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PubMed2004

Quantitative methods for ecological network analysis.

The analysis of networks of ecological trophic transfers is a useful complement to simulation modeling in the quest for understanding whole-ecosystem dynamics. Trophic networks can be studied in quantitative and systematic fashion at several levels. Indirect relationships between any two individual taxa in an ecosystem, which often differ in either nature or magnitude from their direct influences, can be assayed using techniques from linear algebra. The same mathematics can also be employed to ascertain where along the trophic continuum any individual taxon is operating, or to map the web of connections into a virtual linear chain that summarizes trophodynamic performance by the system. Backtracking algorithms with pruning have been written which identify pathways for the recycle of materials and energy within the system. The pattern of such cycling often reveals modes of control or types of functions exhibited by various groups of taxa. The performance of the system as a whole at processing material and energy can be quantified using information theory. In particular, the complexity of process interactions can be parsed into separate terms that distinguish organized, efficient performance from the capacity for further development and recovery from disturbance. Finally, the sensitivities of the information-theoretic system indices appear to identify the dynamical bottlenecks in ecosystem functioning.

Animals↗
PubMed2004

Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast.

The discriminative ability of established diagnostic criteria for MRI of the breast is assessed, and their relative relevance using artificial neural networks (ANNs) is determined. A total of 89 women with 105 histopathologically verified breast lesions (73 invasive cancers, 2 in situ cancers, and 30 benign lesions) were included in this study. A T1-weighted 3D FLASH sequence was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.2 mmol/kg body weight. ANN models were built to test the discriminative ability of kinetic, morphologic, and combined MR features. The subjects were randomly divided into two parts: a training set of 59 lesions and a verification set of 46 lesions. The training set was used for learning, and the performance of each model was evaluated on the verification set by measuring the area under the ROC curve (Az). An optimally minimized model was constructed using the most relevant input variables that were determined by the automatic relevance determination (ARD) method. ANN models were compared with the performance of a human reader. Margin type, time-to-peak enhancement, and washout ratio showed the highest discriminative ability among diagnostic criteria and comprised the minimized model. Compared with the expert radiologist (Az = 0.799), using the same prediction scale, the minimized ANN model performed best (Az = 0.771), followed by the best kinetic (Az = 0.743), the maximized (Az = 0.727), and the morphologic model (Az = 0.678). The performance of a neural network prediction model is comparable to that of an expert radiologist. A neurostatistical approach is preferred for the analysis of diagnostic criteria when many parameters are involved and complex nonlinear relationships exist in the data set.

Adult↗
PubMed1996

A model of the immune network with B-T cell co-operation. I--Prototypical structures and dynamics.

Hitherto, "second generation" network models of the immune system have all been restricted to B-lymphocytes and the Ig molecules they produce. These models have not so far been able to provide a convincing mechanism for the distinction between a "Central Immune System" (CIS) composed of a connected network of lymphocyte clones which couple with "self" antigens in a tolerant mode, and a "Peripheral Immune System" (PIS) composed of clones with little or no supra-clonal organization and which produce classical immune responses when interacting with "non-self" antigens. Here, we present a new network model which explicitly incorporates B-T cell co-operation. In this model, B-cell activation is dependent on T-cell help, and activated T-cells are down-regulated by engagement of their TCRs by soluble Ig. We discuss the underlying biology on which we base the system of ordinary differential equations which defines the present network model. We then illustrate some basic features of the model by examining several prototypical situations with a small number of clones. Depending on the idiotypic connectivity structure, the model exhibits two distinct modes of coupling with antigens: an "immune response" mode in which T- and B-cell clones grow exponentially; and a "tolerant" mode in which T-cell clones are controlled by inclusion of all TCRs in the repertoire of an idiotypic B-cell network. Finally, we discuss the simplifying assumptions of the present model and argue that its range of validity is indeed the region of the state-space of the system where the discrimination between the CIS and the PIS take place.

B-Lymphocytes↗
PubMed2004

Modeling T-cell activation using gene expression profiling and state-space models.

MOTIVATION: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. RESULTS: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. AVAILABILITY: Supplementary data and Matlab computer source code will be made available on the web at the URL given below. SUPPLEMENTARY INFORMATION: http://public.kgi.edu/~wild/LDS/index.htm

Computer Simulation↗
PubMed2005

Modelling the evolution of genetic regulatory networks.

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Algorithms↗
PubMed1999

Global dynamics of bidirectional associative memory neural networks involving transmission delays and dead zones.

