System-level Analysis Research Articles

A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle

We have carried out a systems-level analysis of the spatial and temporal dynamics of cell cycle regulators in the fission yeast Schizosaccharomyces pombe. In a comprehensive single-cell analysis, we have precisely quantified the levels of 38 proteins previously identified as regulators of the G2 to mitosis transition and of 7 proteins acting at the G1- to S-phase transition. Only 2 of the 38 mitotic regulators exhibit changes in concentration at the whole-cell level: the mitotic B-type cyclin Cdc13, which accumulates continually throughout the cell cycle, and the regulatory phosphatase Cdc25, which exhibits a complex cell cycle pattern. Both proteins show similar patterns of change within the nucleus as in the whole cell but at higher concentrations. In addition, the concentrations of the major fission yeast cyclin-dependent kinase (CDK) Cdc2, the CDK regulator Suc1, and the inhibitory kinase Wee1 also increase in the nucleus, peaking at mitotic onset, but are constant in the whole cell. The significant increase in concentration with size for Cdc13 supports the view that mitotic B-type cyclin accumulation could act as a cell size sensor. We propose a two-step process for the control of mitosis. First, Cdc13 accumulates in a size-dependent manner, which drives increasing CDK activity. Second, from mid-G2, the increasing nuclear accumulation of Cdc25 and the counteracting Wee1 introduce a bistability switch that results in a rapid rise of CDK activity at the end of G2 and thus, brings about an orderly progression into mitosis.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets.

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model’s response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.

Open systems navigation based on system-level difference analysis - Case studies with urban augmented ecosystems

Complex systems in the real world, such as ecosystems, comprise a large number of interacting components and are exposed to an open environment under dominant external influence, which makes it difficult to set the reference for the measurement of information needed to manage the system. Here we propose a novel methodology to address the reproducibility and objectivity of measurement in such open systems situations, taking examples with ecosystem management in urban green infrastructure.Through the implementation of three biodiversity operations, we obtained 350 variables based on the subjective and objective measurements from two urban ecosystems, concerning the aboveground plant diversity and belowground soil state. Using PCA, we distinguished the effective responses to the biodiversity operations from environmental baseline drift and other marginal fluctuation in terms of the degree of correlation. We then analyzed the commonality of the responses and classified the variables by generating two categories of indices with different levels of consistency. Supported by a given diversity of the variables, significant 69 subjective and 11 objective measures were successfully extracted as consistent indices of operation response, which provided effective interpretation of the system-level changes through the model as an integrated plant-soil relationship.Additionally, we investigated the possibility to replace invasive measurements with non-invasive ones through the construction of proxies with the regression analysis, which showed that the combinations of mainly subjective measures could find significant regression of all16 objectives but invasive indices, in a compatible way with known ecological mechanisms. The utilization of untapped potential of subjective measures would lead to synergistic amelioration of human ecological literacy and cost-efficiency of the ecosystem management.The overall results imply the possibility to navigate urban green infrastructure to a more diverse range of augmented ecosystems, with scientific support for the detection of system-level differences that have been usually buried in the open-field complexity.

Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for U.S. NSHM 2023

As part of the U.S. National Seismic Hazard Model (NSHM) update planned for 2023, two databases were prepared to more completely represent Quaternary-active faulting across the western United States: the NSHM23 fault sections database (FSD) and earthquake geology database (EQGeoDB). In prior iterations of NSHM, fault sections were included only if a field-measurement-derived slip rate was estimated along a given fault. By expanding this inclusion criteria, we were able to assess a larger set of faults for use in NSHM23. The USGS Quaternary Fault and Fold Database served as a guide for assessing possible additions to the NSHM23 FSD. Reevaluating available data from published sources yielded an increase of fault sections from ~650 faults in NSHM18 to ~1,000 faults proposed for use in NSHM23. EQGeoDB, a companion dataset linked to NSHM23 FSD, contains geologic slip rate estimates for fault sections included in FSD. Together, these databases serve as common input data used in deformation modeling, earthquake rupture forecasting, and additional downstream uses in NSHM development.

Computational prediction and experimental validation of Salmonella Typhimurium SopE-mediated fine-tuning of autophagy in intestinal epithelial cells.

