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1. An optimal regulation of fluxes dictates microbial growth in and out of steady state

   https://doi.org/10.7554/eLife.84878  

   Chure, Griffin; Cremer, Jonas (March 2023, eLife)

   Bitbol, Anne-Florence; Walczak; Aleksandra M (Ed.)

   Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with ≈ 60 data sets from Escherichia coli establishes this regulatory mechanism’s biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments. 

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2. Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell

   https://doi.org/10.1098/rsif.2020.0656  

   Britton, Samuel; Alber, Mark; Cannon, William R. (October 2020, Journal of The Royal Society Interface)

   null (Ed.)

   Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization–reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism. 

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3. Memory Latency Distribution-Driven Regulation for Temporal Isolation in MPSoCs

   https://doi.org/10.4230/LIPIcs.ECRTS.2023.4  

   Saeed, Ahsan; Hoornaert, Denis; Dasari, Dakshina; Ziegenbein, Dirk; Mueller-Gritschneder, Daniel; Schlichtmann, Ulf; Gerstlauer, Andreas; Mancuso, Renato (July 2023, 35th Euromicro Conference on Real-Time Systems (ECRTS 2023))

   Papadopoulos, Alessandro V. (Ed.)

   Temporal isolation is one of the most significant challenges that must be addressed before Multi-Processor Systems-on-Chip (MPSoCs) can be widely adopted in mixed-criticality systems with both time-sensitive real-time (RT) applications and performance-oriented non-real-time (NRT) applications. Specifically, the main memory subsystem is one of the most prevalent causes of interference, performance degradation and loss of isolation. Existing memory bandwidth regulation mechanisms use static, dynamic, or predictive DRAM bandwidth management techniques to restore the execution time of an application under contention as close as possible to the execution time in isolation. In this paper, we propose a novel distribution-driven regulation whose goal is to achieve a timeliness objective formulated as a constraint on the probability of meeting a certain target execution time for the RT applications. Using existing interconnect-level Performance Monitoring Units (PMU), we can observe the Cumulative Distribution Function (CDF) of the per-request memory latency. Regulation is then triggered to enforce first-order stochastical dominance with respect to a desired reference. Consequently, it is possible to enforce that the overall observed execution time random variable is dominated by the reference execution time. The mechanism requires no prior information of the contending application and treats the DRAM subsystem as a black box. We provide a full-stack implementation of our mechanism on a Commercial Off-The-Shelf (COTS) platform (Xilinx Ultrascale+ MPSoC), evaluate it using real and synthetic benchmarks, experimentally validate that the timeliness objectives are met for the RT applications, and demonstrate that it is able to provide 2.2x more overall throughput for NRT applications compared to DRAM bandwidth management-based regulation approaches. 

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4. Opportunities, challenges and pitfalls in characterizing plant water‐use strategies

   https://doi.org/10.1111/1365-2435.13945  

   Kannenberg, Steven A.; Guo, Jessica S.; Novick, Kimberly A.; Anderegg, William R. L.; Feng, Xue; Kennedy, Daniel; Konings, Alexandra G.; Martínez‐Vilalta, Jordi; Matheny, Ashley M. (October 2021, Functional Ecology)

   Abstract Classifying the diverse ways that plants respond to hydrologic stress into generalizable ‘water‐use strategies’ has long been an eco‐physiological research goal. While many schemes for describing water‐use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water‐use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.A generalizable and predictive framework for assessing plant water‐use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water‐use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water‐use strategy framework.This idea is explored using a plant hydraulics model to identify: (a) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water‐use responses, and (b) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water‐use strategies. This modelling exercise can bridge the divide between various conceptualizations of water‐use strategies and provide targeted hypotheses to advance the understanding and quantification of plant water status regulation across spatial and temporal scales.Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water‐use strategy concept, including further investigation into the below‐ground determinants of plant water relations, targeted data collection efforts and the potential to scale these concepts from individuals to whole regions. A freePlain Language Summarycan be found within the Supporting Information of this article. 

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5. Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression

   https://doi.org/10.1146/annurev-biophys-052118-115525  

   Phillips, Rob; Belliveau, Nathan M.; Chure, Griffin; Garcia, Hernan G.; Razo-Mejia, Manuel; Scholes, Clarissa (May 2019, Annual Review of Biophysics)

   It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles’ heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input–output function describing how the level of expression depends upon the parameters of the regulated gene—for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles’ heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life. 

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