Search

Complete Supplemental Figures 1-8 and Supplemental Tables 1-2 are embedded in this file. Each item contains its respective legend.

Tumors are heterogeneous and composed of cells with different dissemination abilities. Despite significant effort, there is no universal biological marker that serves as a metric for metastatic potential of solid tumors. Common to disseminating cells from such tumors, however, is the need to modulate their adhesion as they detach from the tumor and migrate through stroma to intravasate. Adhesion strength is heterogeneous even among cancer cells within a given population, and using a parallel plate flow chamber, we separated and sorted these populations into weakly and strongly adherent groups; when cultured under stromal conditions, this adhesion phenotype was stable over multiple days, sorting cycles, and common across all epithelial tumor lines investigated. Weakly adherent cells displayed increased migration in both two-dimensional and three-dimensional migration assays; this was maintained for several days in culture. Subpopulations did not show differences in expression of proteins involved in the focal adhesion complex but did exhibit intrinsic focal adhesion assembly as well as contractile differences that resulted from differential expression of genes involved in microtubules, cytoskeleton linkages, and motor activity. In human breast tumors, expression of genes associated with the weakly adherent population resulted in worse progression-free and disease-free intervals. These data suggest that adhesion strength could potentially serve as a stable marker for migration and metastatic potential within a given tumor population and that the fraction of weakly adherent cells present within a tumor could act as a physical marker for metastatic potential.Significance:Cancer cells exhibit heterogeneity in adhesivity, which can be used to predict metastatic potential.

Cells originating from the same tissue can respond differently to external signals depending on the genotypic and phenotypic state of the cell and its local environment. We have developed a semi-quantitative-computational model to analyze the intracellular signaling network and its outcome in the presence of multiple external signals including growth factors, hormones, and extracellular matrix. We use this model to analyze the cell’s mechanical response to external stimuli and identify the key internal elements of the network that drive specific outcomes within the response space. The model is built upon the Boolean approach to network modeling, where the state of any given node is determined using the state of the connecting nodes and Boolean logic. This allows us to analyze the network behavior without the need to estimate all the various interaction rates between different cellular components. However, such an approach is limited in its ability to predict network dynamics and temporal evolution of the cell state. So, we introduce modularity in the model and incorporate dynamical aspects, mass-action kinetics, and chemo-mechanical effects on only certain transition rates within specific modules as required, creating a Boolean-Hybrid-Modular (BoHyM) signal transduction model. We present this model as a comprehensive, cell-type agnostic, user-modifiable tool to investigate how extra-and intra-cellular signaling can regulate cellular cytoskeletal components and consequently influence cell-substrate interactions, force generation, and migration. Using this tool, we show how slight changes in signaling network architectures due to phenotypic changes can alter cellular response to stress hormone signaling in an environment-dependent manner. The tool also allows isolating effector proteins driving specific cellular mechanical responses. Ultimately, we show the utility of the tool in analyzing transient chemo-mechanical dynamics of cells in response to time-varying chemical stimuli.

Tissue mechanical properties such as rigidity and fluidity, and changes in these properties driven by jamming-unjamming transitions (UJT), have come under recent highlight as mechanical markers of health and disease in various biological processes including cancer. However, most analysis of these mechanical properties and UJT have sidestepped the effect of cellular death and division in these systems. Cellular apoptosis (programmed cell death) and mitosis (cell division) can drive significant changes in tissue properties. The balance between the two is crucial in maintaining tissue function, and an imbalance between the two is seen in situations such as cancer progression, wound healing and necrosis. In this work we investigate the impact of cell death and division on tissue mechanical properties, by incorporating specific mechanosensitive triggers of cell death and division based on the size and geometry of the cell within in silico models of tissue dynamics. Specifically, we look at cell migration, tissue response to external stress, tissue extrusion propensity and self-organization of different cell types within the tissue, as a function of cell death and division and the rules that trigger these events. We find that not only do cell death and division events significantly alter tissue mechanics when compared to systems without these events, but that the choice of triggers driving these cell death and division events also alter the predicted tissue mechanics and overall system behavior.