Macroautophagy is a ubiquitous homeostasis and health-promoting recycling process of eukaryotic cells, targeting misfolded proteins, damaged organelles and intracellular infectious agents. Some intracellular pathogens such as Salmonella enterica serovar Typhimurium hijack this process during pathogenesis. Here we investigate potential protein-protein interactions between host transcription factors and secreted effector proteins of Salmonella and their effect on host gene transcription. A systems-level analysis identified Salmonella effector proteins that had the potential to affect core autophagy gene regulation. The effect of a SPI-1 effector protein, SopE, that was predicted to interact with regulatory proteins of the autophagy process, was investigated to validate our approach. We then confirmed experimentally that SopE can directly bind to SP1, a host transcription factor, which modulates the expression of the autophagy gene MAP1LC3B. We also revealed that SopE might have a double role in the modulation of autophagy: Following initial increase of MAP1LC3B transcription triggered by Salmonella infection, subsequent decrease in MAP1LC3B transcription at 6h post-infection was SopE-dependent. SopE also played a role in modulation of the autophagy flux machinery, in particular MAP1LC3B and p62 autophagy proteins, depending on the level of autophagy already taking place. Upon typical infection of epithelial cells, the autophagic flux is increased. However, when autophagy was chemically induced prior to infection, SopE dampened the autophagic flux. The same was also observed when most of the intracellular Salmonella cells were not associated with the SCV (strain lacking sifA) regardless of the autophagy induction status before infection. We demonstrated how regulatory network analysis can be used to better characterise the impact of pathogenic effector proteins, in this case, Salmonella. This study complements previous work in which we had demonstrated that specific pathogen effectors can affect the autophagy process through direct interaction with autophagy proteins. Here we show that effector proteins can also influence the upstream regulation of the process. Such interdisciplinary studies can increase our understanding of the infection process and point out targets important in intestinal epithelial cell defense.

Geotechnical uncertainty, modeling, and decision making

Modeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty calculation with a focus on how it enhances the role of modeling in decision making (reliability analysis, reliability-based design, and inverse analysis). The key output from a reliability analysis is the probability of failure, where “failure” is defined as any condition that does not meet a performance criterion or a set of criteria. In contrast to the global factor of safety, the probability of failure respects both mechanics and statistics, is sensitive to data (thus opening one potential pathway to digital transformation), and it is meaningful for both system and component failures. Resilience engineering requires system level analysis. As such, geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output in addition to stresses, strains, forces, and displacements. It is further shown that more critical non-classical failure mechanisms can emerge from spatially variable soils that can escape notice if the engineer were to restrict analysis to conventional homogeneous or layered soil profiles.

Multi-omics analysis reveals expression complexity and functional diversity of mouse kinome.

Protein kinases are a crucial component of signaling pathways involved in a wide range of cellular responses, including growth, proliferation, differentiation, and migration. Systematic investigation of protein kinases is critical to better understand phosphorylation-mediated signaling pathways and may provide insights into the development of potential therapeutic drug targets. Here we perform a systems-level analysis of the mouse kinome by analyzing multi-omics data. We used bulk and single-cell transcriptomic data from the C57BL/6J mouse strain to define tissue- and cell-type-specific expression of protein kinases, followed by investigating variations in sequence and expression between C57BL/6J and DBA/2J strains. We then profiled a deep brain phosphoproteome from C57BL/6J and DBA/2J strains as well as their reciprocal hybrids to infer the activity of the mouse kinome. Finally, we performed phenome-wide association analysis using the BXD recombinant inbred (RI) mice (a cross between C57BL/6J and DBA/2J strains) to identify any associations between variants in protein kinases and phenotypes. Collectively, our study provides a comprehensive analysis of the mouse kinome by investigating genetic sequence variation, tissue-specific expression patterns, and associations with downstream phenotypes.

Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution

The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis.

Energy Savings of Multi-Channel Neurostimulators with Non-Rectangular Current-Mode Stimuli Using Multiple Supply Rails.

