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This tutorial paper focuses on the design, simulation, and optimization of polymerase–exonuclease–nickase (PEN) toolbox systems. The PEN toolbox is a molecular programming framework that relies on the interaction between DNA molecules and enzymes to produce highly non-linear dynamic systems. The flip side of that expressivity is that the design of systems relies heavily on trial-and-error, thus calling for computer assistance. The paper includes a description of the high-level application programming interface of DACCAD, the main simulation library for PEN toolbox systems, and introduces methods to extend the existing software to match the designer’s needs. Several examples are provided to illustrate that approach.

In this article, we study the coupling of a collection of molecular oscillators, called repressilators, interacting indirectly through enzymatic saturation. We extended a measure of autocorrelation to identify the period of the whole system and to detect coupling behaviors. We explored the parameter space of concentrations of molecular species in each oscillator versus enzymatic saturation, and observed regions of uncoupled, partially, or fully coupled systems. In particular, we found a region that provided a sharp transition between no coupling, two coupled oscillators, and full coupling. In practical applications, signals from the environment can directly affect parameters such as local enzymatic saturation, and thus switch the system from a coupled to an uncoupled regime and vice-versa. Our parameter exploration can be used to guide the design of complex molecular systems, such as active materials or molecular robot controllers.

Finding DNA sequences capable of folding into specific nanostructures is a hard problem, as it involves very large search spaces and complex nonlinear dynamics. Typical methods to solve it aim to reduce the search space by minimizing unwanted interactions through restrictions on the design (e.g. staples in DNA origami or voxel-based designs in DNA Bricks). Here, we present a novel methodology that aims to reduce this search space by identifying the relevant properties of a given assembly system to the emergence of various families of structures (e.g. simple structures, polymers, branched structures). For a given set of DNA strands, our approach automatically finds chemical reaction networks (CRNs) that generate sets of structures exhibiting ranges of specific user-specified properties, such as length and type of structures or their frequency of occurrence. For each set, we enumerate the possible DNA structures that can be generated through domain-level interactions, identify the most prevalent structures, find the best-performing sequence sets to the emergence of target structures, and assess CRNs' robustness to the removal of reaction pathways. Our results suggest a connection between the characteristics of DNA strands and the distribution of generated structure families.

Background Wearable activity trackers are regarded as a new opportunity to deliver health promotion interventions. Indeed, while the prediction of active behaviors is currently primarily relying on the processing of accelerometer sensor data, the emergence of smart clothes with multi-sensing capacities is offering new possibilities. Algorithms able to process data from a variety of smart devices and classify daily life activities could therefore be of particular importance to achieve a more accurate evaluation of physical behaviors. This study aims to (1) develop an activity recognition algorithm based on the processing of plantar pressure information provided by a smart-shoe prototype and (2) to determine the optimal hardware and software configurations. Method Seventeen subjects wore a pair of smart-shoe prototypes composed of plantar pressure measurement insoles, and they performed the following nine activities: sitting, standing, walking on a flat surface, walking upstairs, walking downstairs, walking up a slope, running, cycling, and completing office work. The insole featured seven pressure sensors. For each activity, at least four minutes of plantar pressure data were collected. The plantar pressure data were cut in overlapping windows of different lengths and 167 features were extracted for each window. Data were split into training and test samples using a subject-wise assignment method. A random forest model was trained to recognize activity. The resulting activity recognition algorithms were evaluated on the test sample. A multi hold-out procedure allowed repeating the operation with 5 different assignments. The analytic conditions were modulated to test (1) different window lengths (1–60 seconds), (2) some selected sensor configurations and (3) different numbers of data features. Results A window length of 20 s was found to be optimum and therefore used for the rest of the analysis. Using all the sensors and all 167 features, the smart shoes predicted the activities with an average success of 89%. “Running” demonstrated the highest sensitivity (100%). “Walking up a slope” was linked with the lowest performance (63%), with the majority of the false negatives being “walking on a flat surface” and “walking upstairs.” Some 2- and 3-sensor configurations were linked with an average success rate of 87%. Reducing the number of features down to 20 does not alter significantly the performance of the algorithm. Conclusion High-performance human behavior recognition using plantar pressure data only is possible. In the future, smart-shoe devices could contribute to the evaluation of daily physical activities. Minimalist configurations integrating only a small number of sensors and computing a reduced number of selected features could maintain a satisfying performance. Future experiments must include a more heterogeneous population.

