@article{6465, abstract = {Tight control over protein degradation is a fundamental requirement for cells to respond rapidly to various stimuli and adapt to a fluctuating environment. Here we develop a versatile, easy-to-handle library of destabilizing tags (degrons) for the precise regulation of protein expression profiles in mammalian cells by modulating target protein half-lives in a predictable manner. Using the well-established tetracycline gene-regulation system as a model, we show that the dynamics of protein expression can be tuned by fusing appropriate degron tags to gene regulators. Next, we apply this degron library to tune a synthetic pulse-generating circuit in mammalian cells. With this toolbox we establish a set of pulse generators with tailored pulse lengths and magnitudes of protein expression. This methodology will prove useful in the functional roles of essential proteins, fine-tuning of gene-expression systems, and enabling a higher complexity in the design of synthetic biological systems in mammalian cells.}, author = {Chassin, Hélène and Müller, Marius and Tigges, Marcel and Scheller, Leo and Lang, Moritz and Fussenegger, Martin}, issn = {20411723}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{A modular degron library for synthetic circuits in mammalian cells}}, doi = {10.1038/s41467-019-09974-5}, volume = {10}, year = {2019}, } @article{6467, abstract = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA (small nucleolar RNA). Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.}, author = {Fraisse, Christelle and Welch, John J.}, issn = {1744957X}, journal = {Biology Letters}, number = {4}, publisher = {Royal Society of London}, title = {{The distribution of epistasis on simple fitness landscapes}}, doi = {10.1098/rsbl.2018.0881}, volume = {15}, year = {2019}, } @article{6470, abstract = {Investigating neuronal activity using genetically encoded Ca2+ indicators in behaving animals is hampered by inaccuracies in spike inference from fluorescent tracers. Here we combine two‐photon [Ca2+] imaging with cell‐attached recordings, followed by post hoc determination of the expression level of GCaMP6f, to explore how it affects the amplitude, kinetics and temporal summation of somatic [Ca2+] transients in mouse hippocampal pyramidal cells (PCs). The amplitude of unitary [Ca2+] transients (evoked by a single action potential) negatively correlates with GCaMP6f expression, but displays large variability even among PCs with similarly low expression levels. The summation of fluorescence signals is frequency‐dependent, supralinear and also shows remarkable cell‐to‐cell variability. We performed experimental data‐based simulations and found that spike inference error rates using MLspike depend strongly on unitary peak amplitudes and GCaMP6f expression levels. We provide simple methods for estimating the unitary [Ca2+] transients in individual weakly GCaMP6f‐expressing PCs, with which we achieve spike inference error rates of ∼5%. }, author = {Éltes, Tímea and Szoboszlay, Miklos and Szigeti, Margit Katalin and Nusser, Zoltan}, issn = {14697793}, journal = {Journal of Physiology}, number = {11}, pages = {2925–2947}, publisher = {Wiley}, title = {{Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca2+] transients in weakly GCaMP6f-expressing hippocampal pyramidal cells}}, doi = {10.1113/JP277681}, volume = {597}, year = {2019}, } @inproceedings{6493, abstract = {We present two algorithmic approaches for synthesizing linear hybrid automata from experimental data. Unlike previous approaches, our algorithms work without a template and generate an automaton with nondeterministic guards and invariants, and with an arbitrary number and topology of modes. They thus construct a succinct model from the data and provide formal guarantees. In particular, (1) the generated automaton can reproduce the data up to a specified tolerance and (2) the automaton is tight, given the first guarantee. Our first approach encodes the synthesis problem as a logical formula in the theory of linear arithmetic, which can then be solved by an SMT solver. This approach minimizes the number of modes in the resulting model but is only feasible for limited data sets. To address scalability, we propose a second approach that does not enforce to find a minimal model. The algorithm constructs an initial automaton and then iteratively extends the automaton based on processing new data. Therefore the algorithm is well-suited for online and synthesis-in-the-loop applications. The core of the algorithm is a membership query that checks whether, within the specified tolerance, a given data set can result from the execution of a given automaton. We solve this membership problem for linear hybrid automata by repeated reachability computations. We demonstrate the effectiveness of the algorithm on synthetic data sets and on cardiac-cell measurements.}, author = {Garcia Soto, Miriam and Henzinger, Thomas A and Schilling, Christian and Zeleznik, Luka}, booktitle = {31st International Conference on Computer-Aided Verification}, isbn = {9783030255398}, issn = {0302-9743}, keywords = {Synthesis, Linear hybrid automaton, Membership}, location = {New York City, NY, USA}, pages = {297--314}, publisher = {Springer}, title = {{Membership-based synthesis of linear hybrid automata}}, doi = {10.1007/978-3-030-25540-4_16}, volume = {11561}, year = {2019}, } @misc{6485, abstract = {Traditional concurrent programming involves manipulating shared mutable state. Alternatives to this programming style are communicating sequential processes (CSP) [1] and actor [2] models, which share data via explicit communication. Rendezvous channelis the common abstraction for communication between several processes, where senders and receivers perform a rendezvous handshake as a part of their protocol (senders wait for receivers and vice versa). Additionally to this, channels support the select expression. In this work, we present the first efficient lock-free channel algorithm, and compare it against Go [3] and Kotlin [4] baseline implementations.}, author = {Koval, Nikita and Alistarh, Dan-Adrian and Elizarov, Roman}, booktitle = {Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming}, isbn = {9781450362252}, location = {Washington, NY, United States}, pages = {417--418}, publisher = {ACM Press}, title = {{Lock-free channels for programming via communicating sequential processes}}, doi = {10.1145/3293883.3297000}, year = {2019}, }