Resource Sharing Controls Gene Expression Bursting.

Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer.

Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However, understanding how transcriptional bursting, and consequently, cell fate, responds to DNA damage on a genome-wide scale poses a challenge. In this study, we propose an interpretable and scalable inference framework, DeepTX, that leverages deep learning methods to connect mechanistic models and scRNA-seq data, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments, we observed that fluctuations in transcriptional bursting induced by different drugs was associated with distinct fate decisions: IdU treatment was associated with differentiation in mouse embryonic stem cells by increasing the burst size of gene expression, while low- and high-dose 5FU treatments in human colon cancer cells were associated with changes in burst frequency that corresponded to apoptosis- and survival-related fate, respectively. Together, these results show that DeepTX enables genome-wide inference of transcriptional bursting from single-cell transcriptomics data and can generate hypotheses about how bursting dynamics relate to cell fate decisions.

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

Revealing how transcriptional bursting kinetics are genomically encoded is challenging because genome structures are stochastic at the organization level and are suggestively linked to gene transcription. To address this challenge, we develop a generic theoretical framework that integrates chromatin dynamics, enhancer–promoter (E-P) communication, and gene-state switching to study transcriptional bursting. The theory predicts that power law can be a general rule to quantitatively describe bursting modulations by E-P spatial communication. Specifically, burst frequency and burst size are up-regulated by E-P communication strength, following power laws with positive exponents. Analysis of the scaling exponents further reveals that burst frequency is preferentially regulated. Bursting kinetics are down-regulated by E-P genomic distance with negative power-law exponents, and this negative modulation desensitizes at large distances. The mutual information between burst frequency (or burst size) and E-P spatial distance further reveals essential characteristics of the information transfer from E-P communication to transcriptional bursting kinetics. These findings, which are in agreement with experimental observations, not only reveal fundamental principles of E-P communication in transcriptional bursting but also are essential for understanding cellular decision-making.

Shared resource pools are facilities featuring a certain amount of resources which can be used by different applications. For managing resources in such pools, the demand of each application can be used. Such a demand, however, is driven by the workload, which varies over time. For that reason, adaptive approaches have been proposed for the management of shared resources pools. Whereas a number of solutions exist in this context, they are either not truly decentralized or do not apply to the problem we are dealing with. In this paper, we then present Darma, an approach for managing shared resource pools in a truly decentralized, adaptive, and optimal way.

Even if CU jealously envies the public data auditing task of stored data to a trustworthy third-party auditor to prevent communication costs as well as computing costs after data outsourcing. Practically speaking, neither TPA nor CSP can be trusted. Data may be deleted by CSPs in order to clear up storage space for financial gain or in order to protect their own reputation among cloud consumers by fabricating data loss instances. Since they do not have physical control over the outsourced data, cloud users are unable to identify the person claiming responsibility for unlawful data treatment. Internal security risks and risks to shared cloud storage still exist. On the other hand, TPA errs on occasion and acts deliberately or is the victim of other illegal activities. As a result, either the audit message might be spoofed or TPA could create a misleading audit message without performing any data verification work, in which case CSP would be up against it. In this study, we provide an innovative acknowledgment verification scheme for cloud storage services to resolve these challenging issues.

As a convenient, on-demand access service model for network based, configurable shared calculation resource pool, cloud computing has many advantages. At the same time, there are many challenges in the development and application of cloud computing, especially in cloud security. For quantification demand of cloud security services, drawing on cloud computing risk control security assess framework related to the international organizations, based on China's classification protection assessment requirements, the cloud computing security evaluation index system is built using of Delphi method, and the weight of each index entry is calculated using the AHP(Analytic Hierarchy Process). The cloud security assessment model has been validated through examples, and the results show that the proposed model can make quantitative assessment on secure cloud platform effectively, with much more efficiency.

The effective operation of discrete manufacturing systems relies heavily on the resilience and collaborative capabilities of their supply chains. In the context of global supply chain restructuring and the new wave of technological revolution, frequent high-impact disruptions have posed severe resilience challenges to discrete manufacturing-particularly due to its inherent characteristics such as high product variety, small batch sizes, and heterogeneous production processes. Small and medium-sized enterprises (SMEs), in particular, are more vulnerable due to limited digital capabilities and imbalanced resource reserves. This highlights the urgent need for systematically designed and intelligently orchestrated supply chain networks that balance robustness and agility. However, existing studies often focus on passive, risk-oriented strategies and lack proactive approaches that enable value creation through dynamic optimisation. To address these challenges, this study proposes a value-driven spiral-reticular collaborative model centred around three core elements: manufacturing requirement, collaboration mode and shared resource. The model constructs a business-linked, value-optimised supply chain network and builds a semantic based shared resource pool, enabling scenario-based and real-time resource matching. A case study demonstrates that the proposed model offers a systematic approach for enhancing supply chain resilience (SCR) in discrete manufacturing and supports operational continuity and efficiency in the face of high-impact disruptions.

