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PubMed2026

Integrative computational analysis combining network pharmacology, regulatory network modeling, and molecular dynamics reveals the mechanisms of Quanshen compound in ITP.

UNLABELLED: Immune thrombocytopenia (ITP) is a hemorrhagic disorder caused by immune dysfunction. Quanshen Compound (QSC) is an in-house preparation developed by the Uyghur Hospital in Hotan Prefecture. This study primarily investigates and validates the potential pharmacological basis and mechanism of action of QSC in modulating immune thrombopoiesis. Based on the multi-database screening of the QSC and the related targets of ITP, the intersection was obtained to construct a protein-protein interaction (PPI) network and screen the core targets; the intersection targets were analyzed for gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using R packages; a component-target-pathway network was constructed to screen the key active components and their mechanisms of action. At the same time, the TF-mRNA-miRNA regulatory network of the core targets was constructed, and chromosome localization and subcellular localization analysis were performed; further, the binding stability of key components and core targets was verified through molecular docking and molecular dynamics simulation. A total of 227 potential target sites were screened out, among which TNF, IL6, AKT1, TP53 and IL1B were the core targets. The enrichment results indicated that these intersecting target sites mainly participated in inflammatory responses, immune regulation and hemostasis-related biological processes, and were significantly enriched in the PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation and PD-1/PD-L1 signaling pathway. The core target TF-mRNA-miRNA regulatory network contained 184 nodes and 200 edges, suggesting that the core targets were subject to multi-level regulation. Molecular docking results showed that the main active components had good binding activity with the core targets, and molecular dynamics simulation further verified the stability of the complex. QSC may improve ITP through a multi-component, multi-target, and multi-pathway synergistic mechanism involving key targets such as TNF, IL6, AKT1, TP53, and IL1B, as well as the PI3K-Akt signaling pathway. These findings provide new insights into the potential therapeutic mechanisms of QSC against ITP and warrant further experimental validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00718-0.

Immune thrombocytopenia↗
PubMed2006

A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics.

BACKGROUND: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters. RESULTS: In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC) simulations. CONCLUSION: The parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only. Addition of steady state perturbation of enzyme activities solved this problem.

Algorithms↗
PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗
PubMed2002

Dynamics of rumor propagation on small-world networks.

We study the dynamics of an epidemiclike model for the spread of a rumor on a small-world network. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of the network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the two regimes. The variant of dynamic small worlds, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals, is also analyzed in detail and compared with a mean-field approximation.

Journal Article↗
PubMed2003

Background synaptic activity as a switch between dynamical states in a network.

A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.

Action Potentials↗
PubMed2005

Splitting the dynamics of large biochemical interaction networks.

This article is inscribed in the general motivation of understanding the dynamics on biochemical networks including metabolic and genetic interactions. Our approach is continuous modeling by differential equations. We address the problem of the huge size of those systems. We present a mathematical tool for reducing the size of the model, master-slave synchronization, and fit it to the biochemical context.

Algorithms↗
PubMed1994

Dynamics of local neuronal networks: control parameters and state bifurcations in epileptogenesis.

The aim of this overview is to present evidence that local neuronal networks (LNNs) are functionally organized in such a way that they behave as dynamic non-linear systems that can exhibit multiple types of attractor and can present bifurcations between different attractors, depending on control parameters. To begin with, some of the theoretical concepts of non-linear dynamics and chaos are briefly presented. As a case study, we described the CA1 area of the hippocampus and the changes that the corresponding LNNs undergo during kindling epileptogenesis. During epileptic seizures, evidence exists for the presence of low-dimensional chaos, since the correlation dimension estimated from the corresponding EEG signals decreases dramatically from a large value, characteristic of the resting state, to a low value typical of deterministic chaos. We propose that, among other things, an important control parameter of the dynamics of this brain area is the balance between excitatory (E) and inhibitory (I) processes. We assume that this balance can be experimentally estimated by using a paired-pulse paradigm. Accordingly, we demonstrate that the paired-pulse response changes during kindling epileptogenesis in the sense that the E/I ratio increases in the course of the establishment of a kindled epileptogenic focus. This change in E/I leads to a shift in the operating point of the LNN moving it close to a bifurcation where a rapid state change takes place. In this way, the LNN dynamics can change more readily to the basin of attraction of a chaotic attractor than under normal conditions. This is in essence what makes the behavior of the LNN more sensitive to tetanus, and predicts the facilitated occurrence of epileptic seizures during kindling.

Animals↗
PubMed2005

Dynamic pattern evolution on scale-free networks.

