The construction of a simultaneous functional order in nervous systems

We have developed two algorithms that construct a simultaneous functional order in a collection of neural elements using purely functional relations. The input of the first algorithm is a matrix describing the total of covariances of signals carried by the members of the neural collection. The second algorithm proceeds from a matrix describing a primitive inclusion relation among the members of the neural collection that can be determined from coincidences in their signal activity. From this information both algorithms compute a partial functional order in the collection of neural elements. Such an order has an objective existence for the system itself and not only for an external observer. By either merging individual neurons or recruiting previously unspecified ones the partial order is locally transformed into a lattice order. Thus, the simultaneous functional order in a nervous net may become isomorphic with a geometrical order if the system has eneough internal coherence. Simulation experiments were done, both for the neuron-merging and the neuron-recruitment routines, to study the number of individuals in the resulting lattice order as a function of the number of individuals in the underlying partially ordered set.     

The construction of a simultaneous functional order in nervous systems

The functional order of a collection of nervous elements is available to the system itself, as opposed to the anatomical geometrical order which exists only for external observers. It has been shown before (Part I) that covariances or coincidences in the signal activity of a neural net can be used in the construction of a simultaneous functional order in which a modality is represented as a concatenation of districts with a lattice structure. In this paper we will show how the resulting functional order in a nervous net can be related to the geometry of the underlying detector array. In particular, we will present an algorithm to construct an abstract geometrical complex from this functional order. The algebraic structure of this complex reflects the topological and geometrical structure of the underlying detector array. We will show how the activated subcomplexes of a complex can be related to segments of the detector array that are activated by the projection of a stimulus pattern. The homology of an abstract complex (and therefore of all of its subcomplexes) can be obtained from simple combinatorial operations on its coincidence scheme. Thus, both the geometry of a detector array and the topology of projections of stimulus patterns may have an objective existence for the neural system itself.     

The construction of a simultaneous functional order in nervous systems

The signal activity in a neural net will be constrained both by its physical structure and by environmental constraints. By monitoring its signal activity a neural system can build up a simultaneous functional order that encodes these constraints. We have previously (Part I) presented two models that construct a simultaneous functional order in a collection of neural elements using either signal-covariances or signal-coincides. In this paper we present the results of simulation experiments that were performed to study the influence of the physical constraints of a neural system on the simultaneous functional order produced by both models. In the simulation experiments we used a one-dimensional detector array. We delineate the physical constraints such an array has to satisfy in order to induce a functional order relation that allows an isomorphism with a geometrical order. We show that for an appropriate choice of the system parameters both models can produce a simultaneous functional order with sufficient internal coherence to allow isomorphisms with a triangulation. In this case the dimensionality and the coherence of the detector array are objectively available to the system itself.     

Genome-wide analysis of mammalian DNA segment fusion/fission

As a powerful tool for gene function prediction, gene fusion has been widely studied in prokaryotes and certain groups of eukaryotes, but it has been little applied in studies of mammalian genomes. With the first fully sequenced mammalian genomes (human, mouse, rat) now available, we defined and collected a set of fusion/fission event-linked segments (FFLS) based on structured organized genomic alignment. The statistics of the sequence features highlighted the FFLSs against their random context. We found that there are three groups of FFLSs with different component pairs (i.e. gene-gene, gene-noncoding and noncoding-noncoding) in all three mammalian genomes. The proteins encoded by the components of FFLSs in the first group shown a strong tendency to interact with each other. The segmental components in the last two groups which did not contain any protein-coding genes, were found not only to be transcribed to some level, but also more conserved than the random background. Thus, these segments are possibly carrying certain biologically functional elements. We propose that FFLS may be a potential tool for prediction and analysis of function and functional interaction of genetic elements, including both genes and noncoding elements, in mammalian genomes. The full list of the FFLSs in the genomes of the three mammals is available as supporting information at doi:10.1016/j.jtbi.2005.09.016.     

A collection of strains containing genetically linked alternating antibiotic resistance elements for genetic mapping of Escherichia coli.

We present a collection of 182 isogenic strains containing genetically linked antibiotic resistance elements located at approximately 1-min intervals around the Escherichia coli chromosome. At most positions both Tn10 (Tetr) and TN10kan (Kanr) elements are available, so that the collection contains a linked set of alternating antibiotic resistance markers. The map position of each insertion has been aligned to the E. coli genetic map as well as to the Kohara ordered clone bank. These strains are designed to be used in a rapid two-step mapping system in E. coli. In the first step, the mutation is localized to a 5- to 15-min region of the chromosome by Hfr mapping with a set of Hfr strains containing either Tn10 or Tn10kan elements located 20 min from their respective origins of transfer. In the second step, the mutation is localized to a 1-min region by P1 transduction, with a collection of isogenic insertion strains as donors. We discuss the uses of this collection of strains to map and eventually to clone a variety of mutations in E. coli.     

