Abstract-DNA nanotechnology uses the information processing capabilities of nucleic acids to design self-assembling, programmable structures and devices at the nanoscale. Devices developed to date have been programmed to implement logic circuits and neural networks, capture or release specific molecules, and traverse molecular tracks and mazes.Here we investigate the use of requirements engineering methods to make DNA nanotechnology more productive, predictable, and safe. We use goal-oriented requirements modeling to identify, specify, and analyze a product family of DNA nanodevices, and we use PRISM model checking to verify both common properties across the family and properties that are specific to individual products. Challenges to doing requirements engineering in this domain include the errorprone nature of nanodevices carrying out their tasks in the probabilistic world of chemical kinetics, the fact that roughly a nanomole (a 1 followed by 14 0s) of devices are typically deployed at once, and the difficulty of specifying and achieving modularity in a realm where devices have many opportunities to interfere with each other. Nevertheless, our results show that requirements engineering is useful in DNA nanotechnology and that leveraging the similarities among nanodevices in the product family improves the modeling and analysis by supporting reuse.

Dynamic systems in DNA nanotechnology are often programmed using a chemical reaction network (CRN) model as an intermediate level of abstraction. In this paper, we design and analyze a CRN model of a watchdog timer, a device commonly used to monitor the health of a safety critical system. Our process uses incremental design practices with goal-oriented requirements engineering, software verification tools, and custom software to help automate the software engineering process. The watchdog timer is comprised of three components: an absence detector, a threshold filter, and a signal amplifier. These components are separately designed and verified, and only then composed to create the molecular watchdog timer. During the requirements-design iterations, simulation, model checking, and analysis are used to verify the system. Using this methodology several incomplete requirements and design flaws were found, and the final verified model helped determine specific parameters for biological experiments.

We present a uniform method for translating an arbitrary nondeterministic finite automaton (NFA) into a deterministic mass action input/output chemical reaction network (I/O CRN ) that simulates it. The I/O CRN receives its input as a continuous time signal consisting of concentrations of chemical species that vary to represent the NFA's input string in a natural way. The I/O CRN exploits the inherent parallelism of chemical kinetics to simulate the NFA in real time with a number of chemical species that is linear in the size of the NFA. We prove that the simulation is correct and that it is robust with respect to perturbations of the input signal, the initial concentrations of species, the output (decision), and the rate constants of the reactions of the I/O CRN.are still nearly synonymous), but the field has made progress in the present century at a rate whose increase is reminiscent of Moore's law. The achievements of molecular programming are far too numerous to survey here, but they include the self-assembly of virtually any two-or three-dimensional nanoscale structure that one wants to prescribe [19, 25, 27, 35, 42], DNA strand displacement networks that simulate logic circuits and neural networks [31][32][33], and molecular robots that perform various functions while either walking on nanoscale tracks or floating free in solution [13,15,18, 37, 41, 45, 46]. All this has been achieved in real laboratory experiments, and applications to synthetic biology, medicine, and computer electronics are envisioned. Theoretical progress includes demonstrations that various molecular programming paradigms are, in principle, Turing universal [3,14,17, 22, 38, 43, 44], thereby indicating that the full generality and creativity of algorithmic computation may be deployed in molecular and biological arenas.Our objective in this paper is to begin mitigating the "in principle" of the preceding sentence. This is important for two reasons. First, although such theoretical results are steps in the right direction, processes that require unrealistically precise control of unrealistically large numbers of molecules simply cannot be implemented. Second, processes that can be implemented, but only with inordinately precise control of parameters are inherently unreliable and hence inherently unsafe in many envisioned applications. Our objective here is thus to identify a class of computations that can be implemented robustly in the molecular world, i.e., implemented in such a way that they will provably perform correctly, even when crucial parameters are perturbed by small amounts. Future research can then strive to enhance this robustness and to extend the class of computations that enjoy it.In this paper we give a uniform method for translating nondeterministic finite automata to chemical reaction networks that implement them robustly. Nondeterministic finite automata (NFAs) are over half a century old [34] and far from Turing universal, but they have many applications and remain an active research topic [8,9, 26]. Applications of...

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Probabilistic risk assessment has advantages over qualitative risk ranking for cases where choices need to be made that require consideration of variable inputs, where model sensitivities to variable inputs and their effects are to be studied, and where more detailed output is required to form the basis of sound and informed decision making. Monte Carlo Simulation and probability of failure prediction using First Order Reliability Methods (FORM) both provide this functionality and are used to both demonstrate the effects of variability on risk assessments for heat induced tyre failure, and to highlight the advantages of such a probabilistic approach. OPSOMMINGDie probabilistiese assessering van risiko beskik oor bepaalde voordele vir gevalle waar keuses uitgeoefen moet word met onderliggende veranderlike insette, waar modelsensitiwiteit bepaal moet word vir inseteienskappe en die model gesonde en ingeligde besluitvorming moet ondersteun. Monte Carlo simulasie en eerste orde betroubaarheidsmetodes is daartoe instaat om die resultate te demonstreer oor hoe veranderlikheid risikoassessering beïnvloed by hittegeïnduseerde mislukkings van voertuigbande.

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