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PubMed2004

Transient responses and adaptation to steady state in a eukaryotic gene regulation system.

Understanding the structure and functionality of eukaryotic gene regulation systems is of fundamental importance in many areas of biology. While most recent studies focus on static or short-term properties, measuring the long-term dynamics of these networks under controlled conditions is necessary for their complete characterization. We demonstrate adaptive dynamics in a well-known system of metabolic regulation, the GAL system in the yeast S. cerevisiae. This is a classic model for a eukaryotic genetic switch, induced by galactose and repressed by glucose. We followed the expression of a reporter gfp under a GAL promoter at single-cell resolution in large population of yeast cells. Experiments were conducted for long time scales, several generations, while controlling the environment in continuous culture. This combination enabled us, for the first time, to distinguish between transient responses and steady state. We find that both galactose induction and glucose repression are only transient responses. Over several generations, the system converges to a single robust steady state, independent of external conditions. Thus, at steady state the GAL network loses its hallmark functionality as a sensitive carbon source rheostat. This result suggests that, while short-term dynamics are determined by specific modular responses, over long time scales inter-modular interactions take over and shape a robust steady state response of the regulatory system.

Biophysical Phenomena↗
PubMed1999

Storage capacity of non-monotonic neurons.

Non-monotonic neurons have been shown to enhance the storage capacity of auto-associative memories. This work investigates the retrieval capacities of different types of non-monotonic neurons. It is found that storage capacity is maximized when the neuron response is a function with well defined geometrical characteristics. Numerical experiments demonstrate that storage capacity is directly related to the dynamical property of the iterative map, x(i)-->g( summation operator jT(ij)x(j)), that describes the network evolution. Maximum capacity is reached when the neuron dynamics are subdivided into two non-overlapping "erratic bands" around points x(i)=+/-1. The capacity improvements are explained in terms of the energy function associated with the network dynamics.

Journal Article↗
PubMed1998

Dynamic study of the extraembryonic vascular network of the chick embryo by fractal analysis.

Fractal analysis is widely used in many scientific fields, including the study of vascularization. It is a convenient method that defines the complexity of natural structures. The chorioallantoic membrane of the chick embryo is a standard experimental model for the study of vasculogenesis and angiogenesis. The aim of this investigation was to demonstrate that fractal geometry is more appropriate than any other method to describe and analyse the evolution of a vascular network, i.e. the extraembryonar vascular network of the chick embryo. We used an original methodology to evaluate the complexity of this network in the first stages of embryo development (day 3 until 6). We demonstrated an increase of fractal dimension, indicating an increasing complexity of the vascular tree, until an asymptotical value of about 1.70 at day 4. The fractal approach is more accurate than other usual semi-quantitative or quantitative methods evaluating the complexity of a growing vascular tree.

Animals↗
PubMed2004

Metastable network model of protein transport through nuclear pores.

To reconcile the observed selectivity and the high rate of translocation of cargo-importin complexes through nuclear pores, we propose that the core of the nuclear pore complex is blocked by a metastable network of phenylalanine and glycine nucleoporins. Although the network arrests the unfacilitated passage of objects larger than its mesh size, cargo-importin complexes act as catalysts that reduce the free energy barrier between the cross-linked and the dissociated states of the Nups, and open the network. Using Brownian dynamics simulations we calculate the distribution of passage times through the network for inert particles and cargo-importin complexes of different sizes and discuss the implications of our results for experiments on translocation of proteins through the nuclear pore complex.

Active Transport, Cell Nucleus↗
PubMed2003

Phonological processing in language production: time course of brain activity.

Recent neuroimaging studies provide evidence for a shared neural network for phonological processing in language production and comprehension. The temporal dynamics in this network during comprehension has been investigated by Thierry et al., who showed a primacy for Wernicke's over Broca's area. In the present study, we demonstrate the reversed pattern for language production. These results can be interpreted with respect to the functionality of the different regions within the shared network, with Wernicke's area being the sound form store and Broca's area a processor necessary to extract relevant phonological information from that store.

Adult↗
PubMed2005

Neuronal oscillators in aplysia californica that demonstrate weak coupling in vitro.

Networks of oscillators produce vital activity in diverse natural systems. The dynamics of these networks are frequently studied via computational models that assume weak coupling, yet this assumption has not been experimentally validated. We applied weak stimuli to neuronal oscillators in Aplysia californica and deconvolved infinitesimal phase response curves (IPRCs) that describe the phase response of a neuron. We show that these IPRCs reliably predict the phase response for weak stimuli, independent of the stimulus waveform used. These weak stimuli are in the range of normal synaptic activity for these neurons, suggesting that weak coupling is a likely mechanism.

Animals↗
PubMed2005

A new dynamical layout algorithm for complex biochemical reaction networks.

BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at 1). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on 2.

