FtsA mutants impaired for self-interaction bypass ZipA suggesting a model in which FtsA's self-interaction competes with its ability to recruit downstream division proteins

Z-ring assembly requires polymers of the tubulin homologue FtsZ to be tethered to the membrane. Although either ZipA or FtsA is sufficient to do this, both of these are required for recruitment of downstream proteins to form a functional cytokinetic ring. Gain of function mutations in ftsA, such as ftsA* (ftsA-R286W), bypass the requirement for ZipA suggesting that this atypical, well-conserved, actin homologue has a more critical role in Z-ring function. FtsA forms multimers both in vitro and in vivo, but little is known about the role of FtsA polymerization. In this study we identify FtsA mutants impaired for self-interaction. Such mutants are able to support Z-ring assembly and are also able to bypass the requirement for ZipA. These mutants, including FtsA*, have reduced ability to self-interact but interact normally with FtsZ and are less toxic if overexpressed. These results do not support a model in which FtsA monomers antagonize FtsZ polymers. Instead, we propose a new model in which FtsA self-interaction competes with its ability to recruit downstream proteins. In this model FtsA self-interaction at the Z ring is antagonized by ZipA, allowing unpolymerized FtsA to recruit downstream proteins such as FtsN.     

Competing species model with behavioral adaptation

This paper studies the properties of a modified Lotka-Volterra model for two competing species, in which the coefficients of the interaction terms are time-dependent averages of the level of interaction over the entire past. For this model, it is shown that (1) competitive exclusion does not occur, (2) there are two possible stable equilibrium points, and (3) in a certain region of parameter space numerical simulations suggest the existence of interesting oscillatory solutions.     

Competing predators for a prey in a chemostat model with periodic nutrient input

A system of ordinary differential equations is used to model the interactions of n competing predators on a single prey population in a chemostat environment with a periodic nutrient input. In the case of one or no predators, criteria for the existence of periodic solutions are given. In the general case, conditions for all populations to persist are derived.Research is in part based on a Ph.D. thesis submitted to the Faculty of Graduate Studies, University of AlbertaResearch is partly supported by the Natural Sciences and Engineering Research Council of Canada, Grant No. NSERC A4823     

An adaptive visual neuronal model implementing competitive, temporally asymmetric Hebbian learning

A novel depth-from-motion vision model based on leaky integrate-and-fire (I&F) neurons incorporates the implications of recent neurophysiological findings into an algorithm for object discovery and depth analysis. Pulse-coupled I&F neurons capture the edges in an optical flow field and the associated time of travel of those edges is encoded as the neuron parameters, mainly the time constant of the membrane potential and synaptic weight. Correlations between spikes and their timing thus code depth in the visual field. Neurons have multiple output synapses connecting to neighbouring neurons with an initial Gaussian weight distribution. A temporally asymmetric learning rule is used to adapt the synaptic weights online, during which competitive behaviour emerges between the different input synapses of a neuron. It is shown that the competition mechanism can further improve the model performance. After training, the weights of synapses sourced from a neuron do not display a Gaussian distribution, having adapted to encode features of the scenes to which they have been exposed.     

Cortical mechanisms for reinforcement learning in competitive games

Game theory analyses optimal strategies for multiple decision makers interacting in a social group. However, the behaviours of individual humans and animals often deviate systematically from the optimal strategies described by game theory. The behaviours of rhesus monkeys (Macaca mulatta) in simple zero-sum games showed similar patterns, but their departures from the optimal strategies were well accounted for by a simple reinforcement-learning algorithm. During a computer-simulated zero-sum game, neurons in the dorsolateral prefrontal cortex often encoded the previous choices of the animal and its opponent as well as the animal's reward history. By contrast, the neurons in the anterior cingulate cortex predominantly encoded the animal's reward history. Using simple competitive games, therefore, we have demonstrated functional specialization between different areas of the primate frontal cortex involved in outcome monitoring and action selection. Temporally extended signals related to the animal's previous choices might facilitate the association between choices and their delayed outcomes, whereas information about the choices of the opponent might be used to estimate the reward expected from a particular action. Finally, signals related to the reward history might be used to monitor the overall success of the animal's current decision-making strategy.     

Stop CRMPing my style: a new competitive model of CRMP oligomerization

Read the full article ‘Insights into the oligomerization of CRMPs: crystal structure of human collapsin response mediator protein 5’ on doi: 10.1111/jnc.12188 .     

Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.     

