We develop an optical imaging technique for spatially and temporally tracking biofilm growth and the distribution of the main phenotypes of a Bacillus subtilis strain with a triple-fluorescent reporter for motility, matrix production, and sporulation. We develop a calibration procedure for determining the biofilm thickness from the transmission images, which is based on Beer-Lambert's law and involves cross-sectioning of biofilms. To obtain the phenotype distribution, we assume a linear relationship between the number of cells and their fluorescence and determine the best combination of calibration coefficients that matches the total number of cells for all three phenotypes and with the total number of cells from the transmission images. Based on this analysis, we resolve the composition of the biofilm in terms of motile, matrix-producing, sporulating cells and low-fluorescent materials which includes matrix and cells that are dead or have low fluorescent gene expression. We take advantage of the circular growth to make kymograph plots of all three phenotypes and the dominant phenotype in terms of radial distance and time. To visualize the nonlocal character of biofilm growth, we also make kymographs using the local colonization time. Our technique is suitable for real-time, noninvasive, quantitative studies of the growth and phenotype distribution of biofilms which are either exposed to different conditions such as biocides, nutrient depletion, dehydration, or waste accumulation.

How do the topology and geometry of a tubular network affect the spread of particles within fluid flows? We investigate patterns of effective dispersion in the hierarchical, biological transport network formed by Physarum polycephalum. We demonstrate that a change in topology-pruning in the foraging state-causes a large increase in effective dispersion throughout the network. By comparison, changes in the hierarchy of tube radii result in smaller and more localized differences. Pruned networks capitalize on Taylor dispersion to increase the dispersion capability.

Natural odors typically consist of many molecules at different concentrations. It is unclear how the numerous odorant molecules and their possible mixtures are discriminated by relatively few olfactory receptors. Using an information theoretic model, we show that a receptor array is optimal for this task if it achieves two possibly conflicting goals: (i) Each receptor should respond to half of all odors and (ii) the response of different receptors should be uncorrelated when averaged over odors presented with natural statistics. We use these design principles to predict statistics of the affinities between receptors and odorant molecules for a broad class of odor statistics. We also show that optimal receptor arrays can be tuned to either resolve concentrations well or distinguish mixtures reliably. Finally, we use our results to predict properties of experimentally measured receptor arrays. Our work can thus be used to better understand natural olfaction, and it also suggests ways to improve artificial sensor arrays.

Monodisperse drops with diameters between 20 mu m and 200 mu m can be used to produce particles or capsules for many applications such as for cosmetics, food, and biotechnology. Drops composed of low viscosity fluids can be conveniently made using microfluidic devices. However, the throughput of microfluidic devices is limited and scale-up, achieved by increasing the number of devices run in parallel, can compromise the narrow drop-size distribution. In this paper, we present a microfluidic device, the millipede device, which forms drops through a static instability such that the fluid volume that is pinched off is the same every time a drop forms. As a result, the drops are highly monodisperse because their size is solely determined by the device geometry. This makes the operation of the device very robust. Therefore, the device can be scaled to a large number of nozzles operating simultaneously on the same chip; we demonstrate the operation of more than 500 nozzles on a single chip that produces up to 150 mL h(-1) of highly monodisperse drops.

Controlling motion at the microscopic scale is a fundamental goal in the development of biologically inspired systems. We show that the motion of active, self-propelled colloids can be sufficiently controlled for use as a tool to assemble complex structures such as braids and weaves out of microscopic filaments. Unlike typical self-assembly paradigms, these structures are held together by geometric constraints rather than adhesive bonds. The out-of-equilibrium assembly that we propose involves precisely controlling the 2D motion of active colloids so that their path has a nontrivial topology. We demonstrate with proof-of-principle Brownian dynamics simulations that, when the colloids are attached to long semiflexible filaments, this motion causes the filaments to braid. The ability of the active particles to provide sufficient force necessary to bend the filaments into a braid depends on a number of factors, including the self-propulsion mechanism, the properties of the filament, and the maximum curvature in the braid. Our work demonstrates that nonequilibrium assembly pathways can be designed using active particles.

