Microscale robots offer an unprecedented opportunity to perform tasks at resolutions approaching 1 μm, but the great majority of research to this point focuses on actuation and control. Potential applications for microrobots can be considerably expanded by integrating sensing, signal processing and feedback into the system. In this work, we demonstrate that technologies from the field of synthetic biology may be directly integrated into microrobotic systems to create cell-based programmable mobile sensors, with signal processors and memory units. Specifically, we integrate genetically engineered, ultraviolet light-sensing bacteria with magnetic microrobots, creating the first controllable biological microrobot that is capable of exploring, recording and reporting on the state of the microscale environment. We demonstrate two proof-of-concept prototypes: (a) an integrated microrobot platform that is able to sense biochemical signals, and (b) a microrobot platform that is able to deploy biosensor payloads to monitor biochemical signals, both in a biological environment. These results have important implications for integrated micro-bio-robotic systems for applications in biological engineering and research.

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.

In recent years, the importance of interconnects on top-down engineered lithography-based electronic chips has outrun the importance of transistors as a dominant factor of performance. The major challenges in traditional chips are related to delays of non-scalable global interconnects and reliability in general, which leads to the observation that simple scaling will no longer satisfy performance requirements as feature sizes continue to shrink. In addition, the advent of massive-scale multicore architectures, novel silicon and non-silicon manufacturing techniques (such as self-assembly), and an increasing interest in biological components for computing force us to rethink, re-evaluate, and re-design the communication infrastructure and the communication paradigms in the era of nano- and biotechnology.

In the nascent field of synthetic biology, researchers are striving to create biological systems with functionality not seen in nature. This special session features talks that emphasize the fundamental engineering principles underlying this endeavor, highlighting possible synergies with electronic design automation (EDA). Pamela Silver will describe designing and constructing proteins and cells with predictable biological properties. These serve as potential therapeutics, cell-based sensors, factories for generating bio-energy, and bio-remediation. J. Christopher Anderson will demonstrate how complex biological functions can be decomposed into modular devices. He will describe the construction of therapeutic organisms and new tools for building complex systems. Richard Murray will discuss the use of concepts from control and dynamical systems in the analysis and design of biological feedback circuits at the molecular level.

Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. In this talk I will describe our integrated computational / experimental approach to engineering complex behavior in living systems ranging from bacteria to stem cells. In our research, we appropriate useful design principles from electrical engineering and other well established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline.

Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. We have developed an integrated computational/experimental approach to engineering complex behavior in living systems ranging from bacteria to stem cells. In our research, we appropriate useful design principles from electrical engineering and other well established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline.

MOTIVATION: The goal of synthetic biology is to design and construct biological systems that present a desired behavior. The construction of synthetic gene networks implementing simple functions has demonstrated the feasibility of this approach. However, the design of these networks is difficult, notably because existing techniques and tools are not adapted to deal with uncertainties on molecular concentrations and parameter values. RESULTS: We propose an approach for the analysis of a class of uncertain piecewise-multiaffine differential equation models. This modeling framework is well adapted to the experimental data currently available. Moreover, these models present interesting mathematical properties that allow the development of efficient algorithms for solving robustness analyses and tuning problems. These algorithms are implemented in the tool RoVerGeNe, and their practical applicability and biological relevance are demonstrated on the analysis of the tuning of a synthetic transcriptional cascade built in Escherichia coli. AVAILABILITY: RoVerGeNe and the transcriptional cascade model are available at http://iasi.bu.edu/%7Ebatt/rovergene/rovergene.htm.

In this paper, we use two synthetic gene networks, a transcriptional cascade and a pulse generating network, to study the efficacy of a simple statistical parameter fitting algorithm. The fitting was performed on experimental data and computer-generated data (to test how well the algorithm works under ideal conditions with perfect information). Most of the experimental parameter estimations yielded tight ranges of kinetic values for both gene networks. However, the results using simulated data indicate that the algorithm was able to provide better parameter estimates for the pulse generating network than for the transcriptional cascade. This is likely a result of the larger amount of time-series data available for the pulse generator and its greater level of phenotypical complexity, leading to tighter constraints for optimization. The variation in the magnitudes of the standard deviations between parameter estimates may give an indication of system sensitivity to specific kinetic rate constants. In the future, we also plan to verify the experimental results by constructing network variants and attempting to predict behaviors using values obtained in this study.

Availability requirements for database systems are more stringent than ever before with the widespread use of databases as the foundation for ebusiness. This paper highlights Fast-Start™ Fault Recovery, an important availability feature in Oracle, designed to expedite recovery from unplanned outages. Fast-Start allows the administrator to configure a running system to impose predictable bounds on the time required for crash recovery. For instance, fast-start allows fine-grained control over the duration of the roll-forward phase of crash recovery by adaptively varying the rate of checkpointing with minimal impact on online performance. Persistent transaction locking in Oracle allows normal online processing to be resumed while the rollback phase of recovery is still in progress, and fast-start allows quick and transparent rollback of changes made by uncommitted transactions prior to a crash.

Query Refinement is an essential information retrieval tool that interactively recommends new terms related to a particular query.This paper introduces concept recall, an experimental measure of an algorithm's ability to suggest terms humans have judged to be semantically related to an information need.This study uses precision improvement experiments to measure the ability of an algorithm to produce single term query modifications that predict a user's information need as partially encoded by the query.An omcie algorithm produces ideal query modifications, providing a meaningful context for interpreting precision improvement results.This study also introduces RMAP, a fast and practical query refinement algorithm that refines multiple term queries by dynamically combining precomputed suggestions for single term queries.RMAP achieves accuracy comparable to a much slower algorithm, although both RMAP and the slower algorithm lag behind the best possible term suggestions offered by the oracle.We believe RMAP is fast enough to be integrated into present day Internet search engines: RMAP computes 100 term suggestions for a 160,000 document collection in 15 ms on a low-end PC.