Despite better outcomes with early-stage detection, local invasion significantly reduces patient survival rates for many carcinomas. Heterogeneity within and between tumors has precluded identification of predictive biological markers, but adhesion strength has emerged as a potential biophysical marker. Here we demonstrate that cells disseminating from mammary tumors are weakly adherent, and when presorted by adhesion, primary tumors created from strongly adherent cells exhibit fewer lung metastases than weakly adherent cells or unsorted populations. Migratory ontologies from tumors correlate with freshly sorted cells, suggesting that cell intrinsic differences are maintained in vivo. We further demonstrate that admixed cancer lines can be separated by label-free adhesive signatures using a next-generation flow chamber. When applied to metastatic tumors, the device retrospectively predicted metastatic disease from stromal samples with 100% specificity, 85% sensitivity, and AUC of 0.94. Data from this device suggest that label-free adhesive signatures may effectively predict clinical outcomes in patients.

Cells migrating in clusters play a significant role in a number of biological processes such as embryogenesis, wound healing, and tumor metastasis during cancer progression. A variety of environmental and biochemical factors can influence the collective migration of cells with differing degrees of cell autonomy and inter-cellular coupling strength. For example, weakly coupled cells can move collectively under the influence of contact guidance from neighboring cells or the environment. Alternatively strongly coupled cells might follow one or more leader cells to move as a single cohesive unit. Additionally, chemical and mechanical signaling between these cells may alter the degree of coupling and determine effective cluster sizes. Being able to understand this collective cell migration process is critical in the prediction and manipulation of outcomes of key biological processes. Here we focus on understanding how various environmental and cellular factors influence small clusters of cells migrating collectively within a 3D fibrous matrix. We combine existing knowledge of single-cell migration in 2D and 3D environments, prior experimental observations of cell-cell interactions and collective migration, and a newly developed stochastic model of cell migration in 3D matrices, to simulate the migration of cell clusters in different physiologically relevant environments. Our results show that based on the extracellular environment and the strength of cell-cell mechanical coupling, two distinct optimal approaches to driving collective cell migration emerge. The ability to effectively employ these two distinct migration strategies might be critical for cells to collectively migrate through the heterogeneous tissue environments within the body.

The active assembly of molecules by nanorobots has advanced greatly since “molecular manufacturing”—that is, the use of nanoscale tools to build molecular structures—was proposed. In contrast to a catalyst, which accelerates a reaction by smoothing the potential energy surface along the reaction coordinate, molecular machines expend energy to accelerate a reaction relative to the baseline provided by thermal motion and forces. Here, we design a nanorobotics system to accelerate end-to-end microtubule assembly by using kinesin motors and a circular confining chamber. We show that the mechanical interaction of kinesin-propelled microtubules gliding on a surface with the walls of the confining chamber results in a nonequilibrium distribution of microtubules, which increases the number of end-to-end microtubule fusion events 20-fold compared with microtubules gliding on a plane. In contrast to earlier nanorobots, where a nonequilibrium distribution was built into the initial state and drove the process, our nanorobotic system creates and actively maintains the building blocks in the concentrated state responsible for accelerated assembly through the adenosine triphosphate–fueled generation of force by kinesin-1 motor proteins. This approach can be used in the future to develop biohybrid or bioinspired nanorobots that use molecular machines to access nonequilibrium states and accelerate nanoscale assembly.

