanosensory Hair Array on the Cricket Cercus

Background Crickets and other orthopteran insects sense air currents with a pair of abdominal appendages resembling antennae, called cerci. Each cercus in the common house cricket Acheta domesticus is approximately 1 cm long, and is covered with 500 to 750 filiform mechanosensory hairs. The distribution of the hairs on the cerci, as well as the global patterns of their movement vectors, have been characterized semi-quantitatively in studies over the last 40 years, and have been shown to be very stereotypical across different animals in this species. Although the cercal sensory system has been the focus of many studies in the areas of neuroethology, development, biomechanics, sensory function and neural coding, there has not yet been a quantitative study of the functional morphology of the receptor array of this important model system. Methodology/Principal Findings We present a quantitative characterization of the structural characteristics and functional morphology of the cercal filiform hair array. We demonstrate that the excitatory direction along each hairs movement plane can be identified by features of its socket that are visible at the light-microscopic level, and that the length of the hair associated with each socket can also be estimated accurately from a structural parameter of the socket. We characterize the length and directionality of all hairs on the basal half of a sample of three cerci, and present statistical analyses of the distributions. Conclusions/Significance The inter-animal variation of several global organizational features is low, consistent with constraints imposed by functional effectiveness and/or developmental processes. Contrary to previous reports, however, we show that the filiform hairs are not re-identifiable in the strict sense.

Somitogenesis clock-wave initiation requires differential decay and multiple binding sites for clock protein.

Somitogenesis is a process common to all vertebrate embryos in which repeated blocks of cells arise from the presomitic mesoderm (PSM) to lay a foundational pattern for trunk and tail development. Somites form in the wake of passing waves of periodic gene expression that originate in the tailbud and sweep posteriorly across the PSM. Previous work has suggested that the waves result from a spatiotemporally graded control protein that affects the oscillation rate of clock-gene expression. With a minimally constructed mathematical model, we study the contribution of two control mechanisms to the initial formation of this gene-expression wave. We test four biologically motivated model scenarios with either one or two clock protein transcription binding sites, and with or without differential decay rates for clock protein monomers and dimers. We examine the sensitivity of wave formation with respect to multiple model parameters and robustness to heterogeneity in cell population. We find that only a model with both multiple binding sites and differential decay rates is able to reproduce experimentally observed waveforms. Our results show that the experimentally observed characteristics of somitogenesis wave initiation constrain the underlying genetic control mechanisms.

Temporal encoding in a nervous system.

We examined the extent to which temporal encoding may be implemented by single neurons in the cercal sensory system of the house cricket Acheta domesticus. We found that these neurons exhibit a greater-than-expected coding capacity, due in part to an increased precision in brief patterns of action potentials. We developed linear and non-linear models for decoding the activity of these neurons. We found that the stimuli associated with short-interval patterns of spikes (ISIs of 8 ms or less) could be predicted better by second-order models as compared to linear models. Finally, we characterized the difference between these linear and second-order models in a low-dimensional subspace, and showed that modification of the linear models along only a few dimensions improved their predictive power to parity with the second order models. Together these results show that single neurons are capable of using temporal patterns of spikes as fundamental symbols in their neural code, and that they communicate specific stimulus distributions to subsequent neural structures.

ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast

Biologists have long observed periodic-like oxygen consumption oscillations in yeast populations under certain conditions, and several unsatisfactory explanations for this phenomenon have been proposed. These ‘autonomous oscillations’ have often appeared with periods that are nearly integer divisors of the calculated doubling time of the culture. We hypothesize that these oscillations could be caused by a form of cell cycle synchronization that we call clustering. We develop some novel ordinary differential equation models of the cell cycle. For these models, and for random and stochastic perturbations, we give both rigorous proofs and simulations showing that both positive and negative growth rate feedback within the cell cycle are possible agents that can cause clustering of populations within the cell cycle. It occurs for a variety of models and for a broad selection of parameter values. These results suggest that the clustering phenomenon is robust and is likely to be observed in nature. Since there are necessarily an integer number of clusters, clustering would lead to periodic-like behaviour with periods that are nearly integer divisors of the period of the cell cycle. Related experiments have shown conclusively that cell cycle clustering occurs in some oscillating yeast cultures.

Binding Cooperativity in Phage λ is Not Sufficient to Produce an Effective Switch

In the wild-type phage lambda, binding of CI to O(R)2 helps polymerase bound to P(RM) transition from a closed to open complex. Activators on other promoters increase the polymerase-DNA binding energy, or affect both the binding energy and the closed-open transition probability. Using a validated mathematical model, we show that these two modes of upregulation have very different effects on the promoter function. We predict that if CI(2) bound to O(R)2 produced equal increase in RNAP-DNA binding constant (compared to wild-type increase in the closed-open transition probability), the lysogen would be significantly less stable.