Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.     

Molecular dynamics and free energy analysis of neuraminidase-ligand interactions

We report molecular dynamics calculations of neuraminidase in complex with an inhibitor, 4-amino-2-deoxy-2,3-didehydro-N-acetylneuraminic acid (N-DANA), with subsequent free energy analysis of binding by using a combined molecular mechanics/continuum solvent model approach. A dynamical model of the complex containing an ionized Glu119 amino acid residue is found to be consistent with experimental data. Computational analysis indicates a major van der Waals component to the inhibitor-neuraminidase binding free energy. Based on the N-DANA/neuraminidase molecular dynamics trajectory, a perturbation methodology was used to predict the binding affinity of related neuraminidase inhibitors by using a force field/Poisson-Boltzmann potential. This approach, incorporating conformational search/local minimization schemes with distance-dependent dielectric or generalized Born solvent models, correctly identifies the most potent neuraminidase inhibitor. Mutation of the key ligand four-substituent to a hydrogen atom indicates no favorable binding free energy contribution of a hydroxyl group; conversely, cationic substituents form favorable electrostatic interactions with neuraminidase. Prospects for further development of the method as an analysis and rational design tool are discussed.     

A molecular dynamics study of a miRNA:mRNA interaction

In this paper we present a methodology to evaluate the binding free energy of a miRNA:mRNA complex through molecular dynamics (MD)–thermodynamic integration (TI) simulations. We applied our method to the Caenorhabditis elegans let-7 miRNA:lin-41 mRNA complex—a validated miRNA:mRNA interaction—in order to estimate the energetic stability of the structure. To make the miRNA:mRNA simulation possible and realistic, the methodology introduces specific solutions to overcome some of the general challenges of nucleic acid simulations and binding free energy computations that have been discussed widely in many previous research reports. The main features of the proposed methodology are: (1) positioning of the restraints imposed on the simulations in order to guarantee complex stability; (2) optimal sampling of the phase space to achieve satisfactory accuracy in the binding energy value; (3) determination of a suitable trade-off between computational costs and accuracy of binding free energy computation by the assessment of the scalability characteristics of the parallel simulations required for the TI. The experiments carried out demonstrate that MD simulations are a viable strategy for the study of miRNA binding characteristics, opening the way to the development of new computational target prediction methods based on three-dimensional structure information.     

A biomolecular implementation of logically reversible computation with minimal energy dissipation

Energy dissipation associated with logic operations imposes a fundamental physical limit on computation and is generated by the entropic cost of information erasure, which is a consequence of irreversible logic elements. We show how to encode information in DNA and use DNA amplification to implement a logically reversible gate that comprises a complete set of operators capable of universal computation. We also propose a method using this design to connect, or 'wire', these gates together in a biochemical fashion to create a logic network, allowing complex parallel computations to be executed. The architecture of the system permits highly parallel operations and has properties that resemble well known genetic regulatory systems.     

Dynamical and integrative cell signaling: challenges for the new biology

Years of careful experimental analysis have revealed that signaling molecules are organized into complex networks of biochemical reactions exquisitely regulated in time and space to provide a cell with high-fidelity information about an extremely noisy and volatile environment. A new view of signaling networks as systems consisting of multiple complex elements interacting in a multifarious fashion is emerging, a view that conflicts with the single-gene or protein-centric approach common in biological research. The postgenomic era has brought about a different, network-centric methodology of analysis, suddenly forcing researchers toward the opposite extreme of complexity, where the networks being explored are, to a certain extent, intractable and uninterpretable. Both the cartoons of simple pathways and the very large "hair-ball" diagrams of large intracellular networks are also representations of static worlds, superficially devoid of dynamics and chemistry. These representations are often viewed as being analogous to stably linked computer and neural networks rather than dynamically changing networks of chemical interactions, where the notions of concentration, compartmentalization, and diffusion may be the primary determinants of connectivity. Arguably, the systems biology approach, relying on computational modeling coupled with various experimental techniques and methodologies, will be an essential component of analysis of the behavior of signal transduction pathways. Combining the dynamical view of rapidly evolving responses and the structural view arising from high-throughput analyses of the interacting species will be the best approach toward efforts toward greater understanding of intracellular signaling processes.     

Complex spatial dynamics of oncolytic viruses in vitro: mathematical and experimental approaches

Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call "hollow ring structure", "filled ring structure", and "disperse pattern". An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built.     

