Robust Network Topologies for Generating Switch-Like Cellular Responses

Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity) and extent of memory (bistability). Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability). Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28%) and bistability (up to 18%); strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3%) or bistable (up to 1%) responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks

Selection and characterization of DARPins specific for the neurotensin receptor 1

We describe here the selection and characterization of designed ankyrin repeat proteins (DARPins) that bind specifically to the rat neurotensin receptor 1 (NTR1), a G-protein coupled receptor (GPCR). The selection procedure using ribosome display and the initial clone analysis required <10 µg of detergent-solubilized, purified NTR1. Complex formation with solubilized GPCR was demonstrated by ELISA and size-exclusion chromatography; additionally, the GPCR could be detected in native membranes of mammalian cells using fluorescence microscopy. The main binding epitope in the GPCR lies within the 33 amino acids following the seventh transmembrane segment, which comprise the putative helix 8, and additional binding interactions are possibly contributed by the cytoplasmic loop 3, thus constituting a discontinuous epitope. Since the selected binders recognize the GPCR both in detergent-solubilized and in membrane-embedded forms, they will be potentially useful both in co-crystallization trials and for signal transduction experiment

Computational analysis of off-rate selection experiments to optimize affinity maturation by directed evolution

Directed evolution is a powerful approach for isolating high-affinity binders from complex libraries. In affinity maturation experiments, binders with the highest affinities in the library are typically isolated through selections for decreased off rate using a suitable selection platform (e.g. phage display or ribosome display). In such experiments, the library is initially exposed to biotinylated antigen and the binding reaction is allowed to proceed. A large excess of unbiotinylated antigen is then added as a competitor to capture the vast majority of rapidly dissociating molecules; the slowly dissociating library members can subsequently be rescued by capturing the biotin-carrying complexes. To optimize the parameters for such affinity maturation experiments, we performed both deterministic and stochastic simulations of off-rate selection experiments using different input libraries. Our results suggest that the most critical parameters for achieving the lowest off rates after selection are the ratio of competitor antigen to selectable antigen and the selection time. Furthermore, the selection time has an optimum that depends on the experimental setup and the nature of the library. Notably, if selections are carried out for times much longer than the optimum, equilibrium is reached and the selection pressure is weakened or lost. Comparison of different selection strategies revealed that sequential selection rounds with lower stringency are favored over high-stringency selection experiments due to enhanced diversity in the selected pools. Such simulations may be helpful in optimizing affinity maturation strategies and off-rate selection experiment

Integrating Extrinsic and Intrinsic Cues into a Minimal Model of Lineage Commitment for Hematopoietic Progenitors

Autoregulation of transcription factors and cross-antagonism between lineage-specific transcription factors are a recurrent theme in cell differentiation. An equally prevalent event that is frequently overlooked in lineage commitment models is the upregulation of lineage-specific receptors, often through lineage-specific transcription factors. Here, we use a minimal model that combines cell-extrinsic and cell-intrinsic elements of regulation in order to understand how both instructive and stochastic events can inform cell commitment decisions in hematopoiesis. Our results suggest that cytokine-mediated positive receptor feedback can induce a “switch-like” response to external stimuli during multilineage differentiation by providing robustness to both bipotent and committed states while protecting progenitors from noise-induced differentiation or decommitment. Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally

Transient Noise Amplification and Gene Expression Synchronization in a Bistable Mammalian Cell-Fate Switch

Progenitor cells within a clonal population show variable proclivity toward lineage commitment and differentiation. This cell-to-cell variability has been attributed to transcriptome-wide gene expression noise generated by fluctuations in the amount of cellular machinery and stochasticity in the biochemical reactions involved in protein synthesis. It therefore remains unclear how a signaling network, in the presence of such noise, can execute unequivocal cell-fate decisions from external cues. Here, we use mathematical modeling and model-guided experiments to reveal functional interplay between instructive signaling and noise in erythropoiesis. We present evidence that positive transcriptional feedback loops in a lineage-specific receptor signaling pathway can generate ligand-induced memory to engender robust, switch-like responses. These same feedback loops can also transiently amplify gene expression noise in the signaling network, suggesting that external cues can actually bias seemingly stochastic decisions during cell-fate specification. Gene expression levels among key effectors in the signaling pathway are uncorrelated in the initial population of progenitor cells but become synchronized after addition of ligand, which activates the transcriptional feedback loops. Finally, we show that this transient noise amplification and gene expression synchronization induced by ligand can directly influence cell survival and differentiation kinetics within the population

Model-guided engineering of DNA sequences with predictable site-specific recombination rates

Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Here, using quantitative high-throughput experiments and machine learning, the authors achieve rational control of a DNA attachment site sequence to predictably modulate site-specific recombination rates both in vitro and in cells