A general class of dynamic models on scale-free networks is studied by analytical methods and computer simulations. Each network consists of N vertices and is characterized by its degree distribution, P(k), which represents the probability that a randomly chosen vertex is connected to k nearest neighbors. Each vertex can attain two internal states described by binary variables or Ising-like spins that evolve in time according to local majority rules. Scale-free networks, for which the degree distribution has a power law tail P(k) approximately k(-gamma), are shown to exhibit qualitatively different dynamic behavior for gamma < 5/2 and gamma > 5/2, shedding light on the empirical observation that many real-world networks are scale-free with 2 < gamma < 5/2. For 2 < gamma < 5/2, strongly disordered patterns decay within a finite decay time even in the limit of infinite networks. For gamma > 5/2, on the other hand, this decay time diverges as ln(N) with the network size N. An analogous distinction is found for a variety of more complex models including Hopfield models for associative memory networks. In the latter case, the storage capacity is found, within mean field theory, to be independent of N in the limit of large N for gamma > 5/2 but to grow as N(alpha) with alpha = (5 - 2gamma)/(gamma - 1) for 2 < gamma < 5/2.

Journal Article↗
PubMed2006

Stochastic dynamics of macromolecular-assembly networks.

The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current computational methods strongly limited for understanding how the physical interactions between cellular components give rise to systemic properties of cells. Here, we present a stochastic approach to study the dynamics of networks formed by macromolecular complexes in terms of the molecular interactions of their components. Exploiting key thermodynamic concepts, this approach makes it possible to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular networks. As prototype systems, we consider the lac operon and phage lambda induction switches, which rely on the formation of DNA loops by proteins and on the integration of these protein-DNA complexes into intracellular networks. This cross-scale approach offers an effective starting point to move forward from network diagrams, such as those of protein-protein and DNA-protein interaction networks, to the actual dynamics of cellular processes.

Computational Biology↗
PubMed2005

Transcriptional regulation and metabolism.

Understanding organisms from a systems perspective is essential for predicting cellular behaviour as well as designing gene-metabolic circuits for novel functions. The structure, dynamics and interactions of cellular networks are all vital components of systems biology. To facilitate investigation of these aspects, we have developed an integrative technique called network component analysis, which utilizes mRNA expression and transcriptional network connectivity to determine network component dynamics, functions and interactions. This approach has been applied to elucidate transcription factor dynamics in Saccharomyces cerevisiae cell-cycle regulation, detect cross-talks in Escherichia coli two-component signalling pathways, and characterize E. coli carbon source transition. An ultimate test of system-wide understanding is the ability to design and construct novel gene-metabolic circuits. To this end, artificial feedback regulation, cell-cell communication and oscillatory circuits have been constructed, which demonstrate the design principles of gene-metabolic regulation in the cell.

Escherichia coli↗
PubMed2006

Empirical analysis of an evolving social network.

Social networks evolve over time, driven by the shared activities and affiliations of their members, by similarity of individuals' attributes, and by the closure of short network cycles. We analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes. We found that network evolution is dominated by a combination of effects arising from network topology itself and the organizational structure in which the network is embedded. In the absence of global perturbations, average network properties appear to approach an equilibrium state, whereas individual properties are unstable.

Electronic Mail↗
PubMed2004

Virtual and dynamic hierarchical architecture: an overlay network topology for discovering grid services with high performance.

This paper presents an overlay network topology called Virtual and Dynamic Hierarchical Architecture (VDHA) for discovering Grid services with high performance. Service discovery based on VDHA have scalable, autonomous, efficient, reliable and quick responsive. We propose two service discovery algorithms. Full Search Query and Discovery Protocol (FSQDP) discovers the nodes that match the request message from all N nodes, which has time complexity O(logN), space complexity O(nvg) (nvg being node numbers of each virtual group), and message-cost O(N), and Domain-Specific Query and Discovery Protocol (DSQDP) searches nodes in only specific domains with time complexity O(nvg), space complexity O(nvg), and message-cost O(nvg). In this paper, we also describe VDHA, its formal definition, and Grid Group Management Protocol.

Journal Article↗
PubMed2004

A network of coincidence detector neurons with periodic and chaotic dynamics.

We propose a simple neural network model to understand the dynamics of temporal pulse coding. The model is composed of coincidence-detector neurons with uniform synaptic efficacies and random pulse propagation delays. We also assume a global negative feedback mechanism which controls the network activity, leading to a fixed number of neurons firing within a certain time window. Due to this constraint, the network state becomes well defined and the dynamics equivalent to a piecewise nonlinear map. Numerical simulations of the model indicate that the latency of neuronal firing is crucial to the global network dynamics; when the timing of postsynaptic firing is less sensitive to perturbations in timing of presynaptic spikes, the network dynamics become stable and periodic, whereas increased sensitivity leads to instability and chaotic dynamics. Furthermore, we introduce a learning rule which decreases the Lyapunov exponent of an attractor and enlarges the basin of attraction.

