Population correlation and population kinship

The processes of gene identity by descent and of allelic identities (or likenesses) between genes have been previously studied under a wide variety of migration and subdivision models of population evolution. Since the two processes follow probabilistically parallel paths, there has been a tendency to consider the two processes as equivalent, and to equate estimates of correlation of allele frequency with those of gene identity by descent. The adequacy of this procedure must depend upon the evolutionary history. Here, by distinguishing the processes and considering them jointly as a population evolves in time, we demonstrate the relationship between them in a simple situation, thus providing a basis for study of the extent to which observed allelic similarity between populations is a reflection of underlying gene identity.     

Some implications of chaos theory for the genetic analysis of human development and variation.

Non-linear epigenetic processes are a potential underlying source of phenotypic differences in development. Simulation studies of twin pairs using simple non-linear development models characterised by chaotic or near-chaotic behavior are presented. The effect of chaotic processes on correlations is to lower them from their initial values, but high initial correlations are affected much less by chaotic and near-chaotic processes than intermediate correlations. Therefore, we would predict that traits affected by chaotic processes would have high MZ and low DZ twin correlations and this is reminiscent of certain traits such as EEG spectra. However the much more frequent observation of MZ correlations close to twice their DZ counterparts would suggest that the role of chaos in development is quite limited.     

Aligning gene expression time series with time warping algorithms

motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another. Results: We present implementations of time warping algorithms applicable to RNA and protein expression data and demonstrate their application to published yeast RNA expression time series. Programs executing two warping algorithms are described, a simple warping algorithm and an interpolative algorithm, along with programs that generate graphics that visually present alignment information. We show time warping to be superior to simple clustering at mapping corresponding time states. We document the impact of statistical measurement noise and sample size on the quality of time alignments, and present issues related to statistical assessment of alignment quality through alignment scores. We also discuss directions for algorithm improvement including development of multiple time series alignments and possible applications to causality searches and non-temporal processes ('concentration warping').     

A robust numerical algorithm for studying biomolecular transport processes

We present a numerical algorithm that is well suited for the study of biomolecular transport processes. In the algorithm a continuous Markov process is discretized as a jump process and the jump rates are derived from local solutions of the continuous system. Consequently, the algorithm has two advantages over standard numerical methods: (1) it preserves detailed balance for equilibrium processes, (2) it is able to handle discontinuous potentials. The formulation of the algorithm also allows us to calculate the effective diffusion coefficient or, equivalently, the randomness parameter. We provide several simple examples of how to implement the algorithm. All the MATLAB functions files needed to reproduce the results presented in the article are available from www.amath.unc.edu/Faculty/telston/matlab_functions.     

Increasing Accessibility to the Blind of Virtual Environments,Using a Virtual Mobility Aid Based On the "EyeCane": Feasibility Study

Virtual worlds and environments are becoming an increasingly central part of our lives, yet they are still far from accessible to the blind. This is especially unfortunate as such environments hold great potential for them for uses such as social interaction, online education and especially for use with familiarizing the visually impaired user with a real environment virtually from the comfort and safety of his own home before visiting it in the real world. We have implemented a simple algorithm to improve this situation using single-point depth information, enabling the blind to use a virtual cane, modeled on the “EyeCane” electronic travel aid, within any virtual environment with minimal pre-processing. Use of the Virtual-EyeCane, enables this experience to potentially be later used in real world environments with identical stimuli to those from the virtual environment. We show the fast-learned practical use of this algorithm for navigation in simple environments.     

Some insights into protein structural class prediction

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distance algorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural class prediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that the least Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (Protein Engineering, 1999; 12:107-118). Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only in the structural prediction area, but in some other relevant areas in protein science as well.     

A fine structural localization of the non-specific cholinesterase activity in glomerular nerve formations (endings)

Snout glabrous skin (rhinarium) of the cat is innervated not only by typical simple lamellar corpuscles but also glomerular formations. In contrast to simple lamellar corpuscles, glomerular nerve formations are located away the dermal papillae. In cross sections, glomerular nerve formation consists of several axonal profiles enveloped by 1-2 cytoplasmic lamellae of Schwann cells. The space among them is filled by collagenous microfibrils and the basal lamina-like material. Capsule was composed from fibroblast-like cells without definite basal lamina. An electron-dense reaction product due to non-specific cholinesterase activity was associated with Schwann cells and their processes surrounding unmyelinated terminal portion of the sensory axons. Abundant reaction product was bound to the collagenous microfibrils and was deposited in extracellular matrix between Schwann cell processes. These results are further evidence for the presence of the non-specific cholinesterase molecules as integral component of the extracellular matrix in sensory corpuscles. On the basis of histochemical study two possible explanation are considered for functional involving of this enzyme in sensory nerve formations.     

