Mass spectrometric investigation of the DNA-binding properties of an anthracycline with two trisaccharide chains

Cosmomycin D (CosD) is an anthracycline that has two trisaccharide chains linked to its ring system. Gel electrophoresis showed that CosD formed stable complexes with plasmid DNA under conditions where daunorubicin (Dn) and doxorubicin (Dx) dissociated to some extent during the experiments. The footprint and stability of CosD complexed with 10- and 16 trier DNA was investigated using several applications of electrospray ionisation mass spectrometry (ESI-MS). ESI-MS binding profiles showed that fewer CosD molecules bound to the sequences than Dn or Dx. In agreement with this, ESI-MS analysis of nuclease digestion products of the complexes showed that CosD protected the DNA to a greater extent than Dn or Dx. In tandem MS experiments, all CosD-DNA complexes were more stable than Dn- and Dx-DNA complexes. These results Support that CosD binds more tightly to DNA and exerts a larger footprint than ESI-MS investigations of the binding properties of CosD Could be carried out rapidly and using only small amounts of sample. (C) 2008 Elsevier Inc. All rights reserved.

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.