Biophysically inspired rational design of structured chimeric substrates for DNAzyme cascade engineering.

The development of large-scale molecular computational networks is a promising approach to implementing logical decision making at the nanoscale, analogous to cellular signaling and regulatory cascades. DNA strands with catalytic activity (DNAzymes) are one means of systematically constructing molecular computation networks with inherent signal amplification. Linking multiple DNAzymes into a computational circuit requires the design of substrate molecules that allow a signal to be passed from one DNAzyme to another through programmed biochemical interactions. In this paper, we chronicle an iterative design process guided by biophysical and kinetic constraints on the desired reaction pathways and use the resulting substrate design to implement heterogeneous DNAzyme signaling cascades. A key aspect of our design process is the use of secondary structure in the substrate molecule to sequester a downstream effector sequence prior to cleavage by an upstream DNAzyme. Our goal was to develop a concrete substrate molecule design to achieve efficient signal propagation with maximal activation and minimal leakage. We have previously employed the resulting design to develop high-performance DNAzyme-based signaling systems with applications in pathogen detection and autonomous theranostics.

Visual displays that directly interface and provide read-outs of molecular states via molecular graphics processing units.

The monitoring of molecular systems usually requires sophisticated technologies to interpret nanoscale events into electronic-decipherable signals. We demonstrate a new method for obtaining read-outs of molecular states that uses graphics processing units made from molecular circuits. Because they are made from molecules, the units are able to directly interact with molecular systems. We developed deoxyribozyme-based graphics processing units able to monitor nucleic acids and output alphanumerical read-outs via a fluorescent display. Using this design we created a molecular 7-segment display, a molecular calculator able to add and multiply small numbers, and a molecular automaton able to diagnose Ebola and Marburg virus sequences. These molecular graphics processing units provide insight for the construction of autonomous biosensing devices, and are essential components for the development of molecular computing platforms devoid of electronics.

Signal propagation in multi-layer DNAzyme cascades using structured chimeric substrates.

Signal propagation through enzyme cascades is a critical component of information processing in cellular systems. Although such systems have potential as biomolecular computing tools, rational design of synthetic protein networks remains infeasible. DNA strands with catalytic activity (DNAzymes) are an attractive alternative, enabling rational cascade design through predictable base-pair hybridization principles. Multi-layered DNAzyme signaling and logic cascades are now reported. Signaling between DNAzymes was achieved using a structured chimeric substrate (SCS) that releases a downstream activator after cleavage by an upstream DNAzyme. The SCS can be activated by various upstream DNAzymes, can be coupled to DNA strand-displacement devices, and is highly resistant to interference from background DNA. This work enables the rational design of synthetic DNAzyme regulatory networks, with potential applications in biomolecular computing, biodetection, and autonomous theranostics.

Efficient RNAi-based gene family knockdown via set cover optimization.

OBJECTIVE: We address the problem of selecting an efficient set of initiator molecules (siRNAs) for RNA interference (RNAi)-based gene family knockdown experiments. Our goal is to select a minimal set of siRNAs that (a) cover a targeted gene family or a specified subset of it, (b) do not cover any untargeted genes, and (c) are individually highly effective at inducing knockdown. METHODS AND MATERIAL: We give two formalizations of the gene family knockdown problem. First, we show that the problem, minimizing the number of siRNAs required to knock down a family of genes, is NP-Hard via a reduction to the set cover problem. Second, we generalize the basic problem to incorporate additional biological constraints and optimality criteria. To solve the resulting set-cover variants, we modify the classical branch-and-bound algorithm to include some of these biological criteria. We find that in many typical cases these constraints reduce the search space enough that we are able to compute exact minimal siRNA covers within reasonable time. For larger cases, we propose a probabilistic greedy algorithm for finding minimal siRNA covers efficiently. We apply our methods to two different gene families, the FREP genes from Biomphalaria glabrata and the olfactory genes from Caenorhabditis elegans. RESULTS AND CONCLUSION: Our computational results on real biological data show that the probabilistic greedy algorithm produces siRNA covers as good as the branch-and-bound algorithm in most cases. Both algorithms return minimal siRNA covers with high predicted probability that the selected siRNAs will be effective at inducing knockdown. We also examine the role of "off-target" interactions: the constraint of avoiding covering untargeted genes can, in some cases, substantially increase the complexity of the resulting solution. Overall, we find that in many common cases our approach significantly reduces the number of siRNAs required in gene family knockdown experiments, as compared to knocking down genes independently.