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At least 937 records · Page 52Linked to original sources
PubMed2026

Cells and Networks in Flux: Rethinking Ontogenesis and Pathogenesis.

Organ and tissue functions emerge from the coordinated activity of cell networks. Therapeutics that act on pathogenic cell networks, modulating their cellular interplay, follow naturally. Over several decades, our laboratory has developed a series of approaches for rewiring cell networks, culminating in a class of cell surface-directed signal converter proteins (SCPs) that do so by modulating juxtacrine and autocrine signaling in and among their nodal cells. A first such SCP has now produced encouraging clinical data for cancer immunotherapy. Yet, these early network-directed fusion proteins rest on a deliberately simplified picture: discrete end-cell types plugged into graphically tractable networks. That picture is increasingly at odds with what computational cell typing and spatiotemporal analytics, along with epigenetics, now reveal-a hyperdiverse, plastic, experience-shaped cellular landscape embedded in dynamic, multiway networks. Setting the stage for a next generation of network modulators, an extended cell differentiation synthesis is proposed, which formalizes paracell ultradifferentiation and aging-associated differentiation phases. According to this model, cells are ever evolving, and no two cells are alike. A richer cellular ontology forces a more elaborate network ontology, with paralogous networks and their shifting subnetworks opening a concrete design space for next-generation network-directed SCP therapeutics. This exploration calls for a willingness to embrace complexity more fully and borrow freely from conceptual fields close and afar.

Humans↗
PubMed1996

Singular perturbation analysis of competitive neural networks with different time scales.

The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stability method and a modality of detecting the local stability behavior around individual equilibrium points.

Algorithms↗
PubMed2006

Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis.

This article demonstrates that a simple chemical model system, built by using a modular approach, may be used to predict the spatiotemporal dynamics of initiation of blood clotting in the complex network of hemostasis. Microfluidics was used to create in vitro environments that expose both the complex network and the model system to surfaces patterned with patches presenting clotting stimuli. Both systems displayed a threshold response, with clotting initiating only on isolated patches larger than a threshold size. The magnitude of the threshold patch size for both systems was described by the Damköhler number, measuring competition of reaction and diffusion. Reaction produces activators at the patch, and diffusion removes activators from the patch. The chemical model made additional predictions that were validated experimentally with human blood plasma. These experiments show that blood can be exposed to significant amounts of clot-inducing stimuli, such as tissue factor, without initiating clotting. Overall, these results demonstrate that such chemical model systems, implemented with microfluidics, may be used to predict spatiotemporal dynamics of complex biochemical networks.

Blood Coagulation↗
PubMed2005

Compositional properties of random Boolean networks.

Random Boolean networks (RBNs) are used in a number of applications, including cell differentiation, immune response, evolution, gene regulatory networks, and neural networks. This paper addresses the problem of computing attractors in RBNs. An RBN with n vertices has up to 2(n) states. Therefore, for large n , computing attractors by full enumeration of states is not feasible. The state space can be reduced by removing irrelevant vertices, which have no influence on the network's dynamics. In this paper, we show that attractors of an RBN can be computed compositionally from the attractors of the independent components of the subgraph induced by the relevant vertices of the network. The presented approach reduces the complexity of the problem from O (2(n)) to O (2(l)), where l is the number of relevant vertices in the largest component.

Journal Article↗
PubMed1994

Evolution of gene networks by gene duplications: a mathematical model and its implications on genome organization.

Networks of transcriptional regulators have key roles in metazoan development. Important forces in the evolution of these networks are gene duplications and gene deletions, events that may change the spatiotemporal expression pattern of network genes. A measure for the probability of such changes after gene-duplication events is proposed. This measure is based on a simple mathematical model that describes such networks as dynamical systems and on properties of ensembles of these dynamical systems. It is predicted that this probability depends only on the fraction of genes duplicated in a single event and that it is largest if approximately 40% of the genes in a network are duplicated. This property is robust with respect to variations in model parameters. On these grounds, it is argued that (i) evolution of gene networks should preferentially occur either by duplication of single genes or by duplication of all genes involved in a network, and that (ii) tight linkage ("clustering") or strong dispersal are the two evolutionarily most favorable forms of genomic organization of genes forming such networks.

Animals↗
PubMed2004

Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations.

The human brain spontaneously generates large-scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power-law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self-organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long-range power-law correlations is the buildup of a memory of past activity by a continuous modification of the network's functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10- and 20-Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long-range temporal correlations and power-law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power-law exponents were decreased. This implies that the stimuli indeed degraded the network's memory of its past.

Adult↗
PubMed2002

Sensory cortical dynamics.

Sensory cortical networks are commonly regarded as stable, changing only in the face of prolonged alteration of sensory input. There is increasing evidence, however, that the functional connectivity of cortical networks changes significantly, but reversibly, in response to conditions of sensory stimulation similar to those encountered in everyday life. In this review, we provide examples of sensory cortical dynamics at the single neuron and neural population levels. The dynamics detected at both levels of experimental observation suggest that a brief exposure (tens of milliseconds to tens of seconds) to sensory stimulation is accompanied by changes in the capacity of cortical networks to process and represent environmental stimuli. Candidate cellular mechanisms and the potential benefits of such stimulus-driven, rapid, and fully reversible sensory cortical dynamics are discussed.