Action Potentials↗
PubMed1998

Retrieval dynamics in oscillator neural networks.

We present an analytical approach that allows us to treat the long-time behavior of the recalling process in an oscillator neural network. It is well known that in coupled oscillatory neuronal systems, under suitable conditions, the original dynamics can be reduced to a simpler phase dynamics. In this description, the phases of the oscillators can be regarded as the timings of the neuronal spikes. To attempt an analytical treatment of the recalling dynamics of such a system, we study a simplified model in which we discretize time and assume a synchronous updating rule. The theoretical results show that the retrieval dynamics is described by recursion equations for some macroscopic parameters, such as an overlap with the retrieval pattern. We then treat the noise components in the local field, which arise from the learning of the unretrieved patterns, as gaussian variables. However, we take account of the temporal correlation between these noise components at different times. In particular, we find that this correlation is essential for correctly predicting the behavior of the retrieval process in the case of autoassociative memory. From the derived equations, the maximal storage capacity and the basin of attraction are calculated and graphically displayed. We also consider the more general case that the network retrieves an ordered sequence of phase patterns. In both cases, the basin of attraction remains sufficiently wide to recall the memorized pattern from a noisy one, even near saturation. The validity of these theoretical results is supported by numerical simulations. We believe that this model serves as a convenient starting point for the theoretical study of retrieval dynamics in general oscillatory systems.

Artifacts↗
PubMed2005

Generating structured networks based on a weight-dependent deactivation mechanism.

Motivated by the degree-dependent deactivation model generating networks with high clustering coefficient [K. Klemm, Phys. Rev. E. 65, 036123 (2002)], a weight-dependent version is studied to model evolving networks. The growth dynamics of the network is based on a naive weight-driven deactivation mechanism which couples the establishment of new active vertices and the weights' dynamical evolution. Both analytical solutions and numerical simulations show that the generated networks possess a high clustering coefficient larger than that for regular lattices of the same average connectivity. Weighted, structured scale-free networks are obtained as the deactivated vertex is target selected at each time step, and weighted, structured exponential networks are realized for the random-selected case.

Journal Article↗
PubMedDate not supplied

A discrete map for the dynamics of recurrent excitatory neural networks in the presence of noise.

We investigate the effect of the neuron characteristics on the behavior of a recurrent excitatory neural network model. First, we present the different types of dynamics obtained with simulations of a network of coupled excitatory spike-response neuron models placed under the influence of noise. Then, we derive a discrete map describing the dynamics of large fully connected networks. By studying the bifurcation structure of this map, we can determine for which ranges of the neuron model parameters the network will display collective oscillations or other types of dynamics.

Action Potentials↗
PubMed2003

Introducing small-world network effects to critical dynamics.

We analytically investigate the kinetic Gaussian model and the one-dimensional kinetic Ising model of two typical small-world networks (SWN), the adding type and the rewiring type. The general approaches and some basic equations are systematically formulated. The rigorous investigation of the Glauber-type kinetic Gaussian model shows the mean-field-like global influence on the dynamic evolution of the individual spins. Accordingly a simplified method is presented and tested, which is believed to be a good choice for the mean-field transition widely (in fact, without exception so far) observed for SWN. It yields the evolving equation of the Kawasaki-type Gaussian model. In the one-dimensional Ising model, the p dependence of the critical point is analytically obtained and the nonexistence of such a threshold p(c), for a finite-temperature transition, is confirmed. The static critical exponents gamma and beta are in accordance with the results of the recent Monte Carlo simulations, and also with the mean-field critical behavior of the system. We also prove that the SWN effect does not change the dynamic critical exponent z=2 for this model. The observed influence of the long-range randomness on the critical point indicates two obviously different hidden mechanisms.

Journal Article↗
PubMed2005

Structural and dynamical analyses of the kinase network derived from the transpath database.

We analyze the structural design and the dynamical properties of a protein kinase network derived from the Transpath database. We consider structural properties, such as feedback cycles, pathway lengths, fraction of shortest pathways and crosstalk. Dynamic characteristics of the network are analyzed by using nonlinear differential equations with a special focus on kinase amplitudes and signal propagation times. Comparison with random networks shows that the cellular kinase network exhibits special features which might be a result of natural selection. In particular, the Transpath network contains no cycles, and input kinases and output kinases are generally connected by shortest signalling routes. Moreover, it displays a characteristic spectrum of cross-talk between different pathways.

Algorithms↗