Computer-aided biochemical programming of synthetic microreactors as diagnostic devices

Computational capabilities of a multicellular reservoir computing system

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

Advances in single cell technologies in immunology

The immune system is composed of heterogeneous populations of immune cells that regulate physiological processes and protect organisms against diseases. Single cell technologies have been used to assess immune cell responses at the single cell level, which are crucial for identifying the causes of diseases and elucidating underlying biological mechanisms to facilitate medical therapy. In the present review we first discuss the most recent advances in the development of single cell technologies to investigate cell signaling, cell-cell interactions and cell migration. Each technology's advantages and limitations and its applications in immunology are subsequently reviewed. The latest progress toward commercialization, the remaining challenges and future perspectives for single cell technologies in immunology are also briefly discussed.

The Boolean logic tree of molecular self-assembly system based on cobalt oxyhydroxide nanoflakes for three-state logic computation, sensing and imaging of pyrophosphate in living cells and in vivo

Sensing of pyrophosphate (PPi) is helpful to better understand many life processes and diagnose various early-stage diseases. However, many traditional reported methods based on artificial receptors for sensing of PPi exhibit some disadvantages including difficulties in designing appropriate binding sites and complicated multi-step assembly/functionalization. Thus, it is significantly important and a big challenge to know how to use a simple molecular self-assembly or an interaction system to solve the above-mentioned limits to achieve the quantitative analysis of specific substances in the system. Based on the natural connection and similarity (such as stimulus responsiveness) between sensing and logic computing, in this study, the Boolean logic tree of molecular self-assembly system based on the cobalt oxyhydroxide (CoOOH) nanoplatform is constructed and applied to organize and connect "plug and play" molecular events (fluorescent dye, acridine orange and anion, PPi). By using molecules as inputs and the corresponding fluorescence signal as the output, the CoOOH-based molecular self-assembly system can be programmed for three-input fluorescent Boolean logic computation, fluorescent three-state logic computation, detection of PPi (linear range from 50 to 6400 nM with a detection limit of 20 nM) and even for imaging in living cancer cells and in vivo (in systems such as Zebrafish and Carassius auratus). Our approach adds a new dimension for expanding molecular logic computing and sensing systems, which will not only provide more opportunities for developing novel logic computing paradigms, but also be helpful in promoting the development and applications of intelligent molecular computing and sensing systems.

Synthesis and degradation of FtsZ quantitatively predict the first cell division in starved bacteria

In natural environments, microbes are typically non-dividing and gauge when nutrients permit division. Current models are phenomenological and specific to nutrient-rich, exponentially growing cells, thus cannot predict the first division under limiting nutrient availability. To assess this regime, we supplied starving Escherichia coli with glucose pulses at increasing frequencies. Real-time metabolomics and microfluidic single-cell microscopy revealed unexpected, rapid protein, and nucleic acid synthesis already from minuscule glucose pulses in non-dividing cells. Additionally, the lag time to first division shortened as pulsing frequency increased. We pinpointed division timing and dependence on nutrient frequency to the changing abundance of the division protein FtsZ. A dynamic, mechanistic model quantitatively relates lag time to FtsZ synthesis from nutrient pulses and FtsZ protease-dependent degradation. Lag time changed in model-congruent manners, when we experimentally modulated the synthesis or degradation of FtsZ. Thus, limiting abundance of FtsZ can quantitatively predict timing of the first cell division.