This paper describes, in brief, an evolutionary algorithm (EA) for the design of RNA-activated hammerhead ribozymes that selectively cleave a mutant transcript, while leaving the wild type transcript largely unaffected. We present experimental data that provide clear evidence of the selective silencing of the PABPNI gene, at both mRNA and protein levels. As such, this work presents a concrete example of the employability of machine learning algorithms (here, EAs) for the design of trans-acting ribozymes capable of gene silencing via selective mRNA cleavage.

In this paper, we describe a framework for the autonomous capture and servicing of satellites. The work is based on laboratory experiments that illustrate the autonomy and remote-operation aspects. The satellite-capture problem is representative of most on-orbit robotic manipulation tasks where the environment is known and structured, but it is dynamic since the satellite to be captured is in free flight. Bandwidth limitations and communication dropouts dominate the quality of the communication link. The satellite-servicing scenario is implemented on a robotic test-bed in laboratory settings