Interplay of electrostatics and lipid packing determines the binding of charged polymer coated nanoparticles to model membranes

Understanding of nanoparticle-membrane interactions is useful for various applications of nanoparticles like drug delivery and imaging. Here we report on the studies of interaction between hydrophilic charged polymer coated semiconductor quantum dot nanoparticles with model lipid membranes. Atomic force microscopy and X-ray reflectivity measurements suggest that cationic nanoparticles bind and penetrate bilayers of zwitterionic lipids. Penetration and binding depend on the extent of lipid packing and result in the disruption of the lipid bilayer accompanied by enhanced lipid diffusion. On the other hand, anionic nanoparticles show minimal membrane binding although, curiously, their interaction leads to reduction in lipid diffusivity. It is suggested that the enhanced binding of cationic QDs at higher lipid packing can be understood in terms of the effective surface potential of the bilayers which is tunable through membrane lipid packing. Our results bring forth the subtle interplay of membrane lipid packing and electrostatics which determine nanoparticle binding and penetration of model membranes with further implications for real cell membranes.

DETERMINISTIC ALGORITHMS FOR MATCHING AND PACKING PROBLEMS BASED ON REPRESENTATIVE SETS

In this work, we study the well-known r-DIMENSIONAL k-MATCHING ((r, k)-DM), and r-SET k-PACKING ((r, k)-SP) problems. Given a universe U := U-1 ... U-r and an r-uniform family F subset of U-1 x ... x U-r, the (r, k)-DM problem asks if F admits a collection of k mutually disjoint sets. Given a universe U and an r-uniform family F subset of 2(U), the (r, k)-SP problem asks if F admits a collection of k mutually disjoint sets. We employ techniques based on dynamic programming and representative families. This leads to a deterministic algorithm with running time O(2.851((r-1)k) .vertical bar F vertical bar. n log(2)n . logW) for the weighted version of (r, k)-DM, where W is the maximum weight in the input, and a deterministic algorithm with running time O(2.851((r-0.5501)k).vertical bar F vertical bar.n log(2) n . logW) for the weighted version of (r, k)-SP. Thus, we significantly improve the previous best known deterministic running times for (r, k)-DM and (r, k)-SP and the previous best known running times for their weighted versions. We rely on structural properties of (r, k)-DM and (r, k)-SP to develop algorithms that are faster than those that can be obtained by a standard use of representative sets. Incorporating the principles of iterative expansion, we obtain a better algorithm for (3, k)-DM, running in time O(2.004(3k).vertical bar F vertical bar . n log(2)n). We believe that this algorithm demonstrates an interesting application of representative families in conjunction with more traditional techniques. Furthermore, we present kernels of size O(e(r)r(k-1)(r) logW) for the weighted versions of (r, k)-DM and (r, k)-SP, improving the previous best known kernels of size O(r!r(k-1)(r) logW) for these problems.