Nonlinear decision-making with enzymatic neural networks.

Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks1,2. Non-enzymatic networks could in principle support neuromorphic architectures, and seminal proofs-of-principle have been reported3,4. However, leakages (that is, the unwanted release of species), as well as issues with sensitivity, speed, preparation and the lack of strong nonlinear responses, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network remain elusive (for example, the partitioning of a concentration space into regions that cannot be linearly separated). Here we introduce DNA-encoded enzymatic neurons with tuneable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of a neuron to compute various majority functions on 10 bits. We then compose neurons into a two-layer network and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.

Automated exploration of DNA-based structure self-assembly networks.

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.

High-resolution mapping of bifurcations in nonlinear biochemical circuits.

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.

Detection of M2 antimitochondrial antibodies by dot blot assay is more specific than by enzyme linked immunosorbent assay.

AIMS: The objective of our study was, in one hand, to determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ELISA and dot blot assay to investigate IgG M2 antimitochondrial antibodies (M2 AMA) and, on the other hand, to compare these results with those of indirect immunofluorescence technique (IIF). METHODS: Sera from patients suffering from primary biliary cirrhosis (PBC) (n=55), systemic lupus erythematosus (n=21), celiac disease (n=30) and blood donors (n=75) were analyzed. M2 AMA were detected by ELISA and dot blot using pyruvate dehydrogenase purified from porcine heart and by IIF on cryostat sections of rat liver-kidney-stomach. RESULTS: IIF was more sensitive (98%) than ELISA (93%) and dot blot (91%). The specificity of AMA for PBC using IIF, ELISA and dot blot reached 100%, 92% and 100%, respectively. The PPV of IIF, ELISA and dot blot was 100%, 93% and 100%, respectively. The NPV was 98% for IIF, 92% for ELISA and 91% for dot blot. CONCLUSION: Dot blot, using purified pyruvate dehydrogenase, had a higher specificity than ELISA and may be useful in confirming the specificity of AMA in cases of doubt with IIF.