Plasmon-Enhanced Second Harmonic Generation: from Individual Antennas to Extended Arrays

We analyze the emission yield of the second harmonic generation (SHG) from dense ordered arrays of L-shaped Au nanoantennas within a well-defined collection angle and compare it to that of the isolated nanostructures designed with the same geometrical parameters. Thanks to the high antenna surface density, arrays display one order of magnitude higher SHG yield per unit surface with respect to isolated nanoantennas. The difference in the collected nonlinear signals becomes even more pronounced by reducing the collection angle, because of the efficient angular filtering that can be attained in dense arrays around the zero order. Albeit this key-enabling feature allows envisioning application of these platforms to nonlinear sensing, a normalization of the SHG yield to the number of excited antennas in the array reveals a reduced nonlinear emission from each individual antenna element. We explain this potential drawback in terms of resonance broadening, commonly observed in densely packed arrays, and angular filtering of the single antenna emission pattern provided by the array 0th order.

     

Mapping of human H chain V region genes (VH4) using deletional analysis and pulsed field gel electrophoresis.

We have used molecular genetic mapping techniques to establish the order and approximate chromosomal locations of VH4 elements on both chromosomes 14 from a single patient. A total of 10 BglII restriction fragments carrying VH4 elements was characterized. The genomic order of VH4-carrying restriction fragments was determined by analysis of the pattern of loss of hybridizing bands from the genomes of a panel of monoclonal lymphoblastoid cell lines which had well characterized rearrangements of the Ig locus on each chromosome. Some individual elements were identified using sequence-specific oligonucleotide probes. Physical dimensions were estimated by the assignment of these ordered elements to large (50-350-kb) restriction fragments using two-dimensional pulse field gel electrophoresis. Six such fragments spanning approximately 890 kb were physically linked and ordered. The chromosomes differed with respect to the complement of VH4 elements, although no evidence was found of major differences in organization. The establishment of a panel of well characterized deletion lines facilitates the rapid mapping of defined restriction fragments carrying VH elements.     

The remapping of space in motor learning and human–machine interfaces

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human–machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human–machine interfaces and optimal control.     

Smart medication through combination of synthetic biology and cell microencapsulation

Recent advances in the field of synthetic biology have led to the design of a new generation of complex, man-made biological networks that operate inside living cells in a desired manner. Key elements of these systems are often controllable genetic switches that are capable of processing therapeutic signals by sensing and responding to the environment. For biomedical applications, however, it is necessary to seal these engineered cells in order to protect them from the host immune system and enable straightforward removal after completion of the therapy. A promising and successful approach is the microencapsulation of defined cells into a semi-permeable and biocompatible microcapsule. Shielding from the external environment still allows exchange to occur on a molecular basis. Thus, the powerful combination of synthetic biology and microencapsulation has been opening the door to novel and innovative cell-based biomedical applications, such as smart implantable drug delivery systems. This review highlights recent developments in the overlap of these two areas, thereby presenting promising developments and perspectives for future treatment strategies.     

On the Modified and Inflated Discrete Distributions

Firstly, a modified bivariate discrete distribution is considered where a set of counts are misreported as another set of counts with different modification rates. Variances and covariances are put in the closed form and for the case when all modification rates are the same, these variances and covariances are expressed as parabolic functions and they are actually evaluated for the bivariate negative binomial. Regarding the asymptotic distributions of the estimates, elements of variance-covariance matrix are obtained. Next, a multivariate inflated discrete distribution is taken up. For the case of inflated multivariate negative binomial, Bayesian estimates of inflation as well as those of parameters are given.     

Computing the breakpoint distance between partially ordered genomes

The total order of genes or markers on a chromosome is crucial for most comparative genomics studies. However, current gene mapping efforts might only suffice to provide a partial order of the genes on a chromosome. Several different genes or markers might be mapped at the same position due to the low resolution of gene mapping or missing data. Moreover, conflicting datasets might give rise to the ambiguity of gene order. In this paper, we consider the reversal distance and breakpoint distance problems for partially ordered genomes. We first prove that these problems are nondeterministic polynomial-time (NP)-hard, and then give an efficient heuristic algorithm to compute the breakpoint distance between partially ordered genomes. The algorithm is based on an efficient approximation algorithm for a natural generalization of the well-known feedback vertex set problem, and has been tested on both simulated and real biological datasets. The experimental results demonstrate that our algorithm is quite effective for estimating the breakpoint distance between partially ordered genomes and for inferring the gene (total) order.     

Improved least squares topology testing and estimation

Generalized least squares (GLS) methods provide a relatively fast means of constructing a confidence set of topologies. Because they utilize information about the covariances between distances, it is reasonable to expect additional efficiency in estimation and confidence set construction relative to other least squares (LS) methods. Difficulties have been found to arise in a number of practical settings due to estimates of covariance matrices being ill conditioned or even noninvertible. We present here new ways of estimating the covariance matrices for distances that are much more likely to be positive definite, as the actual covariance matrices are. A thorough investigation of performance is also conducted. An alternative to GLS that has been proposed for constructing confidence sets of topologies is weighted least squares (WLS). As currently implemented, this approach is equivalent to the use of GLS but with covariances set to zero rather than being estimated. In effect, this approach assumes normality of the estimated distances and zero covariances. As the results here illustrate, this assumption leads to poor performance. A 95% confidence set is almost certain to contain the true topology but will contain many more topologies than are needed. On the other hand, the results here also indicate that, among LS methods, WLS performs quite well at estimating the correct topology. It turns out to be possible to improve the performance of WLS for confidence set construction through a relatively inexpensive normal parametric bootstrap that utilizes the same variances and covariances of GLS. The resulting procedure is shown to perform at least as well as GLS and thus provides a reasonable alternative in cases where covariance matrices are ill conditioned.     