Chefs and scientists exploring biophysical processes have given rise to molecular gastronomy. In this Commentary, we describe how a scientific understanding of recipes and techniques facilitates the development of new textures and expands the flavor palette. The new dishes that result engage our senses in unexpected ways.

Femtosecond-laser hyperdoping of sulfur in silicon typically produces a concentration gradient that results in undesirable inhomogeneous material properties. Using a mathematical model of the doping process, we design a fabrication method consisting of a sequence of laser pulses with varying sulfur concentrations in the atmosphere, which produces hyperdoped silicon with a uniform concentration depth profile. Our measurements of the evolution of the concentration profiles with each laser pulse are consistent with our mathematical model of the doping mechanism, based on classical heat and solute diffusion coupled to the far-from-equilibrium dopant incorporation. The use of optimization methods opens an avenue for creating controllable hyperdoped materials on demand. (C) 2015 AIP Publishing LLC.

``Exo-C'' is NASAs first community study of a modest aperture space telescope mission that is optimized for high contrast observations of exoplanetary systems. The mission will be capable of taking optical spectra of nearby exoplanets in reflected light, discovering previously undetected planets, and imaging structure in a large sample of circumstellar disks. It will obtain unique science results on planets down to super-Earth sizes and serve as a technology pathfinder toward an eventual flagship-class mission to find and characterize habitable Earth-like exoplanets. We present the mission/payload design and highlight steps to reduce mission cost/risk relative to previous mission concepts. Key elements are an unobscured telescope aperture, an internal coronagraph with deformable mirrors for precise wavefront control, and an orbit and observatory design chosen for high thermal stability. Exo-C has a similar telescope aperture, orbit, lifetime, and spacecraft bus requirements to the highly successful Kepler mission (which is our cost reference). Much of the needed technology development is being pursued under the WFIRST coronagraph study and would support a mission start in 2017, should NASA decide to proceed. This paper summarizes the study final report completed in March 2015.

In this paper, we examine the fundamental processes that occur during femtosecond-laser hyperdoping of silicon with a gas-phase dopant precursor. We probe the dopant concentration profile as a function of the number of laser pulses and pressure of the dopant precursor (sulfur hexafluoride). In contrast to previous studies, we show the hyperdoped layer is single crystalline. From the dose dependence on pressure, we conclude that surface adsorbed molecules are the dominant source of the dopant atoms. Using numerical simulation, we estimate the change in flux with increasing number of laser pulses to fit the concentration profiles. We hypothesize that the native oxide plays an important role in setting the surface boundary condition. As a result of the removal of the native oxide by successive laser pulses, dopant incorporation is more efficient during the later stage of laser irradiation. (C) 2015 AIP Publishing LLC.

Amorphous nanoparticles (a-NPs) have physicochemical properties distinctly different from those of the corresponding bulk crystals; for example, their solubility is much higher. However, many materials have a high propensity to crystallize and are difficult to formulate in an amorphous structure without stabilizers. We fabricated a microfluidic nebulator that can produce amorphous NPs from a wide range of materials, even including pure table salt (NaCl). By using supersonic air flow, the nebulator produces drops that are so small that they dry before crystal nuclei can form. The small size of the resulting spray-dried a-NPs limits the probability of crystal nucleation in any given particle during storage. The kinetic stability of the a-NPs-on the order of months-is advantageous for hydrophobic drug molecules.

