Computational Network Model Research Articles

Systemic risk in banking, fire sales, and macroeconomic disasters

We develop a dynamic computational network model of the banking system where fire sales provide the amplification mechanism of financial shocks. Each period a finite number of banks offers a large, but finite, number of loans to households. Banks with excess liquidity also offer loans to other banks with insufficient liquidity. Thus, each period an interbank loan market is endogenously formed. Bank assets are hit by idiosyncratic shocks drawn from a thin tailed distribution. The uneven distribution of shocks across banks implies that each period there are banks that become insolvent. If insolvent banks happen also to be heavily indebted to other banks, their liquidation can trigger other bank failures. We find that the distribution across time of the growth rate of banking assets has a ‘fat left tail’ that corresponds to rare economic disasters. We also find that the distribution of initial shocks is not a perfect predictor of economic activity; that is some of the uncertainty is endogenous and related to the structure of the interbank network.

Multi-Omics Analysis Identified Drug Repurposing Targets for Chronic Obstructive Pulmonary Disease.

Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified that the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.

Neuromodulation effect of temporal interference stimulation based on network computational model.

Deep brain stimulation (DBS) has long been the conventional method for targeting deep brain structures, but noninvasive alternatives like transcranial Temporal Interference Stimulation (tTIS) are gaining traction. Research has shown that alternating current influences brain oscillations through neural modulation. Understanding how neurons respond to the stimulus envelope, particularly considering tTIS's high-frequency carrier, is vital for elucidating its mechanism of neuronal engagement. This study aims to explore the focal effects of tTIS across varying amplitudes and modulation depths in different brain regions. An excitatory-inhibitory network using the Izhikevich neuron model was employed to investigate responses to tTIS and compare them with transcranial Alternating Current Stimulation (tACS). We utilized a multi-scale model that integrates brain tissue modeling and network computational modeling to gain insights into the neuromodulatory effects of tTIS on the human brain. By analyzing the parametric space, we delved into phase, amplitude, and frequency entrainment to elucidate how tTIS modulates endogenous alpha oscillations. Our findings highlight a significant difference in current intensity requirements between tTIS and tACS, with tTIS requiring notably higher intensity. We observed distinct network entrainment patterns, primarily due to tTIS's high-frequency component, whereas tACS exhibited harmonic entrainment that tTIS lacked. Spatial resolution analysis of tTIS, conducted via computational modeling and brain field distribution at a 13 Hz stimulation frequency, revealed modulation in deep brain areas, with minimal effects on the surface. Notably, we observed increased power within intrinsic and stimulation bands beneath the electrodes, attributed to the high stimulus signal amplitude. Additionally, Phase Locking Value (PLV) showed slight increments in non-deep areas. Our analysis indicates focal stimulation using tTIS, prompting further investigation into the necessity of high amplitudes to significantly affect deep brain regions, which warrants validation through clinical experiments.

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters the challenge of energy constraints. In this paper, we investigate the energy efficiency (EE) optimization problem in heterogeneous computing-assisted non-orthogonal multiple access mobile edge computing (NOMA-MEC) network model. Edge devices are configured with CPU-NPU heterogeneous computing processors to improve the AI task processing, and NOMA is applied to edge task offloading to enable massive connectivity. The system-centric energy efficiency maximization problem is studied. We formulate a system-centric energy efficiency maximization problem. To solve this problem, we decouple the original problem into two subproblems, i.e., the optimization problems of heterogeneous computing workload splitting and task offloading. A heuristic algorithm and binary search algorithm are proposed to optimize NOMA user pairing and task offloading delay, respectively. Furthermore, we propose a multi-objective alternative iterative algorithm to optimize system energy efficiency. Numerical results show that the proposed scheme can significantly improve the energy efficiency of heterogeneous computing-assisted NOMA-MEC networks compared to the existing baselines.

Plasticity in inhibitory networks improves pattern separation in early olfactory processing.