Animals↗
PubMed2006

Epileptogenic neocortical networks are revealed by abnormal temporal dynamics in seizure-free subdural EEG.

Long-term video electroencephalographic (EEG) recording is currently a routine procedure in the presurgical evaluation of localization-related epilepsies. Cortical epileptogenic zone is usually localized from ictal recordings with intracranial electrodes, causing a significant burden to patients and health care. Growing literature suggests that epileptogenic networks exhibit aberrant dynamics also during seizure-free periods. We examined if neocortical epileptogenic regions can be circumscribed by quantifying local long-range temporal (auto-)correlations (LRTC) with detrended fluctuation analysis of seizure-free ongoing subdural EEG activity in 4 frequency bands in 5 patients. We show here with subdural EEG recordings that the LRTC are abnormally strong near the seizure onset area. This effect was most salient in neocortical oscillations in the beta frequency band (14-30 Hz). Moreover, lorazepam, a widely used antiepileptic drug, exerted contrasting effects on LRTC (n = 2): lorazepam attenuated beta-band LRTC near the epileptic focus, whereas it strengthened LRTC in other cortical areas. Our findings demonstrate that interictal neuronal network activity near the focus of seizure onset has pathologically strong intrinsic temporal correlations. The observed effect by lorazepam on beta-band activity suggests that the antiepileptic mechanism of benzodiazepines may be related to the normalization of LRTC within the epileptic focus. We propose that this method may become a promising candidate for routine invasive and noninvasive presurgical localization of epileptic foci.

Adult↗
PubMed2006

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Action Potentials↗
PubMedDate not supplied

Development of an internal dynamic web site to promote quality assurance in a clinical laboratory.

In clinical laboratories, one challenging quality assurance objective is to maintain standardized practices. Meeting this objective entails ensuring information flow, which is necessary to smooth running of the laboratory. To facilitate information flow, we developed an internal quality Web site on our local network. The dynamic generated pages of the site were constructed with EasyPHP v.1.6, a complete freeware package providing PHP dynamic language and databases. The site comprises various sections: general news, specific laboratory units news, documents (quality manual, guidelines, emergency processes), schedules, National Quality Control results, forum, etc. Five to 10 pages are updated each week. This work was facilitated by the use of PHP-written pages and data tables, which enable us to record in real time the operation of our assurance quality project and to improve traceability. This approach could be extended to other aspects of quality management and could help meet the future IS015189 standard requirements.

Computer Communication Networks↗
PubMed2001

Simultaneity of responses in a hierarchical visual network.

The pattern of anatomical connections between areas of the primate visual system is organized hierarchically. However, onset latencies in parietal and occipital stations are often simultaneous, and this seems to contradict hierarchical organization in its simplest interpretation, as serial organization. To understand the reasons for this contradiction, we simulated the cortical network for which there is onset data, including the network's hierarchical structure. The network's dynamics reproduced the simultaneous onset latencies reported in several dorsal areas. These results show that a strictly hierarchical visual system is compatible with much more complex dynamics than serial processing, and that hodological and biophysical properties, are more closely related to onset dynamics than is hierarchical organisation.

Animals↗
PubMed2006

Utilization of self-sorting processes to generate dynamic combinatorial libraries with new network topologies.

The synthesis of water-soluble, organometallic macrocycles is described. They were obtained by self-assembly in reactions of the half-sandwich complexes [[Ru(C6H5Me)Cl2]2], [[Ru(p-cymene)Cl2]2], [[Rh(Cp)Cl2]2], and [[Ir(Cp*)Cl2]2] with the ligand 5-dimethylaminomethyl-3-hydroxy-2-methyl-4-(1H)-pyridone in buffered aqueous solution at pH 8. The structure of the Ru-(p-cymene) complex was determined by single-crystal X-ray crystallography. Upon mixing, these complexes undergo scrambling reactions to give dynamic combinatorial libraries. In combination with structurally related complexes based on amino-methylated 3-hydroxy-2-(1H)-pyridone ligands, an exchange of metal fragments but no mixing of ligands was observed. This self-sorting behavior was used to construct dynamic combinatorial libraries of macrocycles, in which two four-component sub-libraries are connected by two common building blocks. This type of network topology influences the adaptive behavior of the library as demonstrated in selection experiments with lithium ions as the target.

Journal Article↗
PubMed2005

Dynamical patterns of epidemic outbreaks in complex heterogeneous networks.