In this article, a model describing the activation dynamics of bidirectional associative memory (BAM) neural networks involving transmission delays was considered. The concept of BAM networks employed in this work is improved and it includes the earlier notions known in the literature and is applied to a wider class of networks. Further, we introduced a new notion, as a measure of restoring stability and termed it as a dead zone. In this article, the influence of the presence of dead zones on the global asymptotic stability of the equilibrium pattern was investigated. Existence and uniqueness of an equilibrium pattern under fairly general and easily verifiable conditions were also established.

Journal Article↗
PubMed1996

A low-dimensional, time-resolved and adapting model neuron.

A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks.

Action Potentials↗
PubMed1999

Synchrony and desynchrony in integrate-and-fire oscillators.

Due to many experimental reports of synchronous neural activity in the brain, there is much interest in understanding synchronization in networks of neural oscillators and its potential for computing perceptual organization. Contrary to Hopfield and Herz (1995), we find that networks of locally coupled integrate-and-fire oscillators can quickly synchronize. Furthermore, we examine the time needed to synchronize such networks. We observe that these networks synchronize at times proportional to the logarithm of their size, and we give the parameters used to control the rate of synchronization. Inspired by locally excitatory globally inhibitory oscillator network (LEGION) dynamics with relaxation oscillators (Terman & Wang, 1995), we find that global inhibition can play a similar role of desynchronization in a network of integrate-and-fire oscillators. We illustrate that a LEGION architecture with integrate-and-fire oscillators can be similarly used to address image analysis.

Algorithms↗
PubMed2005

Hierarchical organization in smooth dynamical systems.

This article is concerned with defining and characterizing hierarchical structures in smooth dynamical systems. We define transitions between levels in a dynamical hierarchy by smooth projective maps from a phase space on a lower level, with high dimensionality, to a phase space on a higher level, with lower dimensionality. It is required that each level describe a self-contained deterministic dynamical system. We show that a necessary and sufficient condition for a projective map to be a transition between levels in the hierarchy is that the kernel of the differential of the map is tangent to an invariant manifold with respect to the flow. The implications of this condition are discussed in detail. We demonstrate two different causal dependences between degrees of freedom, and how these relations are revealed when the dynamical system is transformed into global Jordan form. Finally these results are used to define functional components on different levels, interaction networks, and dynamical hierarchies.

Linear Models↗
PubMed2006

VASP governs actin dynamics by modulating filament anchoring.

Actin filament dynamics at the cell membrane are important for cell-matrix and cell-cell adhesions and the protrusion of the leading edge. Since actin filaments must be connected to the cell membrane to exert forces but must also detach from the membrane to allow it to move and evolve, the balance between actin filament tethering and detachment at adhesion sites and the leading edge is key for cell shape changes and motility. How this fine tuning is performed in cells remains an open question, but possible candidates are the Drosophila enabled/vasodilator-stimulated phosphoprotein (Ena/VASP) family of proteins, which localize to dynamic actin structures in the cell. Here we study VASP-mediated actin-related proteins 2/3 (Arp2/3) complex-dependent actin dynamics using a substrate that mimics the fluid properties of the cell membrane: an oil-water interface. We show evidence that polymerization activators undergo diffusion and convection on the fluid surface, due to continual attachment and detachment to the actin network. These dynamics are enhanced in the presence of VASP, and we observe cycles of catastrophic detachment of the actin network from the surface, resulting in stop-and-go motion. These results point to a role for VASP in the modulation of filament anchoring, with implications for actin dynamics at cell adhesions and at the leading edge of the cell.

Actin Cytoskeleton↗
PubMed1998

Artificial neural networks for drug vulnerability recognition and dynamic scenarios simulation.

Semeion researchers have developed and used different kinds of Artificial Neural Networks (ANN) in order to process selected, "standard" data coming from drug users and from people who never used drugs before. In the first step a collection of 112 general variables, not traditionally connected to drug user's behavior, were collected from a sample of 545 people (223 heroin addicted and 322 non-users). Different types of ANNs were used to test the capability of the system to classify the drug users and the non-drug users correctly. A special ANN tool, created by Semeion, was also used to prune the number of the independent variables. The ANN selected for this first experiment was a Supervised Feed Forward Network, whose equations were enhanced by Semeion researchers. For the validation of the capability of generalization of the ANN, the Training-Testing protocol was used. This ANN was able, in the Testing phase, to classify approximately 95% of the sample with accuracy. A special sensitivity tool selected only 47 among the 112 independent variables as necessary to train the ANN. In the second step, different types of ANN were tested on the new 47 variables to decide which kind of ANN was better able to classify the sample. This benchmark included the following ANNs: a) Back Propagation with Soft Max; b) Learning Vector Quantization; c) Logicon Projection; d) Radial Basis Function; e) Squash (Semeion Network); f) Fuzzy Art Map; g) Modular Neural Network. In the third step a Constraint Satisfaction Network, specifically created by Semeion, was used to simulate a dynamic fuzzy map of the drug user's world; that is, which fuzzy, or approximate, variables are critical to decide the fuzzy membership of a subject from the fuzzy membership of the drug users to the fuzzy membership of non-users and vice versa.

