Search

Rare events are of particular interest in synthetic biology because rare biochemical events may be catastrophic to a biological system by, for example, triggering irreversible events such as off-target drug delivery. To estimate the probability of rare events efficiently, several weighted stochastic simulation methods have been developed. Under optimal parameters and model conditions, these methods can greatly improve simulation efficiency in comparison to traditional stochastic simulation. Unfortunately, the optimal parameters and conditions cannot be deduced a priori . This paper presents a critical survey of weighted stochastic simulation methods. It shows that the methods considered here cannot consistently, efficiently, and exactly accomplish the task of rare event simulation without resorting to a computationally expensive calibration procedure, which undermines their overall efficiency. The results suggest that further development is needed before these methods can be deployed for general use in biological simulations.

This chapter describes new analysis and verification techniques for synthetic genetic circuits. In particular, it applies stochastic model checking techniques to models of genetic circuits in order to ensure that they behave correctly and are as robust as possible for a variety of different inputs and parameter settings. In addition to stochastic model checking, this chapter proposes new variants to the incremental stochastic simulation algorithm (iSSA) that are capable of presenting a researcher with a simulation trace of the typical behavior of the system. Before the development of this algorithm, discerning this information was extremely error-prone as it involved performing many simulations and attempting to wade through the massive amounts of data. This algorithm greatly aids researchers in designing genetic circuits as it efficiently shows the researcher the most likely behavior of the circuit. Both the iSSA and stochastic model checking can be used in concert to give a researcher the likelihood that the system exhibits its most typical behavior, as well as, non-typical behaviors. This methodology is applied to several genetic circuits leading to new understanding of the effects of various parameters on the behavior of these circuits.

Several low-power communication strategies have been studied for interfacing with cortical implants via mutual inductance links. In this paper, we consider performance optimization strategies for a mutual-inductance link based on the Pulse Harmonic Modulation method. We consider two enhancements that may allow for increased throughput in the PHM system. First, a low-power error-correcting code is used to improve the system's robustness against noise, timing jitter and other non-ideal factors. Second, the system is adapted for multi-level modulation as a means of increasing the data rate. Our results characterize each systems' bit error rate as a function of pulse jitter, power interference and comparator offset.

This paper presents results on the design and analysis of a robust genetic Muller C-element. The Muller C-element is a standard logic gate commonly used to synchronize independent processes in most asynchronous electronic circuits. Synthetic biological logic gates have been previously demonstrated, but there remain many open issues in the design of sequential (state-holding) logic operations. Three designs are considered for the genetic Muller C-element: a majority gate, a toggle switch, and a speed-independent implementation. While the three designs are logically equivalent, each design requires different assumptions to operate correctly. The majority gate design requires the most timing assumptions, the speed-independent design requires the least, and the toggle switch design is a compromise between the two. This paper examines the robustness of these designs as well as the effects of parameter variation using stochastic simulation. The results show that robustness to timing assumptions does not necessarily increase reliability, suggesting that modifications to existing logic design tools are going to be necessary for synthetic biology. Parameter variation simulations yield further insights into the design principles necessary for building robust genetic gates. The results suggest that high gene count, cooperativity of at least two, tight repression, and balanced decay rates are necessary for robust gates. Finally, this paper presents a potential application of the genetic Muller C-element as a quorum-mediated trigger., (Copyright (c) 2009 Elsevier Ltd. All rights reserved.)

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. The case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience. [ABSTRACT FROM AUTHOR]

• An outage management strategy is proposed to enhance the distribution resilience. • The rank-based constraint is proposed to ensure the radial structure of the network. • The comprehensive post-fault management strategy considers various resources. • The DER scheduling strategy is based on the linearized power flow for efficiency. Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration. [ABSTRACT FROM AUTHOR]