Bacterial biofilms represent an important medical problem; however, the mechanisms of the onset of biofilm formation are poorly understood. Here, using new controlled methods allowing high-throughput and reproducible biofilm growth, we show that biofilm formation is linked to self-imposed mechanical stress. In growing uropathogenic Escherichia coli colonies, we report that mechanical stress can initially emerge from the physical stress accompanying colony confinement within micro-cavities or hydrogel environments reminiscent of the cytosol of host cells. Biofilm formation can then be enhanced by a nutrient access-modulated feedback loop, in which biofilm matrix deposition can be particularly high in areas of increased mechanical and biological stress, with the deposited matrix further enhancