Correction: A bioenergetic mechanism for amoeboid-like cell motility profiles tested in a microfluidic electrotaxis assay.

Correction for 'A bioenergetic mechanism for amoeboid-like cell motility profiles tested in a microfluidic electrotaxis assay' by Hagit Peretz-Soroka et al., Integr. Biol., 2017, DOI: .

A bioenergetic mechanism for amoeboid-like cell motility profiles tested in a microfluidic electrotaxis assay.

The amoeboid-like cell motility is known to be driven by the acidic enzymatic hydrolysis of ATP in the actin-myosin system. However, the electro-mechano-chemical coupling, whereby the free energy of ATP hydrolysis is transformed into the power of electrically polarized cell movement, is poorly understood. Previous experimental studies showed that actin filaments motion, cytoplasmic streaming, and muscle contraction can be reconstituted under actin-activated ATP hydrolysis by soluble non-filamentous myosin fragments. Thus, biological motility was demonstrated in the absence of a continuous protein network. These results lead to an integrative conceptual model for cell motility, which advocates an active role played by intracellular proton currents and cytoplasmic streaming (iPC-CS). In this model, we propose that protons and fluid currents develop intracellular electric polarization and pressure gradients, which generate an electro-hydrodynamic mode of amoeboid motion. Such energetic proton currents and active streaming are considered to be mainly driven by stereospecific ATP hydrolysis through myosin heads along oriented actin filaments. Key predictions of this model are supported by microscopy visualization and in-depth sub-population analysis of purified human neutrophils using a microfluidic electrotaxis assay. Three distinct phases in cell motility profiles, morphology, and cytoplasmic streaming in response to physiological ranges of chemoattractant stimulation and electric field application are revealed. Our results support an intrinsic electric dipole formation linked to different patterns of cytoplasmic streaming, which can be explained by the iPC-CS model. Collectively, this alternative biophysical mechanism of cell motility provides new insights into bioenergetics with relevance to potential new biomedical applications.

Investigating multi-objective fluence and beam orientation IMRT optimization.

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a 'bird's-eye-view' perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird's-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters, such as beam fluence and beam angles, were included in the optimization.

PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning.

PURPOSE: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. METHODS: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. RESULTS: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number of beams. CONCLUSIONS: This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient.