Fragile Nodes Research Articles

Cell state-directed therapy - epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide.

Cancer therapy continues to be plagued by modest therapeutic advances. This is particularly evident in glioblastoma multiforme (GBM) wherein treatment failures are attributed to intratumoral heterogeneity (ITH), a dynamic process of cell state transitions or plasticity. To address ITH, we introduce the concept of cell state-directed (CSD) therapy through a quantitative systems pharmacology model of temozolomide (TMZ), a cornerstone of GBM drug therapy. The model consisting of multiple modules incorporated an epigenetic-based gene transcription-translation module that enabled CSD therapy. Numerous model simulations were conducted to demonstrate the potential impact of CSD therapy on TMZ activity. The simulations included those based on global sensitivity analyses to identify fragile nodes - MDM2 and XIAP - in the network, and also how an epigenetic modifier (birabresib) could overcome a mechanism of TMZ resistance. The positive results of CSD therapy on TMZ activity supports continued efforts to develop CSD therapy as a new anticancer approach.

Situation awareness method of the distribution network based on EMD-SVD and Elman neural network

In order to ensure reliability and security of power supply, the situation awareness of some elements in the distribution network is studied in this paper. Considering the influence of the interference signal, Empirical Mode Decomposition (EMD) is introduced to reduce it. Subsequently, according to operation characteristics of the actual distribution network, several evaluation indexes are proposed to describe the operation situation of the overall distribution network and internal elements, especially evaluation of fragile nodes. Based on these indexes and characteristics of actual distribution network, the comprehensive evaluation method of fragile nodes (CEMFN) is constructed, which mainly aims at the fragile nodes in the distribution network and synthesizes the single index coefficient to realize the identification of fragile nodes. Through the simulation calculation, it can be seen that the result of CEMFN is more accurate than that of the traditional evaluation model, which provides an important theoretical support for the operation and maintenance of distribution network and reliable power supply.

Research on Static and Dynamic Fragile Node Identification Algorithms Based on Uncertainty in New Energy

In order to identify the uncertain static and dynamic fragile nodes in new energy, the instability and randomness of new energy bring new challenges to the identification of vulnerable nodes in a power grid. Due to the characteristics of low cost and low energy consumption of new energy, people have paid much attention to the exploration and development of new energy. Due to the uncertainty of new energy, it is needed to properly analyze the uncertainty factors. To analyze the uncertainty factors in new energy using the framework of power big data artificial intelligence analysis based on cost-benefit analysis (CBA), it is required to carry out Fourier transform and extract the data characteristic matrix so that a vulnerability risk prediction index can be obtained by using a fuzzy convolution algorithm and binarization, and the safety form between the uncertainty factors in new energy and power stations can be evaluated. In this paper, a fuzzy neural network algorithm is proposed to identify the static and dynamic fragile nodes based on the uncertainty in new energy, so as to ensure the security and stability of the power generation system. The safety performance of the power station system is detected through different levels of early warning sensitivity. The simulation model of the above algorithm is constructed in MATLAB. The simulation results show that the proposed algorithm increases the sensitivity of the early warning system of the power station and the sensitivity of triggering the early warning system and improves the security of the power station system as a whole.

Research on evaluation of minimum backbone grid of transmission network based on differentiation planning

The essence of the differential planning of transmission network is to evaluate the minimum backbone grid of transmission network, and the evaluation of the backbone grid of transmitting minimum network includes the evaluation of important nodes and the evaluation of important lines. In this paper, the important node evaluation of the transmission network takes into account the structural fragile nodes and the important load nodes, and the important line evaluation is based on the line number, which is considered synthetically from the point of view of the structural fragile line and the important load line. The minimum backbone grid of transmission network is constructed by the structure backbone grid and the important load backbone grid.

Research on Identification Algorithm of Fragile Nodes: Based on Static and Dynamic Combination of Uncertainty in New Energy

<p>In this work, <span style="font-family: 'Times New Roman';">t</span>aking the coupled 54-node power distribution system and 25-node power flow system as examples, the effectiveness of this method is verified. In addition, a multi-objective ADN joint planning model is established by means of Wasserstein distance measurement and K-medoid scenario analysis. The location, size RES, BESS and expansion schemes of distribution network based on this model are analyzed in detail, in order to provide scientific, reliable, and cost-maximized EVCS’s charging service capabilities. More importantly, we propose a multi-objective optimization algorithm−MONAA algorithm to solve the model.</p>