In this work, we report on the development of slit-surface electrospinning - a process that co-localizes two solutions along a slit surface to spontaneously emit multiple core-sheath cone-jets at rates of up to 1 L/h. To the best of our knowledge, this is the first time that production of electrospun core-sheath fibers has been scaled to this magnitude. Fibers produced in this study were defect-free (i.e. non-beaded) and core-sheath geometry was visually confirmed under scanning electron microscopy. The versatility of our system was demonstrated by fabrication of (1) fibers encapsulating a drug, (2) bicomponent fibers, (3) hollow fibers, and (4) fibers from a polymer that is not normally electrospinnable. Additionally, we demonstrate control of the process by modulating parameters such as flow rate, solution viscosity, and fixture design. The technological achievements demonstrated in this work significantly advance core-sheath electrospinning towards commercial and manufacturing viability.

We study experimentally what is arguably the simplest yet nontrivial colloidal system: two-dimensional clusters of six spherical particles bound by depletion interactions. These clusters have multiple, degenerate ground states whose equilibrium distribution is determined by entropic factors, principally the symmetry. We observe the equilibrium rearrangements between ground states as well as all of the low-lying excited states. In contrast to the ground states, the excited states have soft modes and low symmetry, and their occupation probabilities depend on the size of the configuration space reached through internal degrees of freedom, as well as a single ``sticky parameter'' encapsulating the depth and curvature of the potential. Using a geometrical model that accounts for the entropy of the soft modes and the diffusion rates along them, we accurately reproduce the measured rearrangement rates. The success of this model, which requires no fitting parameters or measurements of the potential, shows that the free-energy landscape of colloidal systems and the dynamics it governs can be understood geometrically.

Inspired by multiprotein complexes in biology and recent successes in synthetic DNA tile and colloidal self-assembly, we study the spontaneous assembly of structures made of many kinds of components. The major challenge with achieving high assembly yield is eliminating incomplete or incorrectly bound structures. Here, we find that such undesired structures rapidly degrade yield with increasing structural size and complexity in diverse models of assembly, if component concentrations reflect the composition (that is, stoichiometry) of the desired structure. But this yield catastrophe can be mitigated by using highly non-stoichiometric concentrations. Our results support a general principle of `undesired usage'-concentrations of components should be chosen to account for how they are `used' by undesired structures and not just by the desired structure. This principle could improve synthetic assembly methods, but also raises new questions about expression levels of proteins that form biological complexes such as the ribosome.

Motivated by colloidal lithography, we study the problem of characterizing periodic planar patterns formed by shadows of spheres. The set of patterns accessible to shadow lithography spanned by lattice types, tilt, and rotation angles is rich, but topological considerations of shadow overlap along simplex edges and faces lead us to just 4 + 1 distinct categories. These planar patterns are in one-to-one correspondence with a 4-valued index linked to Cayley-Menger determinants. The characterization is confirmed by a phase diagram which predicts surface patterns for any experimental geometry.

The explosive growth in the number of protein sequences gives rise to the possibility of using the natural variation in sequences of homologous proteins to find residues that control different protein phenotypes. Because in many cases different phenotypes are each controlled by a group of residues, the mutations that separate one version of a phenotype from another will be correlated. Here we incorporate biological knowledge about protein phenotypes and their variability in the sequence alignment of interest into algorithms that detect correlated mutations, improving their ability to detect the residues that control those phenotypes. We demonstrate the power of this approach using simulations and recent experimental data. Applying these principles to the protein families encoded by Dscam and Protocadherin allows us to make testable predictions about the residues that dictate the specificity of molecular interactions.

``Exo-C'' is NASA's first community study of a modest aperture space telescope designed for high contrast observations of exoplanetary systems. The mission will be capable of taking optical spectra of nearby exoplanets in reflected light, discover previously undetected planets, and imaging structure in a large sample of circumstellar disks. It will obtain unique science results on planets down to super-Earth sizes and serve as a technology pathfinder toward an eventual flagship-class mission to find and characterize habitable exoplanets. We present the mission/payload design and highlight steps to reduce mission cost/risk relative to previous mission concepts. At the study conclusion in 2015, NASA will evaluate it for potential development at the end of this decade.