In neuromodulation applications, conventional current mode stimulation is often preferred over its voltage mode equivalent due to its good control of the injected charge. However, it comes at the cost of less energy-efficient output stages. To increase energy efficiency, recent studies have explored non-rectangular stimuli. The current work highlights the importance of an adaptive supply for an output stage with programmable non-rectangular stimuli and accordingly proposes a system-level architecture for multi-channel stimulators. In the proposed architecture, a multi-output DC/DC Converter (DDC) allows each channel to choose among the available supply levels (i.e., DDC outputs) independently and based on its instant voltage/current requirement. A system-level analysis is carried out in Matlab to calculate the possible energy savings of this solution, compared to the conventional approach with a fixed supply. The energy savings have been simulated for a variety of supply levels and waveform amplitudes, suggesting energy savings of up to 83% when employing 6 DDC outputs and the lowest current amplitude explored ( 250 μA), and as high as 26% for a full-scale amplitude (4 mA).

System-level analysis for continuous BTX production from shale gas over Mo/HZSM-5 catalyst: Promotion effects of CO2 co-feeding on process economics and environment

Benzene, toluene, and xylene (BTX) production from shale-derived CH4, C2H6, and C3H8 is considered an energy-efficient technology substituting their naphtha-cracking counterparts. However, the economics of BTX production from shale gas remains questionable because of the rapid deactivation of the catalyst, originating from the coke generation. Herein, we present a BTX production strategy with CO2 co-feeding for catalyst stability enhancement and additional H2 production. Our process prevents coke deposition on the active sites of Mo and zeolite, and improves the operation time of reactors compared to that of shale gas without CO2 co-feeding. Concurrently, H2 and CO are produced with BTX owing to the dry reforming reaction of hydrocarbons over the Mo/HZSM-5 catalyst. Most importantly, the economics of BTX production with CO2 co-feeding is evaluated using individual system components integrated with two different CH4 conversion processes (steam-reforming-based H2 and methanol production). The BTX production with CO2 co-feeding lowers the methanol production cost by additionally producing methanol generated by CO from the BTX reactor, while the H2 production cost increases. Moreover, the life cycle assessment proves that the CO2 emission during the methanol production process is significantly reduced by recycling the CO2 produced in the subsequent process.

Large-signal stability analysis of islanded DC microgrids with multiple types of loads

Due to the lack of support for the power system, the small inertia of the system, and the access to negative impedance elements, the stable operation of the islanded DC microgrid can be challenging. To ensure the stability of the system in a large operating range, a system-level large-signal stability analysis is carried out for a droop-controlled dc microgrid in this article. Firstly, the system-level equivalent model of the DC microgrid is established, the steady-state of the DC microgrid is analyzed and the instabilities principle, as well as their physical interpretation, is revealed. Second, necessary and sufficient conditions of the DC microgrid are deduced by the Lyapunov direct method, and the load limit of the DC microgrid is obtained under the restriction of stability and voltage deviation. Third, the correctness of the theoretical analysis is verified by the simulation results of a droop-controlled DC microgrid system.

Integrated Process-System Modeling and Performance Analysis for Serial Production Lines

The performance of a smart manufacturing system is affected by not only the constituent processes but also their system-level interactions. However, in most current studies, individual process modeling and system-level performance evaluation are independent. This can substantially impact production efficiency. In this paper, utilizing available sensor data, an integrated data-enabled model is developed to seamlessly fuse two conventionally separated system-level and process-level models and analysis. A fast recursive method is developed to evaluate the system yield. The permanent production loss (PPL) concept is defined and evaluated based on the proposed integrated model. Furthermore, PPL attributions due to random downtime and quality issues have been identified. Case studies have shown that the integrated model is of high fidelity, and the PPL analysis can effectively identify the root cause of production yield loss.

System-Level Analysis of Joint Sensing and Communication Based on 5G New Radio

This work investigates a multibeam system for joint sensing and communication (JSC) based on multiple-input multiple-output (MIMO) 5G new radio (NR) waveforms. In particular, we consider a base station (BS) acting as a monostatic sensor that estimates the range, speed, and direction of arrival (DoA) of multiple targets via beam scanning using a fraction of the transmitted power. The target position is then obtained via range and DoA estimation. We derive the sensing performance in terms of probability of detection and root mean squared error (RMSE) of position and velocity estimation of a target under line-of-sight (LOS) conditions. Furthermore, we evaluate the system performance when multiple targets are present, using the optimal sub-pattern assignment (OSPA) metric. Finally, we provide an in-depth investigation of the dominant factors that affect performance, including the fraction of power reserved for sensing.