AEROSPACE APPLICATIONS IN NANO SCALE CMOS TECHNOLOGY USING 12T MEMORY CELL

Semiconductor sizes and the gaps between them are decreasing with the advancement of technology. This means that SRAM cells used in aerospace applications are more vulnerable to soft-error when the fundamental charge of the fragile nodes decreases. A single-event upset (SEU) caused by a radiation particle striking a sensitive node in a conventional 6T SRAM cell could lead to data inversion. To lessen the consequences of SEUs, this study proposes the use of a Soft-Error-Aware Read-StabilityEnhanced Low-Power 12T (SARP12T) SRAM cell. The performance of SARP12T is evaluated in relation to other recently released soft-error-aware SRAM cells, such as QUCCE12T, QUATRO12T, RHD12T, RHPD12T, and RSP14T. In the event that a radiation attack flips the values of the sensitive nodes in SARP12T, the data may still be recoverable. When a storage node pair initiates a singleevent multi-node upset (SEMNU), SARP12T is resilient enough to endure it. During read operations, the bitline makes it simple to reach the '0' storing memory nodes in the suggested cell, and it also makes them very resilient to interruptions. In terms of energy usage, SARP12T is the most effective holding strategy. SARP12T outperforms competing cells in terms of write performance, and its write latency is much decreased. To achieve all of these advantages, the suggested cell very slightly increases read latency and read/write energy.

Metabolic perturbation of acrylate pathway in Lactobacillus plantarum

Engineering microbes with heterologous pathway for production of bio-based products has received considerable attention. Reconstituting such non-native pathway in addition to desired product formation often brings an allosteric modulation in enzymes competing at fragile nodes that result in by-product redistribution, in order to retain energy and redox balance. This work, Lactobacillus plantarum engineered with acrylate pathway for propionate production was studied under similar perspectives. Upon expression, the heterologous pathway did not result in propionic acid production under standard glucose concentration of 20 g/L, but 0.01 mM of propionate was formed when grown under low glucose concentration of 1 g/L. Further analysis of secreted metabolites with increased glucose concentration of 10 and 30 g/L remained futile towards propionate formation but showed reorientation in pyruvate metabolism which was related to the control imposed by the host to regulate the hidden constraints caused by gene perturbation. Further, it was ensured that the limitation of supplements did not play any functional role in inhibiting propionic acid synthesis but still followed similar metabolic pattern which was quite unclear though interpreted to certain extent. Thus, the findings gave insights into physiological and metabolic capabilities of Lactobacillus plantarum that at least in principle can be used to enhance the strain performance for increased propionic acid production.

Identify Critical Nodes in Network Cascading Failure Based on Data Analysis

In communication networks, the cascading failure, which is initiated by influential nodes, may cause local paralysis of communication networks and make network management systems face big challenges in both fault location and the rational use of maintenance resource. As network failure is inevitable, how to find the fragile nodes and the root cause of cascade failure has been recognized as an important research problem in both academia and industry. In this paper, we focus on the problem of identifying critical nodes when cascading failures occur in communication networks. Based on the Barabasi–Albert (BA) model, which is used to generate the scale-free network, we design a reasonable global model of load redistribution for the communication network, and we also find that the betweenness centrality can accurately reflect the scale of cascading failure, and the closeness centrality is negatively correlated to the frequency of failure participation, by (1) establishing a reasonable model of fault propagation, (2) extracting and analyzing the dataset derived from the topology information. Simulation results demonstrate that our model can effectively identify critical nodes of networks and the global redistribution model is more robust than other existing models.

Hierarchical, imbalanced pro-inflammatory cytokine networks govern the pathogenesis of chronic arthropathies.

Chronic inflammatory arthropathies, such as rheumatoid arthritis (RA), spondyloarthritis, including psoriatic arthritis (PsA), ankylosing spondyloarthritis (AS), osteoarthritis (OA), and intervertebral disc degenerative disease (DDD) constitute major public health problems that are anticipated to grow significantly as the human population ages. However, many aspects concerning the molecular mechanisms underlying their onset and progression remain unclear. This narrative review critically analyzes the molecular mechanisms underlying the inflammation-associated pathogenesis of the aforementioned joint diseases. This includes, in particular, the major role played by several key soluble factors (such as cytokines and the associated signaling pathways, designated as "fragile nodes") produced by local cells and recruited to the joints' immune cells, whose elimination by specific drugs has dramatically improved the diseases' symptomatology and outcome in human clinical trials or in rodent arthritis models. We hypothesize that the pathogenesis of chronic inflammatory arthropathies is governed by hierarchical, imbalanced pro-inflammatory cytokine networks (HIPICNs) (comprising a combination of fragile nodes) that are created during the development of both autoimmune (RA, PsA, and AS) and non-autoimmune (OA and DDD) disorders. The main aim of this review is to provide evidence that despite substantial pathobiological differences between these arthropathies, the HIPICNs created are quite common, thus justifying the merging of these disorders mechanistically and suggesting that these common mechanisms exist in the onset and progression of different joint diseases.