Algorithms↗
PubMed2004

Long chaotic transients in complex networks.

We show that long chaotic transients dominate the dynamics of randomly diluted networks of pulse-coupled oscillators. This contrasts with the rapid convergence towards limit cycle attractors found in networks of globally coupled units. The lengths of the transients strongly depend on the network connectivity and vary by several orders of magnitude, with maximum transient lengths at intermediate connectivities. The dynamics of the transients exhibit a novel form of robust synchronization. An approximation to the largest Lyapunov exponent characterizing the chaotic nature of the transient dynamics is calculated analytically.

Models, Biological↗
PubMed2004

The evolution of molecular genetic pathways and networks.

There is growing interest in the evolutionary dynamics of molecular genetic pathways and networks, and the extent to which the molecular evolution of a gene depends on its position within a pathway or network, as well as over-all network topology. Investigations on the relationships between network organization, topological architecture and evolutionary dynamics provide intriguing hints as to how networks evolve. Recent studies also suggest that genetic pathway and network structures may influence the action of evolutionary forces, and may play a role in maintaining phenotypic robustness in organisms.

Animals↗
PubMed2006

Reverse engineering the gap gene network of Drosophila melanogaster.

A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development.

Animals↗
PubMedDate not supplied

A self-organising network that grows when required.

The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input space more accurately, and often more parsimoniously, than a network with predefined structure and size, such as the Self-Organising Map. In addition, a growing network can deal with dynamic input distributions. Most of the growing networks that have been proposed in the literature add new nodes to support the node that has accumulated the highest error during previous iterations or to support topological structures. This usually means that new nodes are added only when the number of iterations is an integer multiple of some pre-defined constant, A. This paper suggests a way in which the learning algorithm can add nodes whenever the network in its current state does not sufficiently match the input. In this way the network grows very quickly when new data is presented, but stops growing once the network has matched the data. This is particularly important when we consider dynamic data sets, where the distribution of inputs can change to a new regime after some time. We also demonstrate the preservation of neighbourhood relations in the data by the network. The new network is compared to an existing growing network, the Growing Neural Gas (GNG), on a artificial dataset, showing how the network deals with a change in input distribution after some time. Finally, the new network is applied to several novelty detection tasks and is compared with both the GNG and an unsupervised form of the Reduced Coulomb Energy network on a robotic inspection task and with a Support Vector Machine on two benchmark novelty detection tasks.

Algorithms↗
PubMed2003

Electrical synapses and synchrony: the role of intrinsic currents.

Electrical synapses are ubiquitous in the mammalian CNS. Particularly in the neocortex, electrical synapses have been shown to connect low-threshold spiking (LTS) as well as fast spiking (FS) interneurons. Experiments have highlighted the roles of electrical synapses in the dynamics of neuronal networks. Here we investigate theoretically how intrinsic cell properties affect the synchronization of neurons interacting by electrical synapses. Numerical simulations of a network of conductance-based neurons randomly connected with electrical synapses show that potassium currents promote synchrony, whereas the persistent sodium current impedes it. Furthermore, synchrony varies with the firing rate in qualitatively different ways depending on the intrinsic currents. We also study analytically a network of quadratic integrate-and-fire neurons. We relate the stability of the asynchronous state of this network to the phase-response function (PRF), which characterizes the effect of small perturbations on the firing timing of the neurons. In particular, we show that the greater the skew of the PRF toward the first half of the period, the more stable the asynchronous state. Combining our simulations with our analytical results, we establish general rules to predict the dynamic state of large networks of neurons coupled with electrical synapses. Our work provides a natural explanation for surprising experimental observations that blocking electrical synapses may increase the synchrony of neuronal activity. It also suggests different synchronization properties for LTS and FS cells. Finally, we propose to further test our predictions in experiments using dynamic clamp techniques.

Computer Simulation↗
PubMed2006

Systems biology approaches to metabolic and cardiovascular disorders: network perspectives of cardiovascular metabolism.

In this review, we examine cardiovascular metabolism from three different, but highly complementary, perspectives. First, from the abstract perspective of a metabolite network, composed of nodes and links. We present fundamental concepts in network theory, including emergence, to illustrate how nature has designed metabolism with a hierarchal modular scale-free topology to provide a robust system of energy delivery. Second, from the physical perspective of a modular spatially compartmentalized network. We review evidence that cardiovascular metabolism is functionally compartmentalized, such that oxidative phosphorylation, glycolysis, and glycogenolysis preferentially channel ATP to ATPases in different cellular compartments, using creatine kinase and adenylate kinase to maximize efficient energy delivery. Third, from the dynamics perspective, as a network of dynamically interactive metabolic modules capable of self-oscillation. Whereas normally, cardiac metabolism exists in a regime in which excitation-metabolism coupling closely matches energy supply and demand, we describe how under stressful conditions, the network can be pushed into a qualitatively new dynamic regime, manifested as cell-wide oscillations in ATP levels, in which the coordination between energy supply and demand is lost. We speculate how this state of "metabolic fibrillation" leads to cell death if not corrected and discuss the implications for cardioprotection.