By combining computational modeling, machine learning, and analysis of calcium imaging data, we demonstrate that associative and non-associative plasticity in the honeybee antennal lobe (AL) - first relay of the insect olfactory system - work together to enhance the contrast between rewarded and unrewarded odors. Training the AL's inhibitory network within specific odor environments enables the suppression of neural responses to common odor components, while amplifying responses to distinctive ones. This study sheds light on the olfactory system's ability to adapt and efficiently learn new odor-reward associations across varying environments, and it proposes innovative, energy-efficient principles applicable to artificial intelligence.

Infiltration and permeability of porous ceramics simulated by random networks

A network computational model for a 3D ceramic structure is developed. The model is applied to study the impact of geometric and material parameters of structure on the liquid metal flow through random porous ceramic medium in pressure infiltration processes. The characteristic geometric features of the ceramic structure favorable for liquid metal flow during the infiltration process are determined. The method of structural approximation and constructive homogenization are applied, and the discrete stationary Stokes equations on random graphs are considered. This approach gives a robust algorithm to determine the macroscopic permeability K of interpenetrating phases. The dependencies of K on the distribution of connections (windows) between the cells (inclusions) are derived. The numerical simulations demonstrate that the permeability K does not depend on the scaled distribution sizes of windows. This implies that K is proportional to the mean value of the window areas. The considered model takes into account a random complex structure of 3D ceramic. Hence, it complements the previous study on the local transport properties of tubes (windows) connecting the cells.

Game theory-based optimization for efficient IoT task offloading in 6G network base stations

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

A constrained constructive optimization model of branching arteriolar networks in rat skeletal muscle.

Blood flow regulation within the microvasculature reflects a complex interaction of regulatory mechanisms and varies spatially and temporally according to conditions such as metabolism, growth, injury, and disease. Understanding the role of microvascular flow distributions across conditions is of interest to investigators spanning multiple disciplines; however, data collection within networks can be labor-intensive and challenging due to limited resolution. To overcome these experimental challenges, computational network models that can accurately simulate vascular behavior are highly beneficial. Constrained constructive optimization (CCO) is a commonly used algorithm for vascular simulation, particularly well known for its adaptability toward vascular modeling across tissues. The present work demonstrates an implementation of CCO aimed to simulate a branching arteriolar microvasculature in healthy skeletal muscle, validated against literature including comprehensive rat gluteus maximus vasculature datasets, and reviews a list of user-specified adjustable model parameters to understand how their variability affects the simulated networks. Network geometric properties, including mean element diameters, lengths, and numbers of bifurcations per order, Horton's law ratios, and fractal dimension, demonstrate good validation once model parameters are adjusted to experimental data. This model successfully demonstrates hemodynamic properties such as Murray's law and the network Fahraeus effect. Application of centrifugal and Strahler ordering schemes results in divergent descriptions of identical simulated networks. This work introduces a novel CCO-based model focused on generating branching skeletal muscle microvascular arteriolar networks based on adjustable model parameters, thus making it a valuable tool for investigations into skeletal muscle microvascular structure and tissue perfusion.NEW & NOTEWORTHY The present work introduces a CCO-based algorithm for generating branching arteriolar networks, with adjustable model parameters to enable modeling in varying skeletal muscle tissues. The geometric and hemodynamic parameters of the generated networks have been comprehensively validated using experimental data collected previously in-house and from literature. This is one of few validated CCO-based models to specialize in skeletal muscle microvasculature and acts as a beneficial tool for investigating the microvasculature for hypothesis testing and validation.