Optimal Control of Solar Energy Resources in Loading Margin Enhancement for Peninsular Malaysia Network Using Artificial Neural Network (ANN) Model

Renewable energy invariably foreseen to be fully spurs by 2030-2050 and Malaysia has been seriously intensifies their capabilities on producing solar energy for generating amount of power to be injected into the national grid. The aim of this paper is to investigate the provision of photovoltaic generators quantitatively with respect to future load generation while considering the Loading Margin (LM) capability. Renewable energy resources are known with its ambiguity and commonly for rural electrifications each houses or buildings are installed with battery storage system which allows unused power during daytime to be stored and will be utilized at night. This kind of system would not experience big impact in conjunction of intermittent level of insolation due to its small requirement at receiving end. Conversely, in big cities located in the west region of Malaysia it becomes a big issue to cope with variations of load demand. In an explicit form of voltage deficiency caused by irregular operation on the grid, a fragile electrical nodes most likely vulnerable and PV generators are inevitably have to stay connected to the point of common coupling supporting grid’s voltage. Eventually at this stage, Grid System Operator (GSO) needs to have brisk respond to these online scenarios but in practice it could not be done at ease. Thus, an intelligent model of ANN which incorporated tremendous set of local data is developed in this paper to regulate loading margin (LM) at critical node by taking into account possible wheather condition at any given time and loading increments. This method employed a kind of supervised training and successfully yields smart predictive technique of LM in any certain correspond inputs. By having this, GSO can barely make the arrangement of reactive power injection throughout the network with the central coordination control among PV generators. Simulation results show the effectiveness of the proposed method and have been discussed thoroughly.

Upregulation of MAPK Negative Feedback Regulators and RET in Mutant ALK Neuroblastoma: Implications for Targeted Treatment.

Activating ALK mutations are present in almost 10% of primary neuroblastomas and mark patients for treatment with small-molecule ALK inhibitors in clinical trials. However, recent studies have shown that multiple mechanisms drive resistance to these molecular therapies. We anticipated that detailed mapping of the oncogenic ALK-driven signaling in neuroblastoma can aid to identify potential fragile nodes as additional targets for combination therapies. To achieve this goal, transcriptome profiling was performed in neuroblastoma cell lines with the ALK(F1174L) or ALK(R1275Q) hotspot mutations, ALK amplification, or wild-type ALK following pharmacologic inhibition of ALK using four different compounds. Next, we performed cross-species genomic analyses to identify commonly transcriptionally perturbed genes in MYCN/ALK(F1174L) double transgenic versus MYCN transgenic mouse tumors as compared with the mutant ALK-driven transcriptome in human neuroblastomas. A 77-gene ALK signature was established and successfully validated in primary neuroblastoma samples, in a neuroblastoma cell line with ALK(F1174L) and ALK(R1275Q) regulable overexpression constructs and in other ALKomas. In addition to the previously established PI3K/AKT/mTOR, MAPK/ERK, and MYC/MYCN signaling branches, we identified that mutant ALK drives a strong upregulation of MAPK negative feedback regulators and upregulates RET and RET-driven sympathetic neuronal markers of the cholinergic lineage. We provide important novel insights into the transcriptional consequences and the complexity of mutant ALK signaling in this aggressive pediatric tumor. The negative feedback loop of MAPK pathway inhibitors may affect novel ALK inhibition therapies, whereas mutant ALK induced RET signaling can offer novel opportunities for testing ALK-RET oriented molecular combination therapies.

Fragility in dynamic networks: application to neural networks in the epileptic cortex.

Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state feedback control to steer the network back to its stable state.