Calcium (Ca2+) is necessary for cardiac muscle contraction. RyR, NCX, and SERCA are key regulatory protein channels for cytosolic Ca2+ in cardiac myocytes. Expression levels of these proteins are a function of development, with protein expression shifting toward the adult phenotype over time. We investigated how downregulation by siRNAs of RyR and NCX affected expression levels of complimentary proteins and the corresponding intracellular Ca2+ transients. We compared experimentally observed Ca2+ transients to those predicted by mathematical models. Experiments show RyR downregulation decreased SERCA and increased NCX protein levels. The associated Ca2+ transient had a decreased amplitude, increased time-to-peak, 50%, and 90% Ca2+ removal with respect to the control cell. NCX downregulation increased SERCA production without significant changes in RyR expression levels. The corresponding [Ca2+] transient had increased amplitude, no change in time-to-peak and 50% Ca2+ removal, and increased 90% Ca2+ removal with respect to the control cell. Computational models that accurately predict the observed experimental data suggest compensatory changes occurring in the expression levels as well as biochemical activity of the regulatory proteins.

Materials can be endowed with unique properties by the integration of molecular motors. Molecular motors can have a biological origin or can be chemically synthesized and produce work from chemical energy or light. Their ability to access large internal or external reservoirs of energy enables a wide range of nonequilibrium behaviors, including the production of force, changes in shape, internal reorganization, and dynamic changes in mechanical properties—muscle tissue is one illustration of the possibilities. Current research efforts advance our experimental capabilities to create such “active matter” by using either biomolecular or synthetic motors, and also advance our theoretical understanding of these materials systems. Here, we introduce this exciting research field and highlight a few of the recent advances as well as open questions.

The study of protein adsorption at the single molecule level has recently revealed that the adsorption is reversible, but with a long-tailed residence time distribution which can be approximated with a sum of exponential functions putatively related to distinct adsorption sites. Here it is proposed that the shape of the residence time distribution results from an adsorption process with sequential and reversible steps that contribute to overall binding strength resembling "zippering". In this model, the survival function of the residence time distribution of single proteins varies from an exponential distribution for a single adsorption step to a power law distribution with exponent -1/2 for a large number of adsorption steps. The adsorption of fluorescently labeled fibrinogen to glass surfaces is experimentally studied with single molecule imaging. The experimental residence time distribution can be readily fit by the proposed model. This demonstrates that the observed long residence times can arise from stepwise adsorption rather than rare but strong binding sites and provides guidance for the control of protein adsorption to biomaterials.

Significant changes in cell stiffness, contractility and adhesion, i.e. mechanotype, are observed during a variety of biological processes. Whether cell mechanics merely change as a side effect of or driver for biological processes is still unclear. Here we sort genotypically similar metastatic cancer cells into strongly adherent (SA) versus weakly adherent (WA) phenotypes to study how contractility and adhesion differences alter the ability of cells to sense and respond to gradients in material stiffness. We observe that SA cells migrate up a stiffness gradient, or durotax, while WA cells largely ignore the gradient, i.e. adurotax. Biophysical modeling and experimental validation suggest that differences in cell migration and durotaxis between weakly and strongly adherent cells are driven by differences in intra-cellular actomyosin activity. These results provide a direct relationship between cell phenotype and durotaxis and suggest how, unlike other senescent cells, metastatic cancer cells navigate against stiffness gradients.

Tumors are heterogeneous and composed of cells with different dissemination abilities. Despite significant effort, there is no universal biological marker that serves as a metric for metastatic potential of solid tumors. Common to disseminating cells from such tumors, however, is the need to modulate their adhesion as they detach from the tumor and migrate through stroma to intravasate. Adhesion strength is heterogeneous even among cancer cells within a given population, and using a parallel plate flow chamber, we separated and sorted these populations into weakly and strongly adherent groups; when cultured under stromal conditions, this adhesion phenotype was stable over multiple days, sorting cycles, and common across all epithelial tumor lines investigated. Weakly adherent cells displayed increased migration in both two-dimensional and three-dimensional migration assays; this was maintained for several days in culture. Subpopulations did not show differences in expression of proteins involved in the focal adhesion complex but did exhibit intrinsic focal adhesion assembly as well as contractile differences that resulted from differential expression of genes involved in microtubules, cytoskeleton linkages, and motor activity. In human breast tumors, expression of genes associated with the weakly adherent population resulted in worse progression-free and disease-free intervals. These data suggest that adhesion strength could potentially serve as a stable marker for migration and metastatic potential within a given tumor population and that the fraction of weakly adherent cells present within a tumor could act as a physical marker for metastatic potential. Significance: Cancer cells exhibit heterogeneity in adhesivity, which can be used to predict metastatic potential.