The Hourly Peak Coefficient of Single-Family and Multi-Family Buildings in Poland: Support for the Selection of Water Meters and the Construction of a Water Distribution System Model

Taking care of water resources and minimizing water losses in water supply networks requires a broad approach to identifying and neutralizing operational problems. The correct selection of water meters to minimize apparent losses requires knowledge of the characteristic flows that may occur in the facility to which water is supplied. The research aimed to develop tools in the form of mathematical models and water consumption curves along with hourly water consumption coefficients to facilitate the process of selecting water meters for engineers and creating computer models of water supply systems. The research involved monitoring the flow of 76 single-family and multi-family buildings in four towns in Poland, followed by data analysis and development of tools supporting the selection of water meters and the construction of computer models of water distribution networks. High correlation coefficients of the studied variables indicate the results’ usefulness. Four models were developed to determine the maximum flow values in multi-family buildings (three models) and single-family buildings (one model) in the range of water meter diameters DN15-DN40. Characteristics of the average hourly peak coefficient (HPC) values were also developed, along with the range of changes in HPC values for single-family and multi-family buildings.

Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Objective. Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach. We developed a computational neuronal network model of two-compartment pyramidal neurons (PY) and inhibitory interneurons, which mimic the local cortical circuits. We modeled tACS with electric field strengths that are achievable in human applications. We then simulated intrinsic network activity and measured neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. Main results. The intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV mm−1), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV mm−1), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that the entrainment of ongoing cortical oscillations also depends on stimulation frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS-induced entrainment via synaptic coupling and network effects. Our model shows that PY are directly entrained by the exogenous electric field and drive the inhibitory neurons. Significance. The results presented in this study provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameter selection for tACS in cognitive studies and clinical applications.

Structural optimization of new media communication culture industry on the impact of digital innovation on marine economic growth

This study investigates the impact of digital innovation on the growth of the marine economy by optimizing the structure of the new media communication culture industry. Through the simulation of the particle swarm optimization (PSO) and Back Propagation (BP) computational network model, the Gross Domestic Production (GDP) of the marine economic zone is predicted, and the prediction effect of the PSO-BP computational network model on multivariate spatiotemporal transitions is further verified. The results show that the PSO-BP model has less prediction variance and a better fit than the BP model. The analysis of the joint prediction model reveals that there are differences in the publicity effects of different self-media modes in various types of sea areas. In terms of the economic peak of the overall data, Bilibili has the best publicity effect, followed by WeChat, Weibo, and Shake. The results show that the trend of macroeconomic indicators and the size of values in different regions affect the prediction effect. Region A has a higher annual average rate of change of macroeconomic indicators and has a better prediction effect. Region B has a faster macroeconomic development and has a more fluctuating prediction effect. Regions C and D have their advantages and disadvantages of the prediction effect under the self-media model.

Hierarchy and war

AbstractScholars have written extensively about hierarchical international order, on the one hand, and war on the other, but surprisingly little work systematically explores the connection between the two. This disconnect is all the more striking given that empirical studies have found a strong relationship between the two. We provide a generative computational network model that explains hierarchy and war as two elements of a larger recursive process: The threat of war drives the formation of hierarchy, which in turn shapes states' incentives for war. Grounded in canonical theories of hierarchy and war, the model explains an array of known regularities about hierarchical order and conflict. Surprisingly, we also find that many traditional results of the international relations literature—including institutional persistence, balancing behavior, and systemic self‐regulation—emerge from the interplay between hierarchy and war.

A Spatial Artificial Chemistry Implementation of a Gene Regulatory Network Aimed at Generating Protein Concentration Dynamics.

Gene regulatory networks are networks of interactions in organisms responsible for determining the production levels of proteins and peptides. Mathematical and computational models of gene regulatory networks have been proposed, some of them rather abstract and called artificial regulatory networks. In this contribution, a spatial model for gene regulatory networks is proposed that is biologically more realistic and incorporates an artificial chemistry to realize the interaction between regulatory proteins called the transcription factors and the regulatory sites of simulated genes. The result is a system that is quite robust while able to produce complex dynamics similar to what can be observed in nature. Here an analysis of the impact of the initial states of the system on the produced dynamics is performed, showing that such models are evolvable and can be directed toward producing desired protein dynamics.

Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer's disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort.

