Engineering orthogonal dual transcription factors for multi-input synthetic promoters

Synthetic biology has seen an explosive growth in the capability of engineering artificial gene circuits from transcription factors (TFs), particularly in bacteria. However, most artificial networks still employ the same core set of TFs (for example LacI, TetR and cI). The TFs mostly function via repression and it is difficult to integrate multiple inputs in promoter logic. Here we present to our knowledge the first set of dual activator-repressor switches for orthogonal logic gates, based on bacteriophage λ cI variants and multi-input promoter architectures. Our toolkit contains 12 TFs, flexibly operating as activators, repressors, dual activator–repressors or dual repressor–repressors, on up to 270 synthetic promoters. To engineer non cross-reacting cI variants, we design a new M13 phagemid-based system for the directed evolution of biomolecules. Because cI is used in so many synthetic biology projects, the new set of variants will easily slot into the existing projects of other groups, greatly expanding current engineering capacities

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Background and objectives. The computational biology area is growing up over the years. The interest in researching and developing computational tools for the acquisition, storage, organization, analysis, and visualization of biological data generates the need to create new hardware architectures and new software tools that allow processing big data in acceptable times. In this sense, heterogeneous computing takes an important role in providing solutions but at the same time generates new challenges for developers in relation to the impossibility of porting source code between different architectures. Methods. Intel has recently introduced oneAPI, a new unified programming environment that allows code developed in the SYCL-based Data Parallel C++ (DPC++) language to be run on different devices such as CPUs, GPUs, and FPGAs, among others. Due to the large amount of CUDA software in the field of bioinformatics, this paper presents the migration process of the SW\# suite, a biological sequence alignment tool developed in CUDA, to DPC++ through the oneAPI compatibility tool dpc (recently renowned as SYCLomatic). Results. SW\# has been completely migrated with a small programmer intervention in terms of hand-coding. Moreover, it has been possible to port the migrated code between different architectures (considering different target platforms and vendors), with no noticeable performance degradation. Conclusions. The SYCLomatic tool presented a great performance-portability rate. SYCL and Intel oneAPI can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes

Design of a bistable switch to control cellular uptake

International audienceBistable switches are widely used in synthetic biology to trigger cellular functions in response to environmental signals. All bistable switches developed so far, however, control the expression of target genes without access to other layers of the cellular machinery. Here, we propose a bistable switch to control the rate at which cells take up a metabolite from the environment. An uptake switch provides a new interface to command metabolic activity from the extracellular space and has great potential as a building block in more complex circuits that coordinate pathway activity across cell cultures, allocate metabolic tasks among different strains or require cell-to-cell communication with metabolic signals. Inspired by uptake systems found in nature, we propose to couple metabolite import and utilization with a genetic circuit under feedback regulation. Using mathematical models and analysis, we determined the circuit architectures that produce bistability and obtained their design space for bistability in terms of experimentally tuneable parameters. We found an activation–repression architecture to be the most robust switch because it displays bistability for the largest range of design parameters and requires little fine-tuning of the promoters' response curves. Our analytic results are based on on–off approximations of promoter activity and are in excellent qualitative agreement with simulations of more realistic models. With further analysis and simulation, we established conditions to maximize the parameter design space and to produce bimodal phenotypes via hysteresis and cell-to-cell variability. Our results highlight how mathematical analysis can drive the discovery of new circuits for synthetic biology, as the proposed circuit has all the hallmarks of a toggle switch and stands as a promising design to control metabolic phenotypes across cell cultures

Time-Shifted Rationality and the Law of Law\u27s Leverage: Behavioral Economics Meets Behavioral Biology

A flood of recent scholarship explores legal implications of seemingly irrational behaviors by invoking cognitive psychology and notions of bounded rationality. In this article, I argue that advances in behavioral biology have largely overtaken existing notions of bounded rationality, revealing them to be misleadingly imprecise - and rooted in outdated assumptions that are not only demonstrably wrong, but also wrong in ways that have material implications for subsequent legal conclusions. This can be remedied. Specifically, I argue that behavioral biology offers three things of immediate use. First, behavioral biology can lay a foundation for both revising bounded rationality and fashioning a solid theoretical basis for understanding and predicting many human irrationalities. Second, a principle we may derive from the fundamentals of behavioral biology, which I term time-shifted rationality, can help us to usefully disentangle things currently lumped together under the label of bounded rationality. Doing so suggests that some seeming irrationalities are not, in fact, the product of conventional bounded rationality but are instead the product of a very different phenomenon. As a consequence and by-product of this analysis, it is possible to reconcile some of the supposed irrationalities with an existing rationality framework in a new, more satisfying, and more useful way. Third, behavioral biology affords the raw material for deriving a new principle, which I term the law of law\u27s leverage, that can help us to better understand and predict the effects of law on human behavior. Specifically, it can help us to anticipate the comparative sensitivities of various human behaviors to legal changes in incentives. That is, it enables us to anticipate differences in the slopes of demand curves for various law-relevant behaviors. This law of law\u27s leverage therefore can afford us new, coherent, and systematic power in predicting the comparative costs, to society, of attempting to change behaviors through legal means. And the principle also provides a new and powerful tool for explaining and predicting many of the existing and future architectures of legal systems

From Molecular Recognition to the “Vehicles” of Evolutionary Complexity: An Informational Approach

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements int the majority of test environments. Some of the remaining tested modifications were detrimental, thought most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges

Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks

A deep neural network is a non-spiking artificial neural network which uses multiple structured layers to extract features from the input. Spiking neural networks are another type of artificial neural network which closely mimic biology with time dependent pulses to transmit information. Whetstone is a training algorithm for spiking deep neural networks. It modifies the back propagation algorithm, typically used in deep learning, to train a spiking deep neural network, by converting the activation function found in deep neural networks into a threshold used by a spiking neural network. This work converts a spiking deep neural network trained from Whetstone to a traditional spiking neural network in the TENNLab framework. This conversion decomposes the dot product operation found in the convolutional layer of spiking deep neural networks to synapse connections between neurons in traditional spiking neural networks. The conversion also redesigns the neuron and synapse structure in the convolutional layer to trade time for space. A new architecture is created in the TENNLab framework using traditional spiking neural networks, which behave the same as the spiking deep neural network trained by Whetstone before conversion. This new architecture verifies the converted spiking neural network behaves the same as the original spiking deep neural network. This work can convert networks to run on other architectures from TENNLab, and this allows networks from those architectures to be trained with back propagation from Whetstone. This expands the variety of training techniques available to the TENNLab architectures