Biofilms are communities of sessile microbes that are phenotypically distinct from their genetically identical, free-swimming counterparts. Biofilms initiate when bacteria attach to a solid surface. Attachment triggers intracellular signaling to change gene expression from the planktonic to the biofilm phenotype. For Pseudomonas aeruginosa, it has long been known that intracellular levels of the signal cyclic-di-GMP increase upon surface adhesion and that this is required to begin biofilm development. However, what cue is sensed to notify bacteria that they are attached to the surface has not been known. Here, we show that mechanical shear acts as a cue for surface adhesion and activates cyclic-di-GMP signaling. The magnitude of the shear force, and thereby the corresponding activation of cyclic-di-GMP signaling, can be adjusted both by varying the strength of the adhesion that binds bacteria to the surface and by varying the rate of fluid flow over surface-bound bacteria. We show that the envelope protein PilY1 and functional type IV pili are required mechanosensory elements. An analytic model that accounts for the feedback between mechanosensors, cyclic-di-GMP signaling, and production of adhesive polysaccharides describes our data well.

The ability of cells to deform and generate forces are key mechanical properties that are implicated in metastasis. While various soluble and mechanical cues are known to regulate cancer cell mechanical phenotype or mechanotype, our knowledge of how cells translate external signals into changes in mechanotype is still emerging. We previously discovered that activation of β-adrenergic signaling, which results from soluble stress hormone cues, causes cancer cells to be stiffer or less deformable; this stiffer mechanotype was associated with increased cell motility and invasion. Here, we characterize how β-adrenergic activation is translated into changes in cellular mechanotype by identifying molecular mediators that regulate key components of mechanotype including cellular deformability, traction forces, and non-muscle myosin II (NMII) activity. Using a micropillar assay and computational modelling, we determine that βAR activation increases cellular force generation by increasing the number of actin-myosin binding events; this mechanism is distinct from how cells increase force production in response to matrix stiffness, suggesting that cells regulate their mechanotype using a complementary mechanism in response to stress hormone cues. To identify the molecules that modulate cellular mechanotype with βAR activation, we use a high throughput filtration platform to screen the effects of pharmacologic and genetic perturbations on βAR regulation of whole cell deformability. Our results indicate that βAR activation decreases cancer cell deformability and increases invasion by signaling through RhoA, ROCK, and NMII. Our findings establish βAR-RhoA-ROCK-NMII as a primary signaling axis that mediates cancer cell mechanotype, which provides a foundation for future interventions to stop metastasis.

Cell mobility plays a critical role in immune response, wound healing, and the rate of cancer metastasis and tumor progression. Mobility within a three-dimensional (3D) matrix environment can be characterized by the average velocity of cell migration and the persistence length of the path it follows. Computational models that aim to predict cell migration within such 3D environments need to be able predict both of these properties as a function of the various cellular and extra-cellular factors that influence the migration process. A large number of models have been developed to predict the velocity of cell migration driven by cellular protrusions in 3D environments. However, prediction of the persistence of a cell's path is a more tedious matter, as it requires simulating cells for a long time while they migrate through the model extra-cellular matrix (ECM). This can be a computationally expensive process, and only recently have there been attempts to quantify cell persistence as a function of key cellular or matrix properties. Here, we propose a new stochastic algorithm that can simulate and analyze 3D cell migration occurring over days with a computation time of minutes, opening new possibilities of testing and predicting long-term cell migration behavior as a function of a large variety of cell and matrix properties. In this model, the matrix elements are generated as needed and stochastically based on the biophysical and biochemical properties of the ECM the cell migrates through. This approach significantly reduces the computational resources required to track and calculate cell matrix interactions. Using this algorithm, we predict the effect of various cellular and matrix properties such as cell polarity, cell mechanoactivity, matrix fiber density, matrix stiffness, fiber alignment, and fiber binding site density on path persistence of cellular migration and the mean squared displacement of cells over long periods of time.