Local feature analysis: a statistical theory for reproducible essential dynamics of large macromolecules

Multivariate statistical methods are widely used to extract functional collective motions from macromolecular molecular dynamics (MD) simulations. In principal component analysis (PCA), a covariance matrix of positional fluctuations is diagonalized to obtain orthogonal eigenvectors and corresponding eigenvalues. The first few eigenvectors usually correspond to collective modes that approximate the functional motions in the protein. However, PCA representations are globally coherent by definition and, for a large biomolecular system, do not converge on the time scales accessible to MD. Also, the forced orthogonalization of modes leads to complex dependencies that are not necessarily consistent with the symmetry of biological macromolecules and assemblies. Here, we describe for the first time the application of local feature analysis (LFA) to construct a topographic representation of functional dynamics in terms of local features. The LFA representations are low dimensional, and like PCA provide a reduced basis set for collective motions, but they are sparsely distributed and spatially localized. This yields a more reliable assignment of essential dynamics modes across different MD time windows. Also, the intrinsic dynamics of local domains is more extensively sampled than that of globally coherent PCA modes.     

Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.     

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system''s collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.     

Isolation and electron microscopic characterization of the high molecular mass aminoacyl-tRNA synthetase complex from murine erythroleukemia cells

A high molecular mass aminoacyl-tRNA synthetase complex has been isolated from a murine erythroleukemia cell line. This multienzyme complex contains activities for the arginyl-, aspartyl-, glutamyl-, glutaminyl-, isoleucyl,- leucyl-, lysyl-, methionyl-, and prolyl-tRNA synthetases. This enzyme composition, the polypeptide pattern observed upon sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the relative stoichiometry of the component polypeptides are characteristic of high molecular mass complexes of aminoacyl-tRNA synthetases isolated from a variety of mammalian tissues and cell types. Negatively stained preparations of native complex and of glutaraldehyde-treated material have been examined by electron microscopy. In both cases, a distinctive particle is observed which appears in several orientations. The most common views are of two different projections of a squarish particle that measures approximately 27 x 27 nm. Other commonly observed views are of a "U" shape, a rectangle, and a triangle. All of these views are seen in both gradient-purified samples and those prepared directly from material as isolated. These data are consistent with a model for the multienzyme aminoacyl-tRNA synthetase complex as a "cup" or elongated U structure. These studies demonstrate that the high molecular mass complex of eukaryotic aminoacyl-tRNA synthetases does have a coherent structure that can be visualized by electron microscopy.     

The Structural Dilemma of Bulk Polyethylene: An Intermediary Structure

Background

The Fourier space (reciprocal space) image of bulk polyethylene consists of lines superimposed on the coherent diffuse background. The mixed character of the image indicates the complex nature of these compounds. The inability in detecting full images of reciprocal space of polymeric substances without Compton radiation and the other undesirable diffuse scatterings has misled the structural analysis (structural characterisation) of these materials.

Principal Findings

We propose the use of anomalous diffractometry where, it is possible to obtain a real image of reciprocal space without Compton radiation and other undesirable scatterings. By using classical diffractometry techniques this procedure is not possible. This methodology permitted us to obtain the “Direct Delta function”, in the case of polycrystalline substances that was not previously detected. A new procedure was proposed to interpret the image of reciprocal space of bulk polyethylene. The results show the predominance of the geometry of local order determination compared to the crystal unit cell. The analysis of x-ray diffraction images illustrates that the elementary structural unit is a tetrahedron. This structural unit illustrates the atoms in the network scatter in a coherent diffuse manner. Moreover, the interference function derived from the coherent diffuse scattering dampens out quickly and the degree of randomness is superior to a liquid state. The radial distribution function derived from this interference function shows bond shortening in the tetrahedron configuration. It is this particular effect, which stabilises polyethylene.

Conclusion

Here we show by anomalous diffractometry that the traditional concept of the two-phase or the crystal-defect model is an oversimplification of the complex reality. The exploitation of anomalous diffractometry has illustrated that polyethylene has an intermediate ordered structure.     

On the complex dynamics of intracellular ganglion cell light responses in the cat retina

We recorded intracellular responses from cat retinal ganglion cells to sinusoidal flickering lights, and compared the response dynamics with a theoretical model based on coupled nonlinear oscillators. Flicker responses for several different spot sizes were separated in a smooth generator (G) potential and corresponding spike trains. We have previously shown that the G-potential reveals complex, stimulus-dependent, oscillatory behavior in response to sinusoidally flickering lights. Such behavior could be simulated by a modified van der Pol oscillator. In this paper, we extend the model to account for spike generation as well, by including extended Hodgkin-Huxley equations describing local membrane properties. We quantified spike responses by several parameters describing the mean and standard deviation of spike burst duration, timing (phase shift) of bursts, and the number of spikes in a burst. The dependence of these response parameters on stimulus frequency and spot size could be reproduced in great detail by coupling the van der Pol oscillator and Hodgkin-Huxley equations. The model mimics many experimentally observed response patterns, including non-phase-locked irregular oscillations. Our findings suggest that the information in the ganglion cell spike train reflects both intraretinal processing, simulated by the van der Pol oscillator, and local membrane properties described by Hodgkin-Huxley equations. The interplay between these complex processes can be simulated by changing the coupling coefficients between the two oscillators. Our simulations therefore show that irregularities in spike trains, which normally are considered to be noise, may be interpreted as complex oscillations that might carry information.To the memory of Prof. Otto-Joachim Grusser     

