Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.

Wireless service providers are facing the need to plan and rapidly evolve their networks to meet subscriber demands. This includes adding switching and radio equipment. At the same time, providers want to maximize the return on their investment by yielding the most subscriber capacity from their network. As networks evolve, large numbers of base transceiver stations (BTSs) along with base station controllers (BSCs) and mobile switching centers (MSCs) are added to support subscriber growth and demand. How to deploy this equipment and maximize performance is a complex problem, which calls for sound and useful methodologies. In order to assess the performance of the network, the subscriber's impacts must be known. Without the understanding of how the subscribers will tax the network resources, the providers will not be able to make the most of their investment and ensure quality and grade of service. This paper proposes techniques for modeling subscriber demand and behavior, which is the key in determining how networks will perform as the subscriber base grows and the network evolves. These scientific techniques were developed to generate accurate representations of subscriber behavior to be used in the planning of wireless networks.

We give a linear-time algorithm for single-source shortest paths in planar graphs with nonnegative edge-lengths. Our algorithm also yields a linear-time algorithm for maximum flow in a planar graph with the source and sink on the same face. For the case where negative edge-lengths are allowed, we give an algorithm requiringO(n4/3 log(nL)) time, whereLis the absolute value of the most negative length. This algorithm can be used to obtain similar bounds for computing a feasible flow in a planar network, for finding a perfect matching in a planar bipartite graph, and for finding a maximum flow in a planar graph when the source and sink are not on the same face. We also give parallel and dynamic versions of these algorithms.

Debugging long-running, nondeterministic message-passing parallel programs requires incremental replay, the ability to exactly replay selected parts of an execution. To support incremental replay, we must log enough messages and checkpoint processes often enough to allow any requested replay to complete quickly. We present an adaptive tracing strategy to keep the message-logging overhead down. We let the user specify a bound on the maximum time any replay request is allowed to take. Our algorithm tracks what each process's critical path will be during a replay and logs enough messages to ensure the critical path will never exceed the bound. Overhead is kept low by not logging messages that can be recomputed during a replay. Experiments indicate that we log about 0.1-5% of the messages while still providing a reasonable bound on any replay.>

External 2-dimensional searching is a fundamental problem with many applications in relational, object-oriented, spatial, and temporal databases. For example, interval intersection can be reduced to 2-sided, 2-dimensional searching and indexing class hierarchies of objects to 3-sided, 2-dimensional searching. Path caching is a new technique that can be used to transform a number of time/space efficient data structures for internal 2-dimensional searching (such as segment trees, interval trees, and priority search trees) into I/O efficient external ones. Let n be the size of the database, B the page size, and t the output size of a query. Using path caching, we provide the first data structure with optimal I/O query time O(logBn+t/B) for 2-sided, 2-dimensional searching. Furthermore, we show that path caching requires a small space overhead O(n÷Blog2log2B) and is simple enough to admit dynamic updates in optimal O(logBn) amortized time. We also extend this data structure to handle 3-sided, 2-dimensional searching with optimal I/O query-time, at the expense of slightly higher storage and update overheads.

We give a linear-time algorithm for single-source shortest paths in planar graphs with nonnegative edge-lengths. Our algorithm also yields a linear-time algorithm for maximum flow in a planar graph with the source and sink on the same face. The previous best algorithms for these problems required\\Omega\\Gamma n p log n) time where n is the number of nodes in the input graph. For the case where negative edge-lengths are allowed, we give an algorithm requiring O(n 4=3 log nL) time, where L is the absolute value of the most negative length. Previous algorithms for shortest paths with negative edge-lengths required \\Omega\\Gamma n 3=2 ) time. Our shortest-path algorithm yields an O(n 4=3 log n)-time algorithm for finding a perfect matching in a planar bipartite graph. A similar improvement is obtained for maximum flow in a directed planar graph.

We give an algorithm requiring polylog time and a linear number of processors to solve single-source shortest paths in directed planar graphs, bounded-genus graphs, and 2-dimensional overlap graphs. More generally, the algorithm works for any graph provided with a decomposition tree constructed using size-O(/spl radic/n polylog n) separators.>