DNA nanotechnology offers a fine control over biochemistry by programming chemical reactions in DNA templates. Coupled to microfluidics, it has enabled DNA-based reaction-diffusion microsystems with advanced spatio-temporal dynamics such as traveling waves. The Finite Element Method (FEM) is a standard tool to simulate the physics of such systems where boundary conditions play a crucial role. However, a fine discretization in time and space is required for complex geometries (like sharp corners) and highly nonlinear chemistry. Graphical Processing Units (GPUs) are increasingly used to speed up scientific computing, but their application to accelerate simulations of reaction-diffusion in DNA nanotechnology has been little investigated. Here we study reaction-diffusion equations (a DNA-based predator-prey system) in a tortuous geometry (a maze), which was shown experimentally to generate subtle geometric effects. We solve the partial differential equations on a GPU, demonstrating a speedup of &sim, 100 over the same resolution on a 20 cores CPU.

The MAP-Elites algorithm can efficiently explore reinforcement learning problems and optimize collections of solutions over user-defined ranges of possible behaviors. However, MAP-Elites can be difficult to scale to highly dimensional problems, such as the optimization of large deep neural networks. Traditional deep reinforcement can train agents with a complex network model by relying on human-designed extrinsic rewards. In complex problems, this translates into reward landscapes that are extremely sparse and hard to explore. This has led to the development of curiosity-driven reinforcement learning algorithms that use intrinsic rewards to continuously optimize for novel policies. While this approach encourages exploration, it remains to be seen if it can be used similarly to MAP-Elites to search for a collection of highly diverse solutions.

We design and implement a temperature-based input mechanism for molecular reservoir computing. Using temperature allows us to interact with the system while keeping it chemically closed, a crucial ...

Artificial immune systems are bio-inspired algorithms capable of optimizing highly difficult objective functions, with multi-modal and deceptive landscapes. In order to do so, they require large evaluation budgets. Here, we describe how to reduce the number of function evaluations of the Opt-IA artificial immune system algorithm through the use of a surrogate model based on Gaussian Processes. Furthermore, we describe an exploration strategy, based on the Sobol sequence, to bootstrap the optimization process. We show that such methods increase the convergence speed of Opt-IA on several functions on the noiseless BBOB testbed. Our method outperforms Opt-IA on most tested functions for low-precision targets.

In this review, we describe some of the central philosophical issues facing origins-of-life research and provide a targeted history of the developments that have led to the multidisciplinary field of origins-of-life studies. We outline these issues and developments to guide researchers and students from all fields. With respect to philosophy, we provide brief summaries of debates with respect to (1) definitions (or theories) of life, what life is and how research should be conducted in the absence of an accepted theory of life, (2) the distinctions between synthetic, historical, and universal projects in origins-of-life studies, issues with strategies for inferring the origins of life, such as (3) the nature of the first living entities (the “bottom up” approach) and (4) how to infer the nature of the last universal common ancestor (the “top down” approach), and (5) the status of origins of life as a science. Each of these debates influences the others. Although there are clusters of researchers that agree on some answers to these issues, each of these debates is still open. With respect to history, we outline several independent paths that have led to some of the approaches now prevalent in origins-of-life studies. These include one path from early views of life through the scientific revolutions brought about by Linnaeus (von Linn.), Wöhler, Miller, and others. In this approach, new theories, tools, and evidence guide new thoughts about the nature of life and its origin. We also describe another family of paths motivated by a” circularity” approach to life, which is guided by such thinkers as Maturana & Varela, Gánti, Rosen, and others. These views echo ideas developed by Kant and Aristotle, though they do so using modern science in ways that produce exciting avenues of investigation. By exploring the history of these ideas, we can see how many of the issues that currently interest us have been guided by the contexts in which the ideas were developed. The disciplinary backgrounds of each of these scholars has influenced the questions they sought to answer, the experiments they envisioned, and the kinds of data they collected. We conclude by encouraging scientists and scholars in the humanities and social sciences to explore ways in which they can interact to provide a deeper understanding of the conceptual assumptions, structure, and history of origins-of-life research. This may be useful to help frame future research agendas and bring awareness to the multifaceted issues facing this challenging scientific question., This project/publication was supported by the ELSI Origins Network (EON), which is supported by a grant from the John Templeton Foundation. T.F.’s work on this article was supported by an ELSI Origins Network (EON) Long-Term Visitor Award and by an UNAM-DGAPA-PAPIIT project (IA104717).

We are interested in programming a swarm of molecular robots that can perform self-assembly to form specific shapes at a specific location. Programming such robot swarms is challenging for two reasons. First, the goal is to optimize both the parameters and the structure of chemical reaction networks. Second, the search space is both high-dimensional and deceptive. In this paper, we show that MAP-Elites, an algorithm that searches for both high-performing and diverse solutions, outperforms previous state-of-the-art optimization methods.

