An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices

BACKGROUND: Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors. RESULTS: We demonstrate the three fundamental types of question that our software can address by taking as a specific example the connections between 49 measures of amino acid biophysical properties (e.g., size, charge and hydrophobicity), a generalized model of amino acid substitution (as represented by the PAM74-100 matrix), and the mutational distance that separates amino acids within the standard genetic code (i.e., the number of point mutations required for interconversion during protein evolution). We show that our software allows a user to recapture the insights from several key publications on these topics in just a few minutes. CONCLUSION: Our software facilitates rapid, interactive exploration of three interconnected topics: (i) the multidimensional molecular descriptors of the twenty proteinaceous amino acids, (ii) the correlation of these biophysical measurements with observed patterns of amino acid substitution, and (iii) the causal basis for differences between any two observed patterns of amino acid substitution. This software acts as an intuitive bioinformatic exploration tool that can guide more comprehensive statistical analyses relating to a diverse array of specific research questions

SGDB: a database of synthetic genes re-designed for optimizing protein over-expression

Here we present the Synthetic Gene Database (SGDB): a relational database that houses sequences and associated experimental information on synthetic (artificially engineered) genes from all peer-reviewed studies published to date. At present, the database comprises information from more than 200 published experiments. This resource not only provides reference material to guide experimentalists in designing new genes that improve protein expression, but also offers a dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that influence gene expression. The SGDB was built under MySQL database management system. We also offer an XML schema for standardized data description of synthetic genes. Users can access the database at , or batch downloads all information through XML files. Moreover, users may visually compare the coding sequences of a synthetic gene and its natural counterpart with an integrated web tool at , and discuss questions, findings and related information on an associated e-forum at

Efficient Planning Using Plan Libraries to Capture the Structure of the State Space

Automated, domain-independent planning is a research area within Artificial Intelligence that is used in a variety of practical applications, especially those for which a large degree of autonomy is required. Planning programs that are given information about the current state of the world, the available actions, and a set of goals that should be achieved. The planner's task is determining the plan: a set of actions and ordering constraints among them. A domain-independent planner is not tailored for a specific planning domain but can understand any domain encoded in a generic, expressive description language. Ideally, in domain-independent planning, the planner should be able to analyze the domain on its own, and adjust its reasoning method accordingly. This work focuses on improving the speed of automated, domain-independent planners in different planning domains by automatically reusing the planner's experience from previous planning episodes, and proactively exploring the problem to gather more experience about it. The experience is then stored in a novel type of plan library called a planning backbone, which contains the explored parts of the state space graph for the planning problem. Since the state space of a planning problem is closely related to the structure of the problem, the backbone approach results in a plan library that reflects that underlying structure. The dissertation presents algorithms for building planning backbones, modifying them, and using them in planning. It also presents a theoretical analysis of topological properties of state spaces for benchmark planning domains, and examines the effect of these topological properties on backbone coverage and the resulting performance improvements in planners that use the backbone

Visualizing Spatial Partitions ∗

We describe an application of geospatial visualization and AI search techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives must be considered, such as school capacity, busing costs, and socioeconomic distribution. Additionally, school assignments need to be made for three different levels (elementary, middle, and high school) in a way which allows children to move from one school to the next with a cohort of sufficient size. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present visualization techniques which can be used to visualize the quality of spatial partititioning plans, compare the alternatives presented by different plans, and understand the interrelationships of plans at different educational levels. We demonstrate these techniques on partitions created through both manual construction and intelligient search processes for the population data of the school district of Howard County, Maryland

Local learning to improve organizational performance in networked multi-agent team formation

Networked multiagent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply chain networks and sensor networks. A common challenge in many networked multiagent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multiagent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multiagent team formation in networked multi-agent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a naive heuristic