A common method for extracting true correlations from large data sets is to look for variables with unusually large coefficients on those principal components with the biggest eigenvalues. Here, we show that even if the top principal components have no unusually large coefficients, large coefficients on lower principal components can still correspond to a valid signal. This contradicts the typical mathematical justification for principal component analysis, which requires that eigenvalue distributions from relevant random matrix ensembles have compact support, so that any eigenvalue above the upper threshold corresponds to signal. The new possibility arises via a mechanism based on a variant of the Feynman-Hellmann theorem, and leads to significant correlations between a signal and principal components when the underlying noise is not both independent and uncorrelated, so the eigenvalue spacing of the noise distribution can be sufficiently large. This mechanism justifies a new way of using principal component analysis and rationalizes recent empirical findings that lower principal components can have information about the signal, even if the largest ones do not.

In both fungi and bacterial biofilms, when nutrients are depleted, the organisms cannot physically migrate to find a new source, but instead must develop adaptations that allow them to survive. This paper reviews our work attempting to discover design principles for these adaptations. We develop fluid mechanical models, and aim to understand whether these suggest organizing principles for the observed morphological diversity. Determining whether a proposed organizing principle explains extant biological designs is fraught with difficulty: simply because a design principle predicts characteristics similar to an organism's morphology could just as well be accidental as revealing. In each of the two sets of examples, we adopt different strategies to develop understanding in spite of this difficulty. Within the fungal phylum Ascomycota, we use the large observed diversity of different morphological solutions to the fundamental fluid mechanical problem to measure how far each solution is from a design optimum, thereby measuring how far the extant designs deviate from the hypothesized optimum. This allows comparing different design principles to each other. For biofilms, we use engineering principles to make qualitative predictions of what types of adaptations might exist given the physicochemical properties of the repertoire of proteins that bacteria can create, and then find evidence for these adaptations in experiments. While on the surface this paper addresses the particular adaptations used by the fungal phylum Ascomycota and bacterial biofilms, we also aim to motivate discussion of different approaches to using design principles, fluid mechanical or otherwise, to rationalize observed engineering solutions in biology. (C) 2014 AIP Publishing LLC.

Self-assembly materials are traditionally designed so that molecular or mesoscale components form a single kind of large structure. Here, we propose a scheme to create ``multifarious assembly mixtures,'' which self-assemble many different large structures from a set of shared components. We show that the number of multifarious structures stored in the solution of components increases rapidly with the number of different types of components. However, each stored structure can be retrieved by tuning only a few parameters, the number of which is only weakly dependent on the size of the assembled structure. Implications for artificial and biological self-assembly are discussed.

Many types of bacteria form colonies that grow into physically robust and strongly adhesive aggregates known as biofilms. A distinguishing characteristic of bacterial biofilms is an extracellular polymeric substance (EPS) matrix that encases the cells and provides physical integrity to the colony. The EPS matrix consists of a large amount of polysaccharide, as well as protein filaments, DNA and degraded cellular materials. The genetic pathways that control the transformation of a colony into a biofilm have been widely studied, and yield a spatiotemporal heterogeneity in EPS production. Spatial gradients in metabolites parallel this heterogeneity in EPS, but nutrient concentration as an underlying physiological initiator of EPS production has not been explored. Here, we study the role of nutrient depletion in EPS production in Bacillus subtilis biofilms. By monitoring simultaneously biofilm size and matrix production, we find that EPS production increases at a critical colony thickness that depends on the initial amount of carbon sources in the medium. Through studies of individual cells in liquid culture we find that EPS production can be triggered at the single-cell level by reducing nutrient concentration. To connect the single-cell assays with conditions in the biofilm, we calculate carbon concentration with a model for the reaction and diffusion of nutrients in the biofilm. This model predicts the relationship between the initial concentration of carbon and the thickness of the colony at the point of internal nutrient deprivation.