Multiomic Analysis Reveals Disruption of Cholesterol Homeostasis by Cannabidiol in Human Cell Lines

The nonpsychoactive cannabinoid, cannabidiol (CBD), is Food and Dug Administration approved for treatment of two drug-resistant epileptic disorders and is seeing increased use among the general public, yet the mechanisms that underlie its therapeutic effects and side-effect profiles remain unclear. Here, we report a systems-level analysis of CBD action in human cell lines using temporal multiomic profiling. FRET-based biosensor screening revealed that CBD elicits a sharp rise in cytosolic calcium, and activation of AMP-activated protein kinase in human keratinocyte and neuroblastoma cell lines. CBD treatment leads to alterations in the abundance of metabolites, mRNA transcripts, and proteins associated with activation of cholesterol biosynthesis, transport, and storage. We found that CBD rapidly incorporates into cellular membranes, alters cholesterol accessibility, and disrupts cholesterol-dependent membrane properties. Sustained treatment with high concentrations of CBD induces apoptosis in a dose-dependent manner. CBD-induced apoptosis is rescued by inhibition of cholesterol synthesis and potentiated by compounds that disrupt cholesterol trafficking and storage. Our data point to a pharmacological interaction of CBD with cholesterol homeostasis pathways, with potential implications in its therapeutic use.

Rotor Stability Effects of Tilting Pad Journal Bearings With Assembled Clearance Asymmetry

Abstract This paper presents component and system-level analyses to understand the stiffness, damping, and rotor stability effects of tilting pad journal bearings (TPJB) with assembled clearance variation across pads. This variation results from a normal manufacturing process of the bearing components, i.e., shaft, bearing shell, pad radius, and pad thickness, whose machining tolerances add in a closed-loop producing a different assembled clearance per pad. The results are compared to the standard analysis practice of uniformly setting all pads assembled clearance to its maximum and minimum stack-up values to find the worst case for stability. The component-level analysis follows a rigid rotor approach. While in the system-level analysis, a full rotor-bearings-seals model of a lightweight, high-speed centrifugal compressor is employed. A four-pad TPJB with a load between pads (LBP) configuration is considered. Large differences in stiffness and log dec are encountered, while almost negligible differences are found in damping. Also, some interesting observations related to rotor-bearing planar mode shapes are reported when the bearing pads have dissimilar assembled clearances. These modes disappear once the labyrinth seal force coefficients are incorporated into the full model.

A model based investigation of evaporative cooling for polymer electrolyte fuel cells – System level analysis

Evaporative cooling is a promising concept to reduce the fuel cell system volume and mass significantly. This paper investigates the interactions between the fuel cell stack and the balance of plant in an evaporatively cooled polymer electrolyte fuel cell system (PEFCS). For this, a zero-dimensional PEFCS model, comprising the fuel cell stack, air compressor, charge air cooler, humidifier, hydrogen recirculation blower, condensing radiator and water separator has been developed and analyzed. Two evaporative cooling system architectures are compared to conventional, liquid cooling. Optimal operating conditions are determined by a numerical optimization of the net system power output. Main results show that evaporative cooling works on the system level over a wide range of operating conditions. The optimum system power and highest efficiencies are achieved at high temperatures (80–90 °C), low pressure (125–150 kPa) and a corresponding cathode stoichiometry between 1.5 and 3, allowing for a closed water loop at the same time. The air compressor shows an increased power demand, compared to conventional cooling and the exhaust gas condenser is identified as the one critical component for evaporative cooling. Its performance is key to an efficient operation and closed water loop at all ambient conditions.

Process Analysis of Chemical Looping Gasification of Biomass for Fischer–Tropsch Crude Production with Net-Negative CO2 Emissions: Part 1