Cell-to-cell variability plays a critical role in cellular responses and decision-making in a population, and transcriptional bursting has been broadly studied by experimental and theoretical approaches as the potential source of cell-to-cell variability. Although molecular mechanisms of transcriptional bursting have been proposed, there is little consensus. An unsolved key question is whether transcriptional bursting is intertwined with many transcriptional regulatory factors or is an intrinsic characteristic of RNA polymerase on DNA. Here we design an in vitro single-molecule measurement system to analyse the kinetics of transcriptional bursting. The results indicate that transcriptional bursting is caused by interplay between RNA polymerases on DNA. The kinetics of in vitro transcriptional bursting is quantitatively consistent with the gene-nonspecific kinetics previously observed in noisy gene expression in vivo. Our kinetic analysis based on a cellular automaton model confirms that arrest and rescue by trailing RNA polymerase intrinsically causes transcriptional bursting.

Measurements of the magnitude and frequency of cosmic-ray bursts have been made at elevations of 185, 1620, 3240 and 4300 meters above sea level for periods of time ranging from 189 to 336 hours, using one of the new cosmic-ray intensity meters of the Carnegie Institution. Only bursts releasing more than ${10}^{7}$ ion pairs are considered. The rather meager data which could be collected in these short times of observation indicate that: (1) Burst-frequency decreases with burst magnitude in a way which can be represented by a series of exponentials. (2) These large bursts contribute only a small fraction of the total ionization. (3) The magnitude of the largest burst observed increases rapidly with altitude, one observed at 4300 meters elevation exceeding ${10}^{9}$ ion pairs or 3\ifmmode\times\else\texttimes\fi{}${10}^{10}$ electron volts of energy released in the chamber.

BackgroundGene expression is an inherently stochastic process, owing to its dynamic molecular nature. Protein amount distributions, which can be acquired by cytometry using a reporter gene, can inform about the mechanisms of the underlying microscopic molecular system.ResultsBy using different clones of chicken erythroid progenitor cells harboring different integration sites of a CMV-driven mCherry protein, we investigated the dynamical behavior of such distributions. We show that, on short term, clone distributions can be quickly regenerated from small population samples with a high accuracy. On longer term, on the contrary, we show variations manifested by correlated fluctuation in the Mean Fluorescence Intensity. In search for a possible cause of this correlation, we demonstrate that in response to small temperature variations cells are able to adjust their gene expression rate: a modest (2 °C) increase in external temperature induces a significant down regulation of mean expression values, with a reverse effect observed when the temperature is decreased. Using a two-state model of gene expression we further demonstrate that temperature acts by modifying the size of transcription bursts, while the burst frequency of the investigated promoter is less systematically affected.ConclusionsFor the first time, we report that transcription burst size is a key parameter for gene expression that metazoan cells from homeotherm animals can modify in response to an external thermal stimulus.Electronic supplementary materialThe online version of this article (doi:10.1186/s12867-015-0048-2) contains supplementary material, which is available to authorized users.

This paper focuses on resource sharing in hierarchical fixed priority pre-emptive systems where a number of separate applications, each with its own server, reside on a single processor. It defines the hierarchical stack resource policy, an appropriate global resource access policy that bounds priority inversion and also limits interference due to overruns during resource access. The paper provides detailed response time analysis enabling the schedulability of application servers and tasks to be determined for systems with local and global resource access. This analysis is applicable to real-world systems where server-based applications need mutually exclusive access to shared resources such as communications buffers, peripheral devices, operating system calls and data structures shared with interrupt handlers

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

The sustainable use of common pool resources has become a significant global challenge. It is now widely accepted that specific mechanisms such as community-based management strategies, institutional responses such as resource privatization, information availability and emergent social norms can be used to constrain individual ‘harvesting’ to socially optimal levels. However, there is a paucity of research focused specifically on aligning profitability and sustainability goals. In this paper, an integrated mathematical model of a common pool resource game is developed to explore the nexus between the underlying costs and benefits of harvesting decisions and the sustainable level of a shared, dynamic resource. We derive optimal harvesting efforts analytically and then use numerical simulations to show that individuals in a group can learn to make harvesting decisions that lead to the globally optimal levels. Individual agents make their decision based on signals received and a trade-off between economic and ecological sustainability. When the balance is weighted towards profitability, acceptable economic and social outcomes emerge. However, if individual agents are solely driven by profit, the shared resource is depleted in the long run - sustainability is possible despite some greed, but too much will lead to over-exploitation.

Distributed camera surveillance systems typically consist of multiple cameras that need to store some fraction of their video streams in a central storage node. The disk space of this node as well as the network between the cameras and this central node constitute shared resources. In the paper the disk space allocation as well as the network bandwidth reservation are solved using techniques normally associated with process control. These include mid-range control and tracking-based control of global shared resources. The approach is evaluated by simulations.