We have tested the entire Keio collection of close to 4,000 single-gene knockouts in Escherichia coli for increased susceptibility to one of seven different antibiotics (ciprofloxacin, rifampin, vancomycin, ampicillin, sulfamethoxazole, gentamicin, or metronidazole). We used high-throughput screening of several subinhibitory concentrations of each antibiotic and reduced more than 65,000 data points to a set of 140 strains that display significantly increased sensitivities to at least one of the antibiotics, determining the MIC in each case. These data provide targets for the design of "codrugs" that can potentiate existing antibiotics. We have made a number of double mutants with greatly increased sensitivity to ciprofloxacin, and these overcome the resistance generated by certain gyrA mutations. Many of the gene knockouts in E. coli are hypersensitive to more than one antibiotic. Together, all of these data allow us to outline the cell's "intrinsic resistome," which provides innate resistance to antibiotics.Antibiotics have had a major impact over the past 6 decades in the fight against infectious diseases (for a review by Davies, see reference 11). However, the spread of antibiotic-resistant microorganisms has reached an alarming point (1,11,35), prompting renewed efforts to find new antibiotics by detecting new targets through genomics, altering existing antibiotics, screening chemical (e.g., see reference 9) or peptide (21, 31) libraries for specific inhibitors (e.g., see reference 9), or finding new sources of antibiotics via metagenomics (e.g., see reference 53). While these lines of investigation show great promise, additional approaches are constantly being sought to yield a new generation of useful antimicrobial compounds. For instance, focusing on species-specific antibiotics rather than broad-spectrum antibiotics can result in important new agents (38), as could targeting bacterial transcription factors (5) or different processes, such as Holliday junction processing (21, 31) and quorum sensing (24), or even targeting host factors that support pathogen growth (33). Another approach, examined here, involves potentiating existing antibiotics by identifying targets for increasing susceptibility to specific antimicrobials. There are precedents for using such combinational therapy. For example, inhibitors of ␤-lactamase have been used together with ␤-lactam antibiotics (for a review by Buynak, see reference 7) and inhibitors of efflux pumps together with tetracycline in Escherichia coli (46) and with levofloxacin in Pseudomonas aeruginosa (37). In the case of chemotherapeutics, zebularine, a cytosine analog (41) and mutagen (34) that is converted in vivo to an inhibitor of cytosine deaminase (41), is used in combination with certain cytosine deaminase-susceptible cytosine-based drugs (14, 40). With regard to finding new targets for this type of approach, a number of genes that increase the sensitivities of microorganisms to different antibiotics have been identified (e.g., for a review by Drlica and Zhao, ...

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Computational modelling has made many useful contributions to the field of optical tweezers. One aspect in which it can be applied is the simulation of the dynamics of particles in optical tweezers. This can be useful for systems with many degrees of freedom, and for the simulation of experiments. While modelling of the optical force is a prerequisite for simulation of the motion of particles in optical traps, non-optical forces must also be included; the most important are usually Brownian motion and viscous drag. We discuss some applications and examples of such simulations. We review the theory and practical principles of simulation of optical tweezers, including the choice of method of calculation of optical force, numerical solution of the equations of motion of the particle, and finish with a discussion of a range of open problems.

Nonspherical probe particles are an attractive choice for optically-trapped scanning probe microscopy. We show that it is possible to calibrate a trap with a nonspherical particle using only position measurements, without requiring measurement of orientation, using a pseudopotential based on the position occupation probability. It is not necessary to assume the force is linear with displacement.