Searching for Optimal Sensory Signals: Iterative Stimulus Reconstruction in Closed-Loop Experiments

Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an important clue in a search for relevant sensory signals. We explore this relation and introduce a novel iterative algorithm that allows one to find stimuli that are reliably represented by the sensory system under study. To assess the quality of a neural representation, we use stimulus reconstruction methods. The algorithm starts with the presentation of an initial stimulus (e.g. white noise). The evoked spike train is recorded and used to reconstruct the stimulus online. Within a closed-loop setup, this reconstruction is then played back to the sensory system. Iterating this procedure, the newly generated stimuli can be better and better reconstructed. We demonstrate the feasibility of this method by applying it to auditory receptor neurons in locusts. Our data show that the optimal stimuli often exhibit pronounced sub-threshold periods that are interrupted by short, yet intense pulses. Similar results are obtained for simple model neurons and suggest that these stimuli are encoded with high reliability by a large class of neurons.     

A Stochastic Algorithm for Selecting of Defining Contrasts in Two-Level Experiments

Franklin and Bailey (1977) provided an algorithm for construction of fractional factorial designs for estimating a user specified set of factorial effects. Their algorithm is based on a backtrack procedure. This is computer intensive when the number of factors is not small. We propose a stochastic search method called SEF (sequential elimination of factors) algorithm. The SEF algorithm is a simple modification of the exhaustive approach of the Franklin-Bailey algorithm since defining contrasts for the design of interest are chosen stochastically rather than choosing them in a systematic and exhaustive manner. Our experience shows the probability of success of obtaining a required design to be sufficiently large to make this a practical approach. The success probability may be expected to be rather small if the required design is close to a saturated design. We suggest the use of this stochastic alternative particularly when the number of factors is large. This can offer substantial savings in computing time relative to an exhaustive approach. Moreover, if the SEF algorithm fails to produce a design even after several attempts, one can always revert back to the Franklin-Bailey approach.     

Three-dimensional architectures grown by simple 'stigmergic' agents

A simple model of multi-agent three-dimensional construction is presented. The properties of this model are investigated. Based on these properties, a fitness function is defined to characterize the structured patterns that can be generated by the model. The fitness function assigns a value to each pattern. The choice of the fitness function is validated by the fact that human observers tend to view patterns with high (resp. low) fitness as structured (resp. unstructured). A genetic algorithm based on this fitness function is used to explore the space of possible patterns. The genetic algorithm is able to make use of sub-modules of existing patterns and recombine them to produce novel patterns, but strong epistatic interactions among genes make the fitness landscape rugged and prevent more complex patterns from being produced.     

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis

MOTIVATION: Pathway knowledge in public databases enables us to examine how individual metabolites are connected via chemical reactions and what genes are implicated in those processes. For two given (sets of) compounds, the number of possible paths between them in a metabolic network can be intractably large. It would be informative to rank these paths in order to differentiate between them. RESULTS: Focusing on adjacent pairwise coexpression, we developed an algorithm which, for a specified k, efficiently outputs the top k paths based on a probabilistic scoring mechanism, using a given metabolic network and microarray datasets. Our idea of using adjacent pairwise coexpression is supported by recent studies that local coregulation is predominant in metabolism. We first evaluated this idea by examining to what extent highly correlated gene pairs are adjacent and how often they are consecutive in a metabolic network. We then applied our algorithm to two examples of path ranking: the paths from glucose to pyruvate in the entire metabolic network of yeast and the paths from phenylalanine to sinapyl alcohol in monolignols pathways of arabidopsis under several different microarray conditions, to confirm and discuss the performance analysis of our method.     

Segmentation in Tardigrada and diversification of segmental patterns in Panarthropoda

The origin and diversification of segmented metazoan body plans has fascinated biologists for over a century. The superphylum Panarthropoda includes three phyla of segmented animals—Euarthropoda, Onychophora, and Tardigrada. This superphylum includes representatives with relatively simple and representatives with relatively complex segmented body plans. At one extreme of this continuum, euarthropods exhibit an incredible diversity of serially homologous segments. Furthermore, distinct tagmosis patterns are exhibited by different classes of euarthropods. At the other extreme, all tardigrades share a simple segmented body plan that consists of a head and four leg-bearing segments. The modular body plans of panarthropods make them a tractable model for understanding diversification of animal body plans more generally. Here we review results of recent morphological and developmental studies of tardigrade segmentation. These results complement investigations of segmentation processes in other panarthropods and paleontological studies to illuminate the earliest steps in the evolution of panarthropod body plans.     