The role of the striatum in adaptation learning: a computational model

To investigate the functional role of the striatum in visuo-motor adaptation, we extend the DIRECT-model for visuo-motor reaching movements formulated by Bullock et al.(J Cogn Neurosci 5:408–435,1993) through two parallel loops, each modeling a distinct contribution of the cortico–cerebellar–thalamo–cortical and the cortico–striato–thalamo–cortical networks to visuo-motor adaptation. Based on evidence of Robertson and Miall(Neuroreport 10(5): 1029–1034, 1999), we implement the function of the cortico–cerebellar–thalamo–cortical loop as a module that gradually adapts to small changes in sensorimotor relationships. The cortico–striato–thalamo–cortical loop on the other hand is hypothesized to act as an adaptive search element, guessing new sensorimotor-transformations and reinforcing successful guesses while punishing unsuccessful ones. In a first step, we show that the model reproduces trajectories and error curves of healthy subjects in a two dimensional center-out reaching task with rotated screen cursor visual feedback. In a second step, we disable learning processes in the cortico–striato– thalamo–cortical loop to simulate subjects with Parkinson’s disease (PD), and show that this leads to error curves typical of subjects with PD. We conclude that the results support our hypothesis, i.e., that the role of the cortico–striato–thalamo–cortical loop in visuo-motor adaptation is that of an adaptive search element.     

Effects of introducing cultured human chondrocytes into a human articular cartilage explant model

Articular cartilage (AC) heals poorly and effective host-tissue integration after reconstruction is a concern. We have investigated the ability of implanted chondrocytes to attach at the site of injury and to be incorporated into the decellularized host matrix adjacent to a defect in an in vitro human explant model. Human osteochondral dowels received a standardized injury, were seeded with passage 3 chondrocytes labelled with PKH 26 and compared with two control groups. All dowels were cultured in vitro, harvested at 0, 7, 14 and 28 days and assessed for chondrocyte adherence and migration into the region of decellularized tissue adjacent to the defects. Additional evaluation included cell viability, general morphology and collagen II production. Seeded chondrocytes adhered to the standardized defect and areas of lamina splendens disruption but did not migrate into the adjacent acellular region. A difference was noted in viable-cell density between the experimental group and one control group. A thin lattice-like network of matrix surrounded the seeded chondrocytes and collagen II was present. The results indicate that cultured human chondrocytes do indeed adhere to regions of AC matrix injury but do not migrate into the host tissue, despite the presence of viable cells. This human explant model is thus an effective tool for studying the interaction of implanted cells and host tissue.     

Extinctive evolution: Extinctive and competitive evolution combine into a unified model of evolution

Individual extinctions of abundant and widespread species of marine Protista are abrupt and precede the appearance of new species. New species evolve gradually in marginal marine environments and spread only if a suitable ecological domain is available or if such a domain is made available by the disappearance of its occupant species. Competitive evolution, with its classic processes of genetic drift, adaptation, competition, and survival of the fittest, occurs mainly in marginal environments (and possibly within broadly distributed but rare species). Extinctive evolution, on the other hand, with its processes of sudden extinctions and sudden appearances, absence of competition, absence of “missing links”, and frequent survival of the misfit or the indifferently fit is prevalent in broader environments, and more generally applicable to the paleontological record. The modern biosphere is not necessarily better adapted than its predecessors. Global mass extinction affecting different taxa across a broad spectrum of environments is caused by extraordinary environmental disturbances. A major ecosphere is vacated, which is immediately occupied by surviving misfits. These are replaced, through competitive evolution, by a rapid succession of increasingly better adapted species that can be classified into different genera and higher taxa (“macroevolution”). Equilibrium is largely re-established within a few million years. Competitive and extinctive evolution combine into a unified model of evolution.     

A mathematical model of periodically pulsed chemotherapy: Tumor recurrence and metastasis in a competitive environment

A competition model describing tumor-normal cell interaction with the added effects of periodically pulsed chemotherapy is discussed. The model describes parameter conditions needed to prevent relapse following attempts to remove the tumor or tumor metastasis. The effects of resistant tumor subpopulations are also investigated and recurrence prevention strategies are explored.     

Caught in self-interaction: evolutionary and functional mechanisms of protein homooligomerization

Many soluble and membrane proteins form homooligomeric complexes in a cell which are responsible for the diversity and specificity of many pathways, may mediate and regulate gene expression, activity of enzymes, ion channels, receptors, and cell adhesion processes. The evolutionary and physical mechanisms of oligomerization are very diverse and its general principles have not yet been formulated. Homooligomeric states may be conserved within certain protein subfamilies and might be important in providing specificity to certain substrates while minimizing interactions with other unwanted partners. Moreover, recent studies have led to a greater awareness that transitions between different oligomeric states may regulate protein activity and provide the switch between different pathways. In this paper we summarize the biological importance of homooligomeric assemblies, physico-chemical properties of their interfaces, experimental and computational methods for their identification and prediction. We particularly focus on homooligomer evolution and describe the mechanisms to develop new specificities through the formation of different homooligomeric complexes. Finally, we discuss the possible role of oligomeric transitions in the regulation of protein activity and compile a set of experimental examples with such regulatory mechanisms.     