Large-scale biofuel production plants require an efficient gasification process that generates syngas of high quality (with minimal gas contaminants and inert gases) to minimize the extent of the syngas cleaning processes required for liquid biofuel production. This work presents process modeling of the chemical looping gasification (CLG) process for syngas production. The CLG process is integrated with a Fischer–Tropsch synthesis (FTS) process to produce Fischer–Tropsch (FT) crude with net-negative CO2 emissions, enabling process and system-level analyses of this novel biomass-to-liquid process. CLG resembles indirect gasification in an interconnected circulating fluidized bed reactor, where instead of inert bed material, a solid-oxygen carrier, such as mineral ores rich in iron or manganese oxides, is used. The oxygen carrier particles undergo oxidation and reduction in the air reactor and fuel reactor, respectively, thereby providing heat and oxygen for gasification. This work uses data from CLG experiments performed with steel converter slag as the oxygen carrier and investigates its potential when integrated with different downstream gas cleaning trains and the subsequent fuel synthesis process with the primary objective of quantifying and evaluating the performance of the integrated CLG–FT process plant. Syngas with a high energy content of 12 MJ/Nm3 (lower heating value basis) is predicted with a cold gas efficiency of 73%. CO2/CO ratios, higher than indirect biomass gasification, are also predicted in the raw syngas produced; thus, there exists an opportunity to capture biogenic CO2 with a relatively lower energy penalty in the subsequent gas cleaning stages. This work quantifies other key performance indicators, such as heat recovery potential, negative CO2 emission capacity, and FT crude production efficiency of the CLG–FT plant. A 100 MWth CLG plant produces roughly 677–696 barrels per day of FT crude, with net-negative emissions of roughly 180 kilotonnes of CO2 annually.

Ultra-Low-Power IoT Communications: A Novel Address Decoding Approach for Wake-Up Receivers

Providing energy-efficient Internet of Things (IoT) connectivity has attracted significant attention in fifth-generation (5G) wireless networks and beyond. A potential solution for realizing a long-lasting network of IoT devices is to equip each IoT device with a wake-up receiver (WuR) to have always-accessible devices instead of always-on devices. WuRs typically comprise a radio frequency demodulator, sequence decoder, and digital address decoder and are provided with a unique authentication address in the network. Although the literature on efficient demodulators is mature, it lacks research on fast, low-power, and reliable address decoders. As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR’s power consumption is crucial. Motivated by this need, a low-power, reliable address decoder is developed in this paper. We further investigate the integration of WuR in low-power uplink/downlink communications and, using system-level energy analysis; we characterize operation regions in which WuR can contribute significantly to energy saving. The device-level energy analysis confirms the superior performance of our decoder. The results show that the proposed decoder significantly outperforms the state-of-the-art with power consumption of 60 nW, at cost of compromising negligible increase in decoding delay.

Application of self-centring hybrid rocking columns in steel frames

This study proposed a novel rocking component, namely self-centring hybrid rocking columns (SCR). Cyclic loading tests are conducted on a substructure consisting of the SCR and a frame beam-to-SCR connection to investigate its hysteretic performance. A numerical model of the SCR substructure is built and validated against the test results. Finally, the seismic performance of steel frames equipped with the SCR is demonstrated through the nonlinear time history analysis. It shows that the proposed self-centring hybrid rocking column has acceptable lateral resisting, energy dissipation and self-centring capability. The simplified numerical model of the SCR can reasonably well capture the hysteretic behaviour of the SCR substructure no matter with or without the steel damper and tension braces. The system-level analysis of steel frames with the SCRs verifies the effectiveness of the proposed SCR in mitigating maximum and residual drift responses of steel frames under earthquakes. After equipping the SCRs, the inter-storey drift concentration is also mitigated under earthquakes.

Deterministic Wireless Channel Characterization towards the Integration of Communication Capabilities to Enable Context Aware Industrial Internet of Thing Environments

In order to provide interactive capabilities within the context of Internet of Thing (IoT) applications, wireless communication systems play a key role, owing to in-herent mobility, ubiquity and ease of deployment. However, to comply with Quality of Service (QoS) and Quality of Experience (QoE) metrics, coverage/capacity analysis must be performed, to account for the impact of signal blockage as well as multiple interference sources. This analysis is especially complex in the case of indoor scenarios, such as those derived from Industrial Internet of Things (IIoT). In this work, a fully volumetric approach based on hybrid deterministic 3D Ray Launching is employed providing precise wireless channel characterization and hence, system level analysis of indoor scenarios. Coverage/capacity, interference mapping and time domain characterization estimations will be derived, considering different frequencies of operation below 6 GHz. The proposed methodology will be tested against a real measurement scenario, providing full flexibility and scalability for adoption in a wide range of IIoT capable environments.