Temperate bacterial viruses (phages) may enter a symbiosis with their host cell, forming a unit called a lysogen. Infection and viral replication are disassociated in lysogens until an induction event such as DNA damage occurs, triggering viral-mediated lysis. The lysogen-lytic viral reproduction switch is central to viral ecology, with diverse ecosystem impacts. It has been argued that lysogeny is favoured in phages at low host densities. This paradigm is based on the fraction of chemically inducible cells (FCIC) lysogeny proxy determined using DNA-damaging mitomycin C inductions. Contrary to the established paradigm, a survey of 39 inductions publications found FCIC to be highly variable and pervasively insensitive to bacterial host density at global, within-environment and within-study levels. Attempts to determine the source(s) of variability highlighted the inherent complications in using the FCIC proxy in mixed communities, including dissociation between rates of lysogeny and FCIC values. Ultimately, FCIC studies do not provide robust measures of lysogeny or consistent evidence of either positive or negative host density dependence to the lytic-lysogenic switch. Other metrics are therefore needed to understand the drivers of the lytic-lysogenic decision in viral communities and to test models of the host density-dependent viral lytic-lysogenic switch.

Biotinylated microtubules partially coated with streptavidin and gliding on a surface coated with kinesin motors can cross-link with each other and assemble into nanospools with a diameter of a few micrometres. The size distribution of these nanospools is determined, and it is shown with simulations of microtubule gliding that these spools are too small to be formed by thermally activated turns in the gliding direction (a Brownian ratchet mechanism). Instead, spool formation is primarily the result of two processes: pinning of gliding microtubules to inactive motors and simultaneous cross-linking of multiple microtubules.

Nanoscale sensors enable the detection of analytes with improved signal-to-noise ratio but suffer from mass transport limitations. Molecular shuttles, assembled from, e.g., antibody-functionalized microtubules and kinesin motor proteins, can selectively capture analytes from solution and deliver the analytes to a sensor patch. This two-stage process can accelerate mass transport to nanoscale biosensors and facilitate the rapid detection of analytes. Here, the possible increase of the signal-to-noise ratio is calculated, and the optimal layout of a system which integrates active transport is determined.

Malignant transformation, though primarily driven by genetic mutations in cells, is also accompanied by specific changes in cellular and extra-cellular mechanical properties such as stiffness and adhesivity. As the transformed cells grow into tumors, they interact with their surroundings via physical contacts and the application of forces. These forces can lead to changes in the mechanical regulation of cell fate based on the mechanical properties of the cells and their surrounding environment. A comprehensive understanding of cancer progression requires the study of how specific changes in mechanical properties influences collective cell behavior during tumor growth and metastasis. Here we review some key results from computational models describing the effect of changes in cellular and extra-cellular mechanical properties and identify mechanistic pathways for cancer progression that can be targeted for the prediction, treatment, and prevention of cancer.

Tumor growth and metastasis are ultimately mechanical processes involving cell migration and uncontrolled division. Using a 3D discrete model of cells, we show that increased compliance as observed for cancer cells causes them to grow at a much faster rate compared to surrounding healthy cells. We also show how changes in intercellular binding influence tumor malignancy and metastatic potential. These findings suggest that changes in the mechanical properties of cancer cells is the proximate cause of uncontrolled division and migration and various biochemical factors drive cancer progression via this mechanism.

