Electrical Capacitance Tomography of Cell Cultures on a CMOS Microelectrode Array.

Electrical capacitance tomography (ECT) can be used to predict information about the interior volume of an object based on measured capacitance at its boundaries. Here, we present a microscale capacitance tomography system with a spatial resolution of 10 microns using an active CMOS microelectrode array. We introduce a deep learning model for reconstructing 3-D volumes of cell cultures using the boundary capacitance measurements acquired from the sensor array, which is trained using a multi-objective loss function that combines a pixel-wise loss function, a distribution-based loss function, and a region-based loss function to improve model's reconstruction accuracy. The multi-objective loss function enhances the model's reconstruction accuracy by 3.2% compared to training only with a pixel-wise loss function. Compared to baseline computational methods, our model achieves an average of 4.6% improvement on the datasets evaluated. We demonstrate our approach on experimental datasets of bacterial biofilms, showcasing the system's ability to resolve microscopic spatial features of cell cultures in three dimensions. Microscale capacitance tomography can be a low-cost, low-power, label-free tool for 3-D imaging of biological samples.

Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling.

Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of "0" and "1." Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as digital circuits and neural networks. We present the building blocks needed to build complete digital circuits using acids and bases for dual-rail encoding data values as complementary pairs, including a set of primitive logic functions that are widely applicable to molecular computation. We demonstrate how to implement neural network classifiers and some classes of digital circuits with acid-base reactions orchestrated by a robotic fluid handling device. We validate the neural network experimentally on a number of images with different formats, resulting in a perfect match to the in-silico classifier. Additionally, the simulation of our acid-base classifier matches the results of the in-silico classifier with approximately 99% similarity.

Microscale 3-D Capacitance Tomography with a CMOS Sensor Array.

Electrical capacitance tomography (ECT) is a non-optical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.

Leveraging autocatalytic reactions for chemical domain image classification.

Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory.

Principles of Information Storage in Small-Molecule Mixtures.

Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes with different practical constraints on total capacity. We experimentally demonstrate kilobyte-scale information storage in mixtures of small synthetic molecules, and we consider some of the new perspectives that will be necessary to harness the information capacity available from the vast non-genomic chemical space.

Multicomponent molecular memory.

Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital data is written using robotically synthesized libraries of Ugi products, and the files are read back using mass spectrometry. We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for single reads, without library purification. In addition to improved scaling of non-biological molecular data storage, these demonstrations offer an information-centric perspective on the high-throughput synthesis and screening of small-molecule libraries.

Scalable heuristics for design of DNA probe arrays.

Design of DNA arrays for very large-scale immobilized polymer synthesis (VLSIPS) (Fodor et al., 1991) seeks to minimize effects of unintended illumination during mask exposure steps. Hannenhalli et al. (2002) formulate this requirement as the Border Minimization Problem and give an algorithm for placement of probes at array sites under the assumption that the array synthesis is synchronous; i.e., nucleotides are synthesized in a periodic sequence (ACGT)(k) and every probe grows by exactly one nucleotide with every group of four masks. Drawing on the analogy with VLSI placement, in this paper we describe and experimentally validate the engineering of several scalable, high-quality placement heuristics for both synchronous and asynchronous DNA array design. We give empirical results on both randomly generated and industry test cases confirming the scalability and improved solution quality enjoyed by our methods. In general, our techniques improve on state-of-the-art industrial results by over 4% and surpass academically published results by up to 35%. Finally, we give lower bounds that offer insights into the amount of available further improvements.