Challenges of Developing New Classes of NASA Self-Managing Mission

NASA is proposing increasingly complex missions that will require a high degree of autonomy and autonomicity. These missions pose hereto unforeseen problems and raise issues that have not been well-addressed by the community. Assuring success of such missions will require new software development techniques and tools. This paper discusses some of the challenges that NASA and the rest of the software development community are facing in developing these ever-increasingly complex systems. We give an overview of a proposed NASA mission as well as techniques and tools that are being developed to address autonomic management and the complexity issues inherent in these missions

Requirements of an Integrated Formal Method for Intelligent Swarms

NASA is investigating new paradigms for future space exploration, heavily focused on the (still) emerging technologies of autonomous and autonomic systems [47, 48, 49]. Missions that rely on multiple, smaller, collaborating spacecraft, analogous to swarms in nature, are being investigated to supplement and complement traditional missions that rely on one large spacecraft [16]. The small spacecraft in such missions would each be able to operate on their own to accomplish a part of a mission, but would need to interact and exchange information with the other spacecraft to successfully execute the mission

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

Modelling a wireless connected swarm of mobile robots

It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model. © Springer Science + Business Media, LLC 2008

Classification of HIV-1 Sequences Using Profile Hidden Markov Models

Accurate classification of HIV-1 subtypes is essential for studying the dynamic spatial distribution pattern of HIV-1 subtypes and also for developing effective methods of treatment that can be targeted to attack specific subtypes. We propose a classification method based on profile Hidden Markov Model that can accurately identify an unknown strain. We show that a standard method that relies on the construction of a positive training set only, to capture unique features associated with a particular subtype, can accurately classify sequences belonging to all subtypes except B and D. We point out the drawbacks of the standard method; namely, an arbitrary choice of threshold to distinguish between true positives and true negatives, and the inability to discriminate between closely related subtypes. We then propose an improved classification method based on construction of a positive as well as a negative training set to improve discriminating ability between closely related subtypes like B and D. Finally, we show how the improved method can be used to accurately determine the subtype composition of Common Recombinant Forms of the virus that are made up of two or more subtypes. Our method provides a simple and highly accurate alternative to other classification methods and will be useful in accurately annotating newly sequenced HIV-1 strains