Artificial Intelligence↗
PubMed2006

Operational principles of neurocognitive networks.

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Animals↗
PubMed2006

Site-directed electronic tunneling through a vibrating molecular network.

The effect of electronic-nuclear coupling on electronic transport through a complex molecular network is studied. Electronic tunneling dynamics in a network of N donor/acceptor sites, connected by molecular bridges, is shown to be controlled by electronic-nuclear coupling at the bridges. Particularly, electronic coupling to an accepting nuclear mode at the contact site between the donor and the rest of the network is shown to affect the tunneling path selection to specific acceptors. In the "deep" tunneling regime, the network is mapped onto an N-level system using a recursive perturbation expansion, enabling analytical treatment of the electronic dynamics. The analytic formulation is applied for two model systems, demonstrating site-directed tunneling by electronic-nuclear coupling. Numerical simulations suggest that this phenomenon is not limited to the deep tunneling regime.

Journal Article↗
PubMed2006

Dynamics of fracture in drying suspensions.

We investigate the dynamics of fracture in drying films of colloidal silica. Water loss quenches the nanoparticle dispersions to form a liquid-saturated elastic network of particles that relieves drying-induced strain by cracking. These cracks display intriguing intermittent motion originating from the deformation of arrested crack tips and aging of the elastic network. The dynamics of a single crack exhibits a universal evolution, described by a balance of the driving elastic power with the sum of interfacial power and the viscous dissipation rate of flowing interstitial fluid.

Journal Article↗
PubMed2006

Chaotic dynamics in a class of three dimensional glass networks.

In this paper, we study chaotic dynamics of a class of three-dimensional Glass networks with different decay constants, illustrate how the horseshoe is generated, and present a rigorous computer-assisted verification of chaoticity by virtue of interval analysis and topological horseshoe theory.

Journal Article↗
PubMed2000

Adaptive information processing in microtubule networks.

Microtubule networks provide a wide range of microskeletal and micromuscular functionalities. Evidence from a number of directions suggests that they can also serve as a medium for intracellular signaling processing. The model presented here comprises an empirically motivated representation of microtubule growth dynamics, an abstract representation of signal processing, and a feedback learning mechanism that we refer to as adaptive self-stabilization. The growth model mimics the dynamic instability picture of microtubule formation and decomposition, but as modulated by the binding activity of microtubule associated proteins (or MAPs). The signal processing submodel treats each microtubule as a string of linked discrete oscillators capable of propagating signals that are introduced, manipulated, and extracted by bound MAP activity. Adaptive self-stabilization is essentially feedback acting on signal processing capabilities via the growth dynamics. The network is presented with a training set of patterns. If the input-output behavior is satisfactory MAP binding affinity increases, thereby stabilizing the network structure; otherwise the binding affinity decreases, allowing for more structural variation. The results obtained suggest that adaptive capabilities are practically inevitable in microtubule networks, a conclusion strengthened by the fact that the signal processing and growth dynamics mechanisms available in nature are undoubtedly much richer than those represented in the model.

Adaptation, Physiological↗
PubMed2006

Inferring gene regulatory networks from time series data using the minimum description length principle.

MOTIVATION: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations. RESULTS: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development. AVAILABILITY: Available from the authors upon request.

Algorithms↗
PubMed2006

Developing a realistic sexual network model of chlamydia transmission in Britain.

BACKGROUND: A national chlamydia screening programme is currently being rolled out in the UK and other countries. However, much of the epidemiology remains poorly understood. In this paper we present a stochastic, individual based, dynamic sexual network model of chlamydia transmission and its parameterisation. Mathematical models provide a theoretical framework for understanding the key epidemiological features of chlamydia: sexual behaviour, health care seeking and transmission dynamics. RESULTS: The model parameters were estimated either directly or by systematic fitting to a variety of appropriate data sources. The fitted model was representative of sexual behaviour, chlamydia epidemiology and health care use in England. We were able to recapture the observed age distribution of chlamydia prevalence. CONCLUSION: Estimating parameters for models of sexual behaviour and transmission of chlamydia is complex. Most of the parameter values are highly correlated, highly variable and there is little empirical evidence to inform estimates. We used a novel approach to estimate the rate of active treatment seeking, by combining data sources, which improved the credibility of the model results. The model structure is flexible and is broadly applicable to other developed world settings and provides a practical tool for public health decision makers.

Adolescent↗