We have been investigating an electrochemical single-molecule counting experiment called nanopore resistive-pulse sensing. The sensor element is a conically shaped gold nanotube embedded in a thin polymeric membrane. We have been especially interested in counting protein molecules using these nanotube sensors. This is accomplished by placing the nanotube membrane between two electrolyte solutions, applying a transmembrane potential difference, and measuring the resulting ionic current flowing through the nanopore. In simplest terms, when a protein molecule enters and translocates the nanopore, it transiently blocks the ion current, resulting in a downward current pulse. We have found that the duration of such current-pulses are many orders of magnitude longer than the electrophoretic transport time of the protein through the nanotube detection zone. We develop here a simple model that accounts for this key, and previously explained, observation. This model assumes that the protein molecule engages in repeated adsorption/desorption events to/from the nanotube walls as it translocates through the detection zone. This model not only accounts for the long pulse duration but also for the triangular shape of the current pulse and the increase in the standard deviation of the pulse duration with increasing protein size. Furthermore, the results of our analyses are in general agreement with results obtained from other investigations of protein adsorption to surfaces. This includes the observations that smaller proteins stick more readily to the surface but remain adsorbed for shorter times than larger proteins. In addition, the sticking probabilities calculated from our data are in general agreement with results obtained from other methods.

Surfaces grafted with poly(ethylene oxide) (PEO) are known to resist protein adsorption. Research efforts in this field have focused on both developing surfaces with better resistance to protein adsorption and understanding the origin of resistance of PEO grafted surfaces to protein adsorption. In the first part of this contribution, we describe a novel quantification technique for extremely low protein coverage on surfaces. This technique utilizes measurement of the landing rate of microtubule filaments on kinesin proteins adsorbed on a surface to determine the kinesin density. The detection limit of our technique is 100 times lower than that of standard characterization methods and is employed to test the performance of novel and established coatings with outstanding resistance to protein adsorption. In the second part, a random sequential adsorption (RSA) model is presented for protein adsorption to PEO coated surfaces. The model suggests that PEO chains act as almost perfect steric barriers to protein adsorption. Furthermore, it can be used to predict the performance of a variety of systems towards resisting protein adsorption and can help in the design of better non-fouling surface coatings.

It has been recently discovered that biological systems exploit complex intermolecular bonds, such as the "catch bond" between FimH proteins and mannose, to regulate dynamic assembly processes. Here we show that the assembly of hybrid nanostructures also has to account for and ideally exploit the subtleties of the utilized intermolecular interactions. We focus on the biotin-streptavidin bond, which gains its ultimate strength on a time scale of milliseconds, due to existence of metastable binding states. As a consequence of the gluelike character of this widely used intermolecular bond, the velocity of molecular shuttles, active nanoscale transport systems based on biomolecular motors and their associated filaments, has to be optimized to permit efficient attachment of cargo via biotin-streptavidin linkages. The attachment process can only be understood by combining rigorous mechanical engineering analysis with detailed physicochemical models.

Modified kinesin gliding motility assays display striking self-assembly phenomena,[1] in particular the formation of non-equilibrium structures. Biotinylated microtubules partially coated with streptavidin form “wires” and “spools” while gliding on kinesins adhered to the surface.[2] The spool formation process was investigated in detail, and we found that the assembled spools exhibit a narrow distribution of spool diameters, and that the average diameter is an order of magnitude smaller than what would be obtained from a thermally driven assembly process. We also observe that pinning of the microtubule leading tip on the surface initiates the spool formation in 80% of the cases. By modeling the mechanism of microtubule tip buckling and the resultant microtubule spool diameter as a function of the microtubule persistence length and the number of motors attached to the microtubule, we can predict the average spool diameter and the observed spool diameter distribution. The model suggests a strong dependence of spool diameters and size distribution on the surface kinesin density. The goal of our research is to obtain a better understanding of the dynamics of this multi-agent process and its implications for self-assembly in general.[1] Hess, H. Self-assembly driven by molecular motors. Soft Matter 2 (8), 669-677 (2006).[2] Hess, Henry et al. Molecular self-assembly of “Nanowires” and “Nanospools” using active transport. Nano Letters 5 (4), 629-633 (2005).