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.     

Computational modeling of evolutionary learning processes in the brain

The evolutionary selection circuits model of learning has been specified algorithmically. The basic structural components of the selection circuits model are enzymatic neurons, that is, neurons whose firing behavior is controlled by membrane-bound macromolecules called excitases. Learning involves changes in the excitase contents of neurons through a process of variation and selection. In this paper we report on the behavior of a basic version of the learning algorithm which has been developed through extensive interactive experiments with the model. This algorithm is effective in that it enables single neurons or networks of neurons to learn simple pattern classification tasks in a number of time steps which appears experimentally to be a linear function of problem size, as measured by the number of patterns of presynaptic input. The experimental behavior of the algorithm establishes that evolutionary mechanisms of learning are competent to serve as major mechanisms of neuronal adaptation. As an example, we show how the evolutionary learning algorithm can contribute to adaptive motor control processes in which the learning system develops the ability to reach a target in the presence of randomly imposed disturbances.     

Giant plasma membrane vesicles to study plasma membrane structure and dynamics

The plasma membrane (PM) is a highly heterogenous structure intertwined with the cortical actin cytoskeleton and extracellular matrix. This complex architecture makes it difficult to study the processes taking place at the PM. Model membrane systems that are simple mimics of the PM overcome this bottleneck and allow us to study the biophysical principles underlying the processes at the PM. Among them, cell-derived giant plasma membrane vesicles (GPMVs) are considered the most physiologically relevant system, retaining the compositional complexity of the PM to a large extent. GPMVs have become a key tool in membrane research in the last few years. In this review, I will provide a brief overview of this system, summarize recent applications and discuss the limitations.     

Simple adaptive pH control in bioreactors using gain-scheduling methods

A simple well-performing adaptive control technique for pH control in fermentations of recombinant protein production processes is described and its design procedure is explained. First, the entire control algorithm was simulated and parameterized. Afterwards it was tested in real cultivation processes. The results show that this simple technique leads to significant reductions in the fluctuations of the pH values in microbial cultures at a minimum of expenditures. The signal-to-noise ratio and thus the information captured by the pH signal were increased by about an order of magnitude. This leads to a substantial improvement in the noise of many other process signals that are used to monitor and control the process. For instance, respiratory off-gas data of CO₂ and its derived carbon dioxide production rate signals from the cultures carry much less noise as compared to those values obtained with conventional pH control. Detailed process analysis revealed that even very small pH jumps of 0.03 values during the fermentation were shown to result in pronounced deflections in CO₂-volume fraction of 8% (peak to peak). The proposed controller, maintaining the pH within the interval of 0.01 around the setpoint, reduces the noise considerably.     

Towards an artificial brain

Three components of a brain model operating on neuromolecular computing principles are described. The first component comprises neurons whose input-output behavior is controlled by significant internal dynamics. Models of discrete enzymatic neurons, reaction-diffusion neurons operating on the basis of the cyclic nucleotide cascade, and neurons controlled by cytoskeletal dynamics are described. The second component of the model is an evolutionary learning algorithm which is used to mold the behavior of enzyme-driven neurons or small networks of these neurons for specific function, usually pattern recognition or target seeking tasks. The evolutionary learning algorithm may be interpreted either as representing the mechanism of variation and natural selection acting on a phylogenetic time scale, or as a conceivable ontogenetic adaptation mechanism. The third component of the model is a memory manipulation scheme, called the reference neuron scheme. In principle it is capable of orchestrating a repertoire of enzyme-driven neurons for coherent function. The existing implementations, however, utilize simple neurons without internal dynamics. Spatial navigation and simple game playing (using tic-tac-toe) provide the task environments that have been used to study the properties of the reference neuron model. A memory-based evolutionary learning algorithm has been developed that can assign credit to the individual neurons in a network. It has been run on standard benchmark tasks, and appears to be quite effective both for conventional neural nets and for networks of discrete enzymatic neurons. The models have the character of artificial worlds in that they map the hierarchy of processes in the brain (at the molecular, neuronal, and network levels), provide a task environment, and use this relatively self-contained setup to develop and evaluate learning and adaptation algorithms.