DEEP REINFORCEMENT LEARNING FOR SCALABLE CONTROL OF BOOLEAN MODELS IN THE CONTEXT OF CELLULAR REPROGRAMMING

Cellular reprogramming, understood as the artificial changing of one cell type into another, could potentially be used for the prevention or cure of complex diseases, such as cancer or neurodegenerative disorders. However, the identification of efficient reprogramming targets and strategies with classical wet-lab experiments is hindered by lengthy time commitments and high costs. To address these issues, in-silico methods are considered. In this study, we formulate a novel control problem in the context of cellular reprogramming for the Boolean network (BN) and Probabilistic Boolean Network (PBN) computational models of gene regulatory networks [1]. To solve this problem, we develop pbn-STAC - a novel computational framework based on deep reinforcement learning (DRL) [2] that facilitates the identification of reprogramming strategies for large BN/PBN models. To address the issue of the computational complexity of finding stable states, i.e., configurations which correspond to cell types or cellular phenotypic states, we introduce the notion of a pseudo-attractor and we devise a procedure for the identification of pseudo-attractor states during DRL training. Given source and target (pseudo-)attractor states, pbn-STAC finds proper control targets and strategies that drive the network from the source to the target by intervening only in intermediate attractor states that correspond to phenotypic cellular states observable in wet-lab practice. We evaluate the performance of pbn-STAC on a number of models of various sizes and a biological case study, i.e., a published BN model of immune response against B. bronchiseptica infection. We compare the results obtained with our approach with the exact, optimal solutions wherever possible and we show the effectiveness of PBN-STAC in identifying control targets and strategies. We consider our framework as a contribution towards the ultimate aim of developing scalable control methods for large models of gene regulatory networks.

Modeling of ergodic processes in dynamic graphs of computer networks

Summary: For a mathematical model of mobile wireless computer networks, the random process of alternating network connectivity and disconnection was studied using several mobility models: isotropic random walk with reflection from the boundaries of the network location area, partially directed random walk, random walk with elements of vortex motion. The probability distribution functions of the durations of periods of network connectivity and disconnection are found, the ergodicity of the process and the uncorrelated nature of the period durations are shown. A large positive asymmetry of the distributions was noted, indicating the presence of a relatively small number of periods of long duration in the process. The analysis was carried out using simulation methods. The results of the analysis are presented as dependency plots.

Exploring Therapeutic Potential of Nutraceutical Compounds from Propolis on MAPK1 Protein Using Bioinformatics Approaches as Anti-Coronavirus Disease 2019 (COVID-19)

This study explores the potential of propolis, a natural substance, as a gene therapy for treating COVID-19. Despite the advent of COVID-19 vaccines, their side effects pose new health challenges. Utilizing network pharmacology, this research identifies propolis compounds through various databases and assesses their ability to target proteins associated with COVID-19. MAPK1 emerges as a potential therapeutic target, and molecular docking reveals Broussoflavonol F, Glyasperin A, and Sulabiroins as promising compounds with strong binding affinities, i.e.,- 9.0, -9.0, and -8.8 kcal/mol, respectively, exceeding the native ligand (-7.2 kcal/mol). Molecular Dynamics displays stable complex behavior, with backbone RMSD values consistently below 4 Angstroms and RMSF simulations showing minimal fluctuations within ±2 Angstroms error. Moreover, MM-PBSA analysis further supports the strong binding of Broussoflavonol F, Glyasperin A, and Sulabiroins A, with relative binding energies of -122.82±89.65, 131.48±95.39, and -155.97±111,37 kJ/mol, respectively. These results indicate that propolis has potential as an anti-COVID-19 agent, primarily through inhibiting the MAPK1 pathway. However, further research is needed to validate these results and develop practical applications for COVID-19 therapy. This study underscores the significance of network pharmacology and computational models in understanding propolis mechanisms, offering potential directions for future research and treatment strategies against COVID-19.