Fragility in Networks: Application to the Epileptic Brain

Networks consist of interacting components that often function together to achieve a particular goal. For example, in the human cortex, populations of neurons in different layers continuously communicate and encode information about the subject's environment and cognitive state to govern behavior. In cortical networks, neurons (network nodes) can be structurally connected (through synapses) in addition to being functionally connected; and in epilepsy, structural connections in just a few nodes change to destabilize the network and cause seizures. We define these nodes as fragile and set out to quantify fragility in networks. We first consider arbitrary linear networks whose state-evolution matrices characterize functional connectivity. Nodal fragility is then computed as the minimum energy perturbation required on the node's functional connectivity to destabilize the network. We then apply our perturbation theory to a stable probabilistic nonlinear neural network model. We show how the destabilizing perturbation in functional connectivity translates to a perturbation on the structural connections between neurons, i.e., the synaptic weights. Our results suggest that the most fragile nodes in the network are excitatory neurons that become more active or inhibitory neurons that become less active. This is consistent with abnormal axonal sprouting of excitatory neurons and loss of inhibitory chandelier cells observed in epileptic cortical tissue. The simulated activity before and after seizure also highlight the heterogeneity observed in actual recordings from epilepsy patients, where parts of the network either increase or decrease baseline firing while the rest of the neurons become silenced.

Design and Implementation of Secure Nodes in the Based-Internet-of-Things Intelligent Household

As one of the most important uses of the Internet of things (IOT), the intelligent household is becoming more and more popular. There are many fragile nodes in the intelligent household and they are bound to encounter some potential risks of hostile attacks, such as eavesdropping, denial of service, error instructs, non-authorized access or fabrication and others. This paper presents a method of design and implement of secure nodes for the intelligent household based on the IOT technology, besides giving the hardware model of nodes, the management of key, the access authentication of network, the transmission of encrypted data, and the alarm based on intrusion detection and other security mechanisms. That is, to improve the security of the based-IOT intelligent household from the viewpoint of nodes security. A test platform is built and the results of simulation prove that the proposed method can effectively improve the security of the intelligent household from access safety and transmission security.

Integrative Toxicoproteomics Implicates Impaired Mitochondrial Glutathione Import as an Off-Target Effect of Troglitazone

Troglitazone, a first-generation thiazolidinedione of antihyperglycaemic properties, was withdrawn from the market due to unacceptable idiosyncratic hepatotoxicity. Despite intensive research, the underlying mechanism of troglitazone-induced liver toxicity remains unknown. Here we report the use of the Sod2+/– mouse model of silent mitochondrial oxidative-stress-based and quantitative mass spectrometry-based proteomics to track the mitochondrial proteome changes induced by physiologically relevant troglitazone doses. By quantitative untargeted proteomics, we first globally profiled the Sod2+/– hepatic mitochondria proteome and found perturbations including GSH metabolism that enhanced the toxicity of the normally nontoxic troglitazone. Short- and long-term troglitazone administration in Sod2+/– mouse led to a mitochondrial proteome shift from an early compensatory response to an eventual phase of intolerable oxidative stress, due to decreased mitochondrial glutathione (mGSH) import protein, decreased dicarboxylate ion carrier (DIC), and the specific activation of ASK1-JNK and FOXO3a with prolonged troglitazone exposure. Furthermore, mapping of the detected proteins onto mouse specific protein-centered networks revealed lipid-associated proteins as contributors to overt mitochondrial and liver injury when under prolonged exposure to the lipid-normalizing troglitazone. By integrative toxicoproteomics, we demonstrated a powerful systems approach in identifying the collapse of specific fragile nodes and activation of crucial proteome reconfiguration regulators when targeted by an exogenous toxicant.

Toward a systems‐level understanding of the Hedgehog signaling pathway: defining the complex, robust, and fragile

The Hedgehog (Hh) signaling pathway plays a fundamental role in development and tissue homeostasis, governing cell proliferation and differentiation, as well as cell fate. Hh signaling is mediated by an intricate network of proteins that have positive and negative roles that work in concert to fine-tune signaling output. Using feedback loops, redundancy and subcellular compartmentalization, the temporal and spatial dynamics of Hh signaling have evolved to be complex and robust. Yet developmental defects and cancers that arise from perturbation of the Hh pathway reflect specific pathway fragilities. Importantly, these fragile nodes and edges present opportunities for the design of targeted therapies. Despite these significant advances, unconnected molecular links within the Hh pathway still remain, many of which revolve around the dependence of Hh signaling on the primary cilium, an antenna-like sensory organelle. A systems-level understanding of Hh signaling and of ciliary biology will comprehensively define all nodes and edges of the Hh signaling network and will help identify precise therapeutic targets.