Enzymatic computing

The conformational dynamics of enzymes is a computational resource that fuses milieu signals in a nonlinear fashion. Response surface methodology can be used to elicit computational functionality from enzyme dynamics. We constructed a tabletop prototype to implement enzymatic signal processing in a device context and employed it in conjunction with malate dehydrogenase to perform the linearly inseparable exclusive-or operation. This shows that proteins can execute signal processing operations that are more complex than those performed by individual threshold elements. We view the experiments reported, though restricted to the two-variable case, as a stepping stone to computational networks that utilize the precise reproducibility of proteins, and the concomitant reproducibility of their nonlinear dynamics, to implement complex pattern transformations.     

Automated Design of Complex Dynamic Systems

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system''s structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems.     

DSCR1 is required for both axonal growth cone extension and steering

Local information processing in the growth cone is essential for correct wiring of the nervous system. As an axon navigates through the developing nervous system, the growth cone responds to extrinsic guidance cues by coordinating axon outgrowth with growth cone steering. It has become increasingly clear that axon extension requires proper actin polymerization dynamics, whereas growth cone steering involves local protein synthesis. However, molecular components integrating these two processes have not been identified. Here, we show that Down syndrome critical region 1 protein (DSCR1) controls axon outgrowth by modulating growth cone actin dynamics through regulation of cofilin activity (phospho/dephospho-cofilin). Additionally, DSCR1 mediates brain-derived neurotrophic factor–induced local protein synthesis and growth cone turning. Our study identifies DSCR1 as a key protein that couples axon growth and pathfinding by dually regulating actin dynamics and local protein synthesis.     

Articulating silences: experiential and biomedical constructions of hypertension symptomatology

In this article, we explore the flexible configuration of a local knowledge system about hypertension symptoms, foregrounding it against prevailing biomedical assertions regarding the asymptomatic or "silent" nature of hypertension. The complex and coherent knowledge system held by older African Americans living in a southern, rural community stands in contrast to the current scientific discourse and local biomedical perspectives on hypertension symptomatology. The older African American participants in this study apply local knowledge of hypertension symptomatology to make health decisions nearly every day. Despite this, most biomedical practitioners maintain a distance from these lay sources of knowledge, often remaining stalwart in their refusal to recognize the existence or influence of symptoms. We conclude that authoritative knowledge ultimately lies in the minds and bodies of the elders, who have encountered symptoms as guideposts that direct action, rather than with a biomedical "reality" that is yet unresolved.     

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.     

Ecological consequences of evolution in plant defenses in a metacommunity

Dispersal can affect the assembly of local communities in a metacommunity as well as evolution of local populations in a metapopulation. These two processes may also affect each other in ways that have not yet been well studied and that may have novel effects on community structure. Here, we illustrate the interaction of these two processes on community structure with a model of adaptive evolutionary dynamics of plant defenses in a metacommunity food web involving multiple patches along a productivity gradient. We find an enhanced suite of adaptive plant types in our metacommunity model than is predicted in the absence of dispersal. We also find that this, and the movement of nutrients among patches via dispersal, alters patterns of food web architecture, trophic structure and diversity along the productivity gradient. Overall, our model illustrates that evolutionary and metacommunity dynamics may influence communities in complex interactive ways that may not be predicted by models that ignore either of these types of processes.     

Calculation of the Affinity of the λ Repressor-Operator Complex Based on Free Energy Component Analysis

A calculation of the binding free energy of the u repressor-operator complex is described based on free energy component analysis. The calculations are based on a thermodynamic cycle of seven steps decomposed into a total of 24 individual components. The values of these terms are estimated using a combination of empirical potential functions from AMBER, generalized Born - solvent accessibility calculations, elementary statistical mechanics and semiempirical physicochemical properties. Two alternative approaches are compared, one based on the crystal structure of the complex and the other based on the molecular dynamics simulation of the u repressor-operator complex. The calculated affinity is m 19.7 kcal/mol from the crystal structure calculation and m 17.9 kcal/mol from the MD method. The corresponding experimental affinity of the complex is about m 12.6 kcal/mol, indicating reasonable agreement between theory and experiment, considering the approximations involved in the computational methodology. The results are analyzed in terms of contributions from electrostatics, van der Waal interactions, the hydrophobic effect and solvent release. The capabilities and limitations of free energy component methodology are assessed and discussed on the basis of these results.