We present a thorough inspection of the dynamical behavior of epidemic phenomena in populations with complex and heterogeneous connectivity patterns. We show that the growth of the epidemic prevalence is virtually instantaneous in all networks characterized by diverging degree fluctuations, independently of the structure of the connectivity correlation functions characterizing the population network. By means of analytical and numerical results, we show that the outbreak time evolution follows a precise hierarchical dynamics. Once reached the most highly connected hubs, the infection pervades the network in a progressive cascade across smaller degree classes. Finally, we show the influence of the initial conditions and the relevance of statistical results in single case studies concerning heterogeneous networks. The emerging theoretical framework appears of general interest in view of the recently observed abundance of natural networks with complex topological features and might provide useful insights for the development of adaptive strategies aimed at epidemic containment.

Communicable Disease Control↗
PubMed2006

Assessing the effects of human mixing patterns on human immunodeficiency virus-1 interhost phylogenetics through social network simulation.

Geneticists seeking to understand HIV-1 evolution among human hosts generally assume that hosts represent a panmictic population. Social science research demonstrates that the network patterns over which HIV-1 spreads are highly nonrandom, but the effect of these patterns on the genetic diversity of HIV-1 and other sexually transmitted pathogens has yet to be thoroughly examined. In addition, interhost phylogenetic models rarely account explicitly for genetic diversity arising from intrahost dynamics. This study outlines a graph-theoretic framework (exponential random graph modeling, ERGM) for the estimation, inference, and simulation of dynamic partnership networks. This approach is used to simulate HIV-1 transmission and evolution under eight mixing patterns resembling those observed in empirical human populations, while simultaneously incorporating intrahost viral diversity. Models of parametric growth fit panmictic populations well, yielding estimates of total viral effective population on the order of the product of infected host size and intrahost effective viral population size. Populations exhibiting patterns of nonrandom mixing differ more widely in estimates of effective population size they yield, however, and reconstructions of population dynamics can exhibit severe errors if panmixis is assumed. I discuss implications for HIV-1 phylogenetics and the potential for ERGM to provide a general framework for addressing these issues.

Evolution, Molecular↗
PubMed2001

Developmental changes of tau protein and mRNA in cultured rat brain oligodendrocytes.

Oligodendrocytes elaborate an extensive network of multibranched processes and flat membranous sheets. Microtubules (MT) participate in the elaboration and stabilization of myelin-forming processes and are essential for cellular sorting processes. Microtubule-associated proteins (MAPs) are involved in the regulation and stabilization of the dynamic MT network. It has been shown previously that oligodendrocytes express the MAP tau, a phosphoprotein most abundant in neurons of the CNS. In this article, we demonstrate for the first time that oligodendrocytes contain all six tau isoforms, and that tau mRNA and protein expression is developmentally regulated. Immunoblot analysis reveals that tau protein is more abundant, and mature isoforms are more prominent at later stages of development. During the first week of culture maturation, a marked decrease in phosphorylation is observable. Using an RT-PCR approach, we can show that oligodendrocytes express small amounts of exon 3 containing isoforms and that during culture maturation, tau mRNA splice products with 3 MT-binding domains (3R) decrease and mRNA with 4 MT-binding domains (4R) increase. In situ hybridization study demonstrates that tau mRNA is present in precursor cells and in mature oligodendrocytes. Tau mRNA is actively transported into the cellular processes, is specifically present in the primary and some of the secondary processes, enriched at the turning and branching points and the growing tips, and often appears as small patches. Hence, localized tau translation at specific sites in the cellular extensions might contribute to the regulation of MT stability during process formation, early axonal contact establishment, and myelination.

Aging↗
PubMed2003

Influence of system non-uniformity on dynamic phenomena in arrays of coupled nonlinear networks.

In this paper we investigate the influence of system non-uniformity on the existence and stability of synchronous motion in an array of bi-directionally coupled electronic circuits. In computer simulations we find the level of non-uniformity for which synchronous behavior is sustained. We also present several examples of attractors, which appear when the synchronous motions is no longer stable.

Neural Networks, Computer↗
PubMed2006

Dynamic output feedback stabilization for nonlinear systems based on standard neural network models.

A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.

Algorithms↗
PubMed2002

Coherent and robust modulation of a metabolic network by cytoskeletal organization and dynamics.

In order to investigate the influence of cytoskeletal organization and dynamics on cellular biochemistry, a mathematical model was formulated based on our own experimental evidence. The model couples microtubular protein (MTP) dynamics to the glycolytic pathway and its branches: the Krebs cycle, ethanolic fermentation, and the pentose phosphate (PP) pathway. Results show that the flux through glycolysis coherently and coordinately increases or decreases with increased or decreased levels of polymerized MTP, respectively. The rates of individual enzymatic steps and metabolite concentrations change with the polymeric status of MTP throughout the metabolic network. Negative control is exerted by the PP pathway on the glycolytic flux, and the extent of inhibition depends inversely on the polymerization state of MTP, i.e. a high degree of polymerization relieves the negative control. The stability of the model's steady state dynamics for a wide range of variation of metabolic parameters increased with the degree of polymerized MTP. The findings indicate that the organization of the cytoskeleton bestows coherence and robustness to the coordination of cellular metabolism.

Cytoskeleton↗