RISK ASSESSMENT OF ATTACK-INDUCED CASCADE IN COMPLEX NETWORKS

For the complex networks, including scale-free, small-world, local-world and random networks, the global quantitative evaluation of attack-induced cascade is investigated in this paper by introducing the risk assessment, which integrates the probability of occurrence with the damage size of attacks on nodes. It is discovered by simulations, among the several kinds of networks, that the small-world network has the largest risk assessment of attack-induced cascade; the risk assessment of three other networks are all very low and the most protection against attack should be given to the small-world network accordingly. Furthermore, the percentage of the most fragile nodes in the scale-free network is very low, compared with that in the small-world network.

Abstract 2207: Functional interrogation of the colorectal cancer genome identifies numerous suppressors of anchorage-independent growth

Abstract INTRODUCTION: High throughput cancer genome sequencing efforts are leading to the identification of frequently mutated genes in cancer. Unfortunately, the extreme diversity of lesions being detected presents a challenge to segment causal from coincidental mutations and to elucidate how causal lesions disrupt regulatory networks to drive cancer processes. AIMS AND METHODS: Given that a common early perturbation in solid tumor initiation is bypass of matrix-dependent proliferation restraints, we used immortalized human colonic epithelial cells (HCECs) to identify suppressors of anchorage-independent growth by conducting a soft-agar based shRNA screen within frequently mutated colorectal cancer (CRC) genes. RESULTS AND CONCLUSIONS: Remarkably, depletion of 65 of the 151 frequently CRC-mutated genes tested collaborated with K-RASV12 or TP53 knockdown to promote anchorage-independent proliferation of HCECs. These candidates fall under a variety of different signaling pathways, but in particular JNK signaling was found to be a master suppressor of anchorage-independent growth in normal HCECs. We have identified additional members of an extensive gene network specifying matrix-dependent proliferation, by constructing an interaction map of these confirmed progression suppressors with the ∼700 rare CRC-mutated genes and experimentally verifying soft-agar growth enhancement in response to depletion of these genes. Collectively, this study reveals a profound diversity of fragile nodes within a fundamental tumor suppressor network, perturbation of which leads to enhanced cell-autonomous proliferative fitness. These studies establish the importance of functionally annotating cancer genomes using biologically relevant assays. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2207. doi:10.1158/1538-7445.AM2011-2207

Structural Analysis to Determine the Core of Hypoxia Response Network

The advent of sophisticated molecular biology techniques allows to deduce the structure of complex biological networks. However, networks tend to be huge and impose computational challenges on traditional mathematical analysis due to their high dimension and lack of reliable kinetic data. To overcome this problem, complex biological networks are decomposed into modules that are assumed to capture essential aspects of the full network's dynamics. The question that begs for an answer is how to identify the core that is representative of a network's dynamics, its function and robustness. One of the powerful methods to probe into the structure of a network is Petri net analysis. Petri nets support network visualization and execution. They are also equipped with sound mathematical and formal reasoning based on which a network can be decomposed into modules. The structural analysis provides insight into the robustness and facilitates the identification of fragile nodes. The application of these techniques to a previously proposed hypoxia control network reveals three functional modules responsible for degrading the hypoxia-inducible factor (HIF). Interestingly, the structural analysis identifies superfluous network parts and suggests that the reversibility of the reactions are not important for the essential functionality. The core network is determined to be the union of the three reduced individual modules. The structural analysis results are confirmed by numerical integration of the differential equations induced by the individual modules as well as their composition. The structural analysis leads also to a coarse network structure highlighting the structural principles inherent in the three functional modules. Importantly, our analysis identifies the fragile node in this robust network without which the switch-like behavior is shown to be completely absent.

Robustness and fragility in the yeast high osmolarity glycerol (HOG) signal‐transduction pathway

Cellular signalling networks integrate environmental stimuli with the information on cellular status. These networks must be robust against stochastic fluctuations in stimuli as well as in the amounts of signalling components. Here, we challenge the yeast HOG signal-transduction pathway with systematic perturbations in components' expression levels under various external conditions in search for nodes of fragility. We observe a substantially higher frequency of fragile nodes in this signal-transduction pathway than that has been observed for other cellular processes. These fragilities disperse without any clear pattern over biochemical functions or location in pathway topology and they are largely independent of pathway activation by external stimuli. However, the strongest toxicities are caused by pathway hyperactivation. In silico analysis highlights the impact of model structure on in silico robustness, and suggests complex formation and scaffolding as important contributors to the observed fragility patterns. Thus, in vivo robustness data can be used to discriminate and improve mathematical models.