Yaakov Benenson

An autonomous molecular computer for logical control of gene expression

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

Programmable and autonomous computing machine made of biomolecules.

Devices that convert information from one form into another according to a definite procedure are known as automata. One such hypothetical device is the universal Turing machine, which stimulated work leading to the development of modern computers. The Turing machine and its special cases, including finite automata, operate by scanning a data tape, whose striking analogy to information-encoding biopolymers inspired several designs for molecular DNA computers. Laboratory-scale computing using DNA and human-assisted protocols has been demonstrated, but the realization of computing devices operating autonomously on the molecular scale remains rare. Here we describe a programmable finite automaton comprising DNA and DNA-manipulating enzymes that solves computational problems autonomously. The automatons hardware consists of a restriction nuclease and ligase, the software and input are encoded by double-stranded DNA, and programming amounts to choosing appropriate software molecules. Upon mixing solutions containing these components, the automaton processes the input molecule via a cascade of restriction, hybridization and ligation cycles, producing a detectable output molecule that encodes the automatons final state, and thus the computational result. In our implementation 1012 automata sharing the same software run independently and in parallel on inputs (which could, in principle, be distinct) in 120 μl solution at room temperature at a combined rate of 109 transitions per second with a transition fidelity greater than 99.8%, consuming less than 10-10 W.

Multi-Input RNAi-Based Logic Circuit for Identification of Specific Cancer Cells

A synthetic biomolecular circuit identifies abnormal cell states by the integration of multiple endogenous microRNA inputs. Engineered biological systems that integrate multi-input sensing, sophisticated information processing, and precisely regulated actuation in living cells could be useful in a variety of applications. For example, anticancer therapies could be engineered to detect and respond to complex cellular conditions in individual cells with high specificity. Here, we show a scalable transcriptional/posttranscriptional synthetic regulatory circuit—a cell-type “classifier”—that senses expression levels of a customizable set of endogenous microRNAs and triggers a cellular response only if the expression levels match a predetermined profile of interest. We demonstrate that a HeLa cancer cell classifier selectively identifies HeLa cells and triggers apoptosis without affecting non-HeLa cell types. This approach also provides a general platform for programmed responses to other complex cell states.

DNA molecule provides a computing machine with both data and fuel

The unique properties of DNA make it a fundamental building block in the fields of supramolecular chemistry, nanotechnology, nano-circuits, molecular switches, molecular devices, and molecular computing. In our recently introduced autonomous molecular automaton, DNA molecules serve as input, output, and software, and the hardware consists of DNA restriction and ligation enzymes using ATP as fuel. In addition to information, DNA stores energy, available on hybridization of complementary strands or hydrolysis of its phosphodiester backbone. Here we show that a single DNA molecule can provide both the input data and all of the necessary fuel for a molecular automaton. Each computational step of the automaton consists of a reversible software molecule/input molecule hybridization followed by an irreversible software-directed cleavage of the input molecule, which drives the computation forward by increasing entropy and releasing heat. The cleavage uses a hitherto unknown capability of the restriction enzyme FokI, which serves as the hardware, to operate on a noncovalent software/input hybrid. In the previous automaton, software/input ligation consumed one software molecule and two ATP molecules per step. As ligation is not performed in this automaton, a fixed amount of software and hardware molecules can, in principle, process any input molecule of any length without external energy supply. Our experiments demonstrate 3 × 1012 automata per μl performing 6.6 × 1010 transitions per second per μl with transition fidelity of 99.9%, dissipating about 5 × 10−9 W/μl as heat at ambient temperature.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network‐coding genes. Gene product levels could potentially be decoupled from these changes via built‐in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post‐transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post‐transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.

Rationally designed logic integration of regulatory signals in mammalian cells

Molecular-level information processing1,2, or computing, is essential for ‘smart’ in vivo nanosystems. Natural molecular computing, such as messenger RNA (mRNA) synthesis regulation by special proteins called transcription factors (TFs)3,4, may inspire engineered systems leading to the next generation of nanobiotechnological and nanomedical applications. Synthetic pathways5–15 have already implemented logical control of mRNA levels by certain TF combinations. Here we show an alternative approach toward general-purpose control of mRNA and protein levels by logic integration of transcription factor input signals in mammalian cells. The factors regulate synthetic genes coding for small regulatory RNAs – microRNAs – that in turn control mRNA of interest (i.e., output) via RNA interference pathway. Simple nature of these modular interactions allows in theory to implement any arbitrary logic relation between the TFs and the output16. We construct, test, and optimize increasingly complex circuits with up to three TF inputs, establishing a platform for in-vivo molecular computing.

Logic integration of mRNA signals by an RNAi-based molecular computer

Synthetic in vivo molecular ‘computers’ could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that ‘transduce’ mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi ‘computational’ module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting.

Digital switching in a biosensor circuit via programmable timing of gene availability

Transient delivery of gene circuits is required in many potential applications of synthetic biology, yet pre-steady-state processes that dominate this delivery route pose significant challenges for robust circuit deployment. Here we show that site-specific recombinases can rectify undesired effects by programmable timing of gene availability in multi-gene circuits. We exemplify the concept with a proportional sensor for endogenous microRNA and show dramatic reduction in its ground state leakage thanks to desynchronization of circuit’s repressor components and their repression target. The new sensors display dynamic range of up to 1000-fold compared to 20-fold in the standard configuration. We applied the approach to classify cell types based on miRNA expression profile and measured > 200-fold output differential between positively- and negatively-identified cells. We also showed major improvement of specificity with cytotoxic output. Our study opens new venues in gene circuit design via judicious temporal control of circuits’ genetic makeup.

Highly modular bow-tie gene circuits with programmable dynamic behaviour

Synthetic gene circuits often require extensive mutual optimization of their components for successful operation, while modular and programmable design platforms are rare. A possible solution lies in the “bow-tie” architecture, which stipulates a focal component - a “knot” - uncoupling circuits’ inputs and outputs, simplifying component swapping, and introducing additional layer of control. Here we construct, in cultured human cells, synthetic bow-tie circuits that transduce microRNA inputs into protein outputs with independently programmable logical and dynamic behavior. The latter is adjusted via two different knot configurations: a transcriptional activator causing the outputs to track input changes reversibly, and a recombinase-based cascade, converting transient inputs into permanent actuation. We characterize the circuits in HEK293 cells, confirming their modularity and scalability, and validate them using endogenous microRNA inputs in additional cell lines. This platform can be used for biotechnological and biomedical applications in vitro, in vivo, and potentially in human therapy.

Biocomputing: DNA computes a square root

Complex molecular circuits with reliable digital behaviour can be created using DNA strands.