SUMMARY Many signaling systems show adaptation—the ability to reset themselves after responding to a stimulus. We computationally searched all possible three-node enzyme network topologies to identify those that could perform adaptation. Only two major core topologies emerge as robust solutions: a negative feedback loop with a buffering node and an incoherent feedforward loop with a proportioner node. Minimal circuits containing these topologies are, within proper regions of parameter space, sufficient to achieve adaptation. Morecomplex circuits that robustly performadaptation all contain at least one of these topologies at their core. This analysis yields a design table highlighting a finite set of adaptive circuits. Despite the diversity of possible biochemical networks, it may be common to find that only a finite set of core topologies can execute a particular function. These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks. For a video summary of this article, see the PaperFlick file with the Supplemental Data available online.

A long-held tenet of molecular pharmacology is that canonical signal transduction mediated by G-protein-coupled receptor (GPCR) coupling to heterotrimeric G proteins is confined to the plasma membrane. Evidence supporting this traditional view is based on analytical methods that provide limited or non-subcellular resolution1. It has been subsequently proposed that signalling by internalized GPCR is restricted to G-protein-independent mechanisms such as scaffolding by arrestins2,3, or GPCR activation elicits a discrete form of persistent G protein activation4–9, or that internalized GPCR can indeed contribute to the acute G protein-mediated response10. Evidence supporting these various latter hypotheses is indirect or subject to alternate interpretation, and it remains unknown if endosome-localized GPCR are even present in an active form. Here we describe the application of conformation-specific single domain antibodies (nanobodies) to directly probe activation of the β2-adrenoceptor, a prototypical GPCR11, and its cognate G protein, Gs (ref. 12) in living mammalian cells. We show that the adrenergic agonist isoprenaline promotes receptor and G protein activation in the plasma membrane as expected, but also in the early endosome membrane; and that internalized receptors contribute to the overall cellular cyclic AMP response within several minutes after agonist application. These findings provide direct support for the hypothesis that canonical GPCR signalling occurs from endosomes as well as the plasma membrane, and suggest a versatile strategy for probing dynamic conformational change in vivo.

Computational modeling and experimentation in the unfolded protein response reveals a role for the ER-resident chaperone protein BiP in fine-tuning the system's response dynamics.

We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.

Molecular biology studies the cause-and-effect relationships among microscopic processes initiated by individual molecules within a cell and observes their macroscopic phenotypic effects on cells and organisms. These studies provide a wealth of information about the underlying networks and pathways responsible for the basic functionality and robustness of biological systems. At the same time, these studies create exciting opportunities for the development of quantitative and predictive models that connect the mechanism to its phenotype then examine various modular structures and the range of their dynamical behavior. The use of such models enables a deeper understanding of the design principles underlying biological organization and makes their reverse engineering and manipulation both possible and tractable The heat shock response presents an interesting mechanism where such an endeavor is possible. Using a model of heat shock, we extract the design motifs in the system and justify their existence in terms of various performance objectives. We also offer a modular decomposition that parallels that of traditional engineering control architectures.control theory ͉ heat shock stress response ͉ mathematical modeling A systems approach is a promising venue for studying biological complexity. The hallmark of this approach is an analysis of the dynamics of the biological network and the protocols used for their interactions. Experiences from fields like engineering, where model-based analysis and design have been the central paradigm, provide a glimpse of what is achievable (1). Biology and engineering share many similarities at the system level (2), including the use of complexity to achieve robustness and performance rather than for minimal functionality. We define robustness as the capability of a system to operate reliably when its physical parameters vary within their expected ranges; conversely, fragility is defined as hypersensitivity to such parametric variations. We suspect that the comparative study of genetic networks will confirm that robustness and the ability to achieve prompt adaptation in the presence of environmental challenges impose severe constraints on the underlying architecture of a system and are the source of the general design motifs repeated in many cellular networks.Here, we study the heat shock response (hsr) in Escherichia coli. The hsr is universally conserved among organisms and serves to remedy protein damage induced by heat and other stresses (reviewed in ref.3). We build a model for the E. coli hsr system, analyze its layers of regulation, and argue that its complexity is a necessary outcome of design requirements such as robustness, speed of response, noise rejection, and efficiency. This model provides useful insight into the heat shock system design architecture. It also suggests a mathematical and conceptual modular decomposition that defines the functional blocks or submodules of the heat shock system. This decomposition is drawn by analogy to manmade control systems and is foun...

Summary Stochasticity is a hallmark of cellular processes and different classes of genes show large differences in their cell-to-cell variability (noise). To decipher the sources and consequences of this noise, we systematically measured pairwise correlations between large numbers of genes, including those with high variability. We find that there is substantial pathway variability shared across similarly regulated genes. This induces quantitative correlations in the expression of functionally related genes such as those involved in the Msn2/4 stress response pathway, amino-acid biosynthesis, and mitochondrial maintenance. Bioinformatic analyses and genetic perturbations suggest that fluctuations in PKA and Tor signaling contribute to pathway-specific variability. Our results argue that a limited number of well-delineated “noise regulons” operate across a yeast cell, and that such coordinated fluctuations enable a stochastic but coherent induction of functionally related genes. Finally, we show that pathway noise is a quantitative tool for exploring pathway features and regulatory relationships in un-stimulated systems.

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Author Contributions RAL, AHN, and SEB contributed equally to this publication. RAL, SEB, ZC, WRPN, and DB conceived of the idea and initial steps for designing protein switches from de novo designed helical bundles. RAL and DB developed the thermodynamic model and the code upon which it works. RAL, SEB, and WRPN designed and biophysically characterized LOCKR scaffolds and BimLOCKR. RAL performed mutagenesis and Bio-layer interferometry experiments. SB characterized Bim interactions to Bcl2 homologs and aided experimental design. RAL performed design calculations for orthogonal LOCKR designs using code from SEB and VKM. AHN and RAL conceived of caging cODC. RAL performed design calculations to cage cODC and tune degronLOCKR. AHN conceived of and contributed to all experiments with degronLOCKR. THN performed dynamic measurement of degronLOCKR. AMW tested degronLOCKR in HEK293T cells. MJL, SEB, and RAL performed design calculations for asymmetric LOCKR. GD performed experiments with degronLOCKR and dCas9. GD contributed to plasmid and strain construction. RAL, SEB, and MJL conceived of caging sequences to control subcellular location and RAL performed design calculations for nesLOCKR. JAS and AHN performed all experiments for nesLOCKR. RAL, SEB, AHN, HE-S, and DB wrote the manuscript, all authors edited and approved.