Academic learning,a variational model

By assigning coordinates to the information space comprising all knowledge, rigorous mathematical interpretations can be placed on such terms as academic ability, memory and creativity such that these psychometric concepts can be incorporated into a framework of functional analysis which then permits the optimization of long-term academic learning processes through the location of the teaching trajectories in information space which will maximize the knowledge accumulated in a generalized educational system composed of a complex of subject-pupil-teacher interactions. The concepts of discrete and continuous information spaces are discussed in connection with subject-subject, subjectpupil and pupil-pupil interactions, and the advantages of using variational versus dynamic programming methods of optimizing alternative educational systems are evaluated.     

Robust uniform persistence and competitive exclusion in a nonautonomous multi-strain SIR epidemic model with disease-induced mortality

A nonautonomous version of the SIR epidemic model in Ackleh and Allen (2003) is considered, for competition of $n$ infection strains in a host population. The model assumes total cross immunity, mass action incidence, density-dependent host mortality and disease-induced mortality. Sufficient conditions for the robust uniform persistence of the total population, as well as of the susceptible and infected subpopulations, are given. The first two forms of persistence depend entirely on the rate at which the population grows from the extinction state, respectively the rate at which the disease is vertically transmitted to offspring. We also discuss the competitive exclusion among the $n$ infection strains, namely when a single infection strain survives and all the others go extinct. Numerical simulations are also presented, to account for the situations not covered by the analytical results. These simulations suggest that the nonautonomous nature of the model combined with the disease induced mortality allow for many strains to coexist. The theoretical approach developed here is general enough to apply to other nonautonomous epidemic models.     

Latent learning,shortcuts and detours: a computational model

Voicu and Schmajuk (Rob. Auto. Syst. 35 (2001a) 23) described a model of spatial navigation and exploration that includes an action system capable of guiding, with the help of a cognitive system, the search for specific goals as determined by a motivation system. Whereas in the original model the cognitive map stores information about the connectivity between places in the environment, in the present version the cognitive map also stores information about the paths traversed by the agent. Computer simulations show that the network correctly describes experimental results including latent learning in a maze, detours in a maze, and shortcuts in an open field. In addition, the model generates novel predictions about detours and shortcuts in an open field.     

Associative learning in a network model of Hermissenda crassicornis

A time-varying Resistance-Capacitance (RC) circuit computer model was constructed based on known membrane and synaptic properties of the visualvestibular network of the marine snail Hermissenda crassicornis. Specific biophysical properties and synaptic connections of identified neurons are represented as lumped parameters (circuit elements) in the model; in the computer simulation, differential equations are approximated by difference equations. The model's output, membrane potential, an indirect measure of firing frequency, closely parallels the behavioral and electrophysiologic outputs of Hermissenda in response to the same input stimuli presented during and after associative learning. The parallelism of the computer modeled and the biologic outputs suggests that the model captures the features necessary and sufficient for associative learning.     

Associative learning in a network model of Hermissenda crassicornis

A companion paper in a previous issue of this journal presented a resistance-capacitance circuit computer model of the four-neuron visual-vestibular network of the invertebrate marine mollusk Hermissenda crassicornis. In the present paper, we demonstrate that changes in the model's output in response to simulated associative training is quantitatively similar to behavioral and electrophysiological changes in response to associative training of Hermissenda crassicornis. Specifically, the model demonstrates many characteristics of conditioning: sensitivity to stimulus contingency, stimulus specificity, extinction, and savings. The model's learning features also are shown to be devoid of non-associative components. Thus, this computational model is an excellent tool for examining the information flow and dynamics of biological associative learning and for uncovering insights concerning associative learning, memory, and recall that can be applied to the development of artificial neural networks.     

Why a neuromaturational model of memory fails: Exuberant learning in early infancy

The characteristics of memory in infants and adults seem vastly different. The neuromaturational model attributes these differences to an ontogenetic change in the basic memory process, namely, to the hierarchical maturation of two distinct memory systems. The early-maturing (implicit) system is functional during the first third of infancy and supports the gradual learning of perceptual and motor skills; the late-maturing (explicit) system supports representations of contextually specific events, relationships, and associations. An alternative model holds that the basic memory process does not change, but what infants and adults select to encode for learning does. This ontogenetic change in selective attention has been mistaken for an ontogenetic shift in the basic memory process. Over the last 25 years, evidence from transfer studies with developing rats and human infants has revealed that the first third of infancy is actually a period of exuberant learning that ends, not coincidentally, at the same age that the late-maturing memory system presumably emerges. This article reviews data from recent studies of sensory preconditioning, potentiation, associative chains, and transitive inference with human infants that support this conclusion—data for which the neuromaturational model cannot account. Fast mapping is a general learning mechanism that accounts for this evidence.