Formation of segregated regions of feature detecting cells in self-organizing nerve field

This paper investigates the formation of a segregated region for a specific set of input signals in a nerve field. The segregated region is formed by a feature detecting cell group which fires only for a specific set of input signals. This firing region is called the region of feature detecting cells for the corresponding input set. First, a basic self-organizing model is given. The model is composed of the first input layer, the second nerve cell layer with lateral inhibitory interactions (it is called a lateral-inhibition type nerve field) and an inhibitory nerve cell. An input signal is an-dimensional vector whose components assume continuous values. Next, the condition which gurantees the formation of a segregated region for a specific set of input signals is derived, and the properties of the model are discussed based on the derived condition. In addition, the behavior of the model is examined through computer-simulated experiments. The following observations are made: When a certain condition is satisfied, a segregated region is formed in the nerve field according to a specific set of input signals. By varying the parameters of learning, the region is formed depending on the similarity between input signals. The regions for similar input signals are formed near each other in the nerve field. The region is created depending on the occurence probability of the input signal and its norm.     

Cell adhesion on artificial materials for tissue engineering

Advanced interdisciplinary scientific field of tissue engineering has been developed to meet increasing demand for safe, functional and easy available substitutes of irreversibly damaged tissues and organs. First biomaterials were constructed as "two-dimensional" (allowing cell adhesion only on their surface), and durable (non-biodegradable). In contrast, biomaterials of new generation are characterized by so-called three dimensional porous or scaffold-like architecture promoting attachment, growth and differentiation of cells inside the material, accompanied by its gradual removal and replacement with regenerated fully functional tissue. In order to control these processes, these materials are endowed with a defined spectrum of bioactive molecules, such as ligands for adhesion receptors on cells, functional parts of natural growth factors, hormones and enzymes or synthetic regulators of cell behavior, incorporated in defined concentrations and spatial distribution against a bioinert background resistant to uncontrolled protein adsorption and cell adhesion.     

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.     

AGenDA: gene prediction by comparative sequence analysis

Comparative sequence analysis is a powerful approach to identify functional elements in genomic sequences. Herein, we describe AGenDA (Alignment-based GENe Detection Algorithm), a novel method for gene prediction that is based on long-range alignment of syntenic regions in eukaryotic genome sequences. Local sequence homologies identified by the DIALIGN program are searched for conserved splice signals to define potential protein-coding exons; these candidate exons are then used to assemble complete gene structures. The performance of our method was tested on a set of 105 human-mouse sequence pairs. These test runs showed that sensitivity and specificity of AGenDA are comparable with the best gene- prediction program that is currently available. However, since our method is based on a completely different type of input information, it can detect genes that are not detectable by standard methods and vice versa. Thus, our approach seems to be a useful addition to existing gene-prediction programs. Availability: DIALIGN is available through the Bielefeld Bioinformatics Server (BiBiServ) at http://bibiserv.techfak.uni-bielefeld.de/dialign/ The gene-prediction program AGenDA described in this paper will be available through the BiBiServ or MIPS web server at http://mips.gsf.de.     

Nuclear magnetic resonance studies and molecular dynamics simulations of the solution conformation of a 'designed', alpha-helical peptide.

The complete three-dimensional structure in methanol of an amphipathic alpha-helical peptide, that has been designed by taking into account the three-dimensional structures of small haemolytic peptides, secondary structure prediction algorithms and the well documented literature on alpha-helix stabilizing factors, has been elucidated by two-dimensional NMR spectroscopy. Initially various two-dimensional spectra (COSY, TOCSY, and NOESY) allowed the complete sequence specific assignment of all signals in the 1H spectrum. Consequently trial structures were generated which were then subjected to molecular dynamics simulations using 121 NOE-derived distances and 25 vicinal coupling constant values as structural restraints to give a final set of calculated structures. These structures are in complete agreement with the results of a circular dichroism study and reveal that the peptide adopted a highly ordered alpha-helical conformation. Details of the structure which throw light on future peptide/protein design are discussed.     

Self-organized formation of topologically correct feature maps

This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events. In other words, a principle has been discovered which facilitates the automatic formation of topologically correct maps of features of observable events. The basic self-organizing system is a one- or two-dimensional array of processing units resembling a network of threshold-logic units, and characterized by short-range lateral feedback between neighbouring units. Several types of computer simulations are used to demonstrate the ordering process as well as the conditions under which it fails.