Animals↗
PubMed2004

Theory of networked minority games based on strategy pattern dynamics.

We formulate a theory of agent-based models in which agents compete to be in a winning group. The agents may be part of a network or not, and the winning group may be a minority group or not. An important feature of the present formalism is its focus on the dynamical pattern of strategy rankings, and its careful treatment of the strategy ties which arise during the system's temporal evolution. We apply it to the minority game with connected populations. Expressions for the mean success rate among the agents and for the mean success rate for agents with k neighbors are derived. We also use the theory to estimate the value of connectivity p above which the binary-agent-resource system with high resource levels makes the transition into the high-connectivity state.

Adaptation, Physiological↗
PubMed2001

A neural network approach for predicting and modelling the dynamical behaviour of cardiac ventricular repolarisation.

Physiological signals are usually patient specific, and they are difficult to predict, especially for the cardiovascular system. New methods capable to be adapted to each case and to learn the singular behavior of heart functions should be developed to support physicians in their decision-making. One of the most widely studied relations is the QT-RR one, between the total duration of the ventricle activation and inactivation, and the heart rate. In the past, different studies were made to approach this relation in the steady state. In this paper, a new method for modeling and predicting the transient dynamic behaviour of QT interval in relation to changing RR intervals is presented using artificial neural networks.

Action Potentials↗
PubMed1996

Refining neural network predictions for helical transmembrane proteins by dynamic programming.

For transmembrane proteins experimental determination of three-dimensional structure is problematic. However, membrane proteins have important impact for molecular biology in general, and for drug design in particular. Thus, prediction method are needed. Here we introduce a method that started from the output of the profile-based neural network system PHDhtm (Rost, et al. 1995). Instead of choosing the neural network output unit with maximal value as prediction, we implemented a dynamic programming-like refinement procedure that aimed at producing the best model for all transmembrane helices compatible with the neural network output. The refined prediction was used successfully to predict transmembrane topology based on an empirical rule for the charge difference between extra- and intra-cytoplasmic regions (positive-inside rule). Preliminary results suggest that the refinement was clearly superior to the initial neural network system; and that the method predicted all transmembrane helices correctly for more proteins than a previously applied empirical filter. The resulting accuracy in predicting topology was better than 80%. Although a more thorough evaluation of the method on a larger data set will be required, the results compared favourably with alternative methods. The results reflected the strength of the refinement procedure which was the successful incorporation of global information: whereas the residue preferences output by the neural network were derived from stretches of 17 adjacent residues, the refinement procedure involved constraints on the level of the entire protein.

Algorithms↗
PubMed2004

Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters.

RATIONALE AND OBJECTIVE: An artificial neural network (ANN)-based segmentation method was developed for dynamic contrast-enhanced magnetic resonance (MR) imaging of the breast and compared with quantitative and empiric parameter mapping techniques. MATERIALS AND METHODS: The study population was composed of 10 patients with seven malignant and three benign lesions undergoing dynamic MR imaging of the breast. All lesions were biopsied or surgically excised, and examined by means of histopathology. A T1-weighted 3D FLASH (fast low angle shot sequence) was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.1 mmol/kg body weight. Motion artifacts on MR images were eliminated by voxel-based affine and nonrigid registration techniques. A two-layered feed-forward back-propagation network was created for pixel-by-pixel classification of signal intensity-time curves into benign/malignant tissue types. ANN output was statistically compared with percent-enhancement (E), signal enhancement ratio (SER), time-to-peak, subtracted signal intensity (SUB), pharmacokinetic parameter rate constant (k(ep)), and correlation coefficient to a predefined reference washout curve. RESULTS: ANN was successfully applied to the classification of breast MR images identifying structures with benign or malignant enhancement kinetics. Correlation coefficient (logistic regression, odds ratio [OR] = 12.9; 95% CI: 7.7-21.8), k(ep) (OR = 1.8; 95% CI: 1.2-2.6), and time-to-peak (OR = 0.45; 95% CI: 0.3-0.7) were independently associated to ANN output classes. SER, E, and SUB were nonsignificant covariates. CONCLUSION: ANN is capable of classifying breast lesions on MR images. Mapping correlation coefficient, k(ep) and time-to-peak showed the highest association with the ANN result.

Adult↗
PubMed2006

Synchronizabilities of networks: a new index.

The random matrix theory is used to bridge the network structures and the dynamical processes defined on them. We propose a possible dynamical mechanism for the enhancement effect of network structures on synchronization processes, based upon which a dynamic-based index of the synchronizability is introduced in the present paper.

Action Potentials↗