Biomolecular motors, such as the motor protein kinesin, are simultaneously objects of scientific inquiry and components for nanotechnology. The investigation of the properties of a biomolecular motor is challenging, since it is a dynamic nanoscale object but at the same time soft and fragile. Photonic techniques are well suited to these investigations due to their compatibility with an aqueous environment and their non-destructive character, however their resolution is often insufficient. We adapted Fluorescence Interference Contrast (FLIC) microscopy to the imaging of microtubules transported by kinesin motors (PNAS vol. 103, p. 15812) and achieved nm-resolution in the z-direction. This advance provided insights into the role of the kinesin tail for the functioning of the motor in vivo, but also enabled us to determine the "ground clearance" of molecular shuttles powered by kinesin motors. Kinesin-driven molecular shuttles, in turn, enable the design of highly integrated bionanodevices. Photons are the most suitable tool to communicate with such devices, since they can address molecules and nanoparticles packaged into the devices without the need for a physical connection.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A challenge for nanotechnology is the dynamic and specific control of nanomachines by the user. Molecular shuttles, consisting of cargo-binding microtubules propelled by surface-immobilized kinesin motor proteins, are an example of a nanoscale system that ideally can be selectively activated at programmable locations and times. Here we discuss a biomimetic solution where activating molecules are delivered locally via photolysis of a caged compound and subsequently sequestered in an enzymatic network. The controlled sequestration of the activator not only creates a rapid deactivation when the stimulus is removed but also sharpens the concentration profile of the rapidly diffusing activator. This improvement comes at the expense of a reduced efficiency in the utilization of the activator molecules, suggesting that these nanosystems are most efficiently addressed as a swarm rather than as individuals. Our work represents a step toward transferring the cellular control strategies of molecular activation to bionanotechnology.

Antibiotic resistance adversely affects clinical and public health on a global scale. Using the opportunistic human pathogen Pseudomonas aeruginosa , we show that increasing the number density of bacteria, on agar containing aminoglycoside antibiotics, can non-monotonically impact the survival of antibiotic-resistant mutants. Notably, at high cell densities, mutant survival is inhibited. A wide range of bacterial species can inhibit antibiotic-resistant mutants. Inhibition results from the metabolic breakdown of amino acids, which results in alkaline by-products. The consequent increase in pH acts in conjunction with aminoglycosides to mediate inhibition. Our work raises the possibility that the manipulation of microbial population structure and nutrient environment in conjunction with existing antibiotics could provide therapeutic approaches to combat antibiotic resistance.

A variety of genetic factors causing up-regulation or down-regulation of key enzymes have been attributed to affect the malignancy of tumors. Yet, the ability to predict the aggressiveness of tumors in terms of their growth rate and metastatic potential is fairly limited. We hypothesize that the genetic and enzymatic factors only indirectly influence tumor growth and metastasis and that these processes are primarily physical in nature. This is based on observations that the physical state of a cell plays a major role in determining its fate - division, survival, migration and apoptosis. The physical state of a cell (shape and size) is a direct function of the physical properties of the cell such as the osmotic pressure, the cytoskeletal stiffness and the strength of inter-cellular and cell-matrix bonding. We have developed a computational model describing the physical state of discrete cells in a 3-dimensional tissue environment as a function of the physical properties of cells. Using this model and a cell shape dependent cell death and division rate we recreate the conditions for cell turnover and tissue homeostasis. We then probe the influence of changes in a cell's physical properties on its division rate and apoptosis. The changes in physical properties that cause an uncontrolled growth and division of cells can be identified. Cells with these characteristics correspond in nature to tumor cells. Computer simulations based on this model provide information on the growth rate of particular tumors with cells displaying specific physical properties as well as the shape evolution of these tumors. The identification of changes in physical properties of a cell that drive tumor cell growth and division helps isolate key genetic and enzymatic factors that influence these properties and the mechanism by which they drive tumor progression.