Various artificial molecular devices, including some made of DNA or RNA, have been developed to date. The next step in this area of research is to develop an integrated system from such molecular devices. A molecular robot consists of sensors, computers, and actuators, all made of molecular devices, and reacts autonomously to its environment by observing the environment, making decisions with its computers, and performing actions upon the environment. Molecular computers should thus be the intelligent controllers of such molecular robots. Such controllers can naturally be regarded as hybrid systems because the environment, the robot, and the controller are all state transition systems having discrete and continuous states and transitions. For modeling and designing hybrid systems, formal frameworks, such as hybrid automata, are commonly used. In this perspective paper, we examine how molecular controllers can be modeled as hybrid automata and how they can be realized in a molecular robot. We first summarize the requirements for such molecular controllers and examine existing frameworks of DNA computing with respect to these requirements. We then show the possibility of combining existing frameworks of DNA computing to implement a sample hybrid controller for a molecular robot.

In this paper, we apply the Polymerase-Exonuclease-Nickase Dynamic Network Assembly (PEN DNA) toolbox, a modular framework for molecular computing, to reservoir computing. While reservoir computing...

We introduce a specialized module for molecular programming systems, designed to accelerate the comparison of species concentrations. Like in electronics, co-processors are important to increase the speed of crucial tasks. Concentration comparison is a central operation in DNA computing, similar to variable comparison in its electronic equivalent. This work represents the first attempt to make multiple comparison in a single step, while keeping the mechanism as optimized as possible with respect to enzymatic load. We first demonstrate a possible implementation of the system, showing that there is no theoretical barrier to the design. In particular, it allows enough freedom in the sequence design to work around potential cross-talks in the system. We then add our co-processor to a specific DNA computing paradigm, the PEN toolbox, and use it to implement an optimized tristable circuit and compare its performance to the standard design approach existing in the literature. Our design shows improved time response and lower impact on the saturation of key enzymes, making it a useful module for designing large systems.

International audience; Biochemical systems in which multiple components take part in a given reaction are of increasing interest. Because the interactions between these different components are complex and difficult to predict from basic reaction kinetics, it is important to test for the effect of variations in the concentration for each reagent in a combinatorial manner. For example, in PCR, an increase in the concentration of primers initially increases template amplification, but large amounts of primers result in primer-dimer by-products that inhibit the amplification of the template. Manual titration of biochemical mixtures rapidly becomes costly and laborious, forcing scientists to settle for suboptimal concentrations. Here we present a droplet-based microfluidics platform for mapping of the concentration space of up to three reaction components followed by detection with a fluorescent readout. The concentration of each reaction component is read through its internal standard (barcode), which is fluorescent but chemically orthogonal. We describe in detail the workflow, which comprises the following: (i) production of the microfluidics chips, (ii) preparation of the biochemical mixes, (iii) their mixing and compartmentalization into water-in-oil emulsion droplets via microfluidics, (iv) incubation and imaging of the fluorescent barcode and reporter signals by fluorescence microscopy and (v) image processing and data analysis. We also provide recommendations for choosing the appropriate fluorescent markers, programming the pressure profiles and analyzing the generated data. Overall, this platform allows a researcher with a few weeks of training to acquire ∼10,000 data points (in a 1D, 2D or 3D concentration space) over the course of a day from as little as 100-1,000 μl of reaction mix.

International audience; This paper deals with the programmability of a swarm of bio-micro-robots in order to display self-assembling behaviors into specific shapes. We consider robots that are DNA-functionalized micro-beads capable of sensing and expressing signals as well as self-assembling. We describe an in vitro experimentation with a million of micro-beads conditionally aggregating into clusters. Using a realistic simulation, we then address the question of how to automatically design the reaction networks that define the micro-robots' behavior, to self-assemble into a specific shape at a specific location. We use bioNEAT, an instantiation of the famous NEAT algorithm capable of handling chemical reaction networks, and CMA-ES to optimize the behavior of each micro-bead. As in swarm robotics, each micro-bead shares the same behavioral rules and the general outcome depends on interactions between neighbors and with the environment. Results obtained on four different target functions show that solutions optimized with evolutionary algorithms display efficient self-assembling behaviors, improving over pure hand-designed networks provided by an expert after a week-long trials and errors search. In addition, we show that evolved solutions are able to self-repair after damage, which is a critical property for smart materials.

International audience; Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes—key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap Appendix I Appendix II Supplementary Materials References

We propose a metric which can be used to compute the amount of heritable variation enabled by a given dynamical system. A distribution of selection pressures is used such that each pressure selects a particular fixed point via competitive exclusion in order to determine the corresponding distribution of potential fixed points in the population dynamics. This metric accurately detects the number of species present in artificially prepared test systems, and furthermore can correctly determine the number of heritable sets in clustered transition matrix models in which there are no clearly defined genomes. Finally, we apply our metric to the GARD model and show that it accurately reproduces prior measurements of the model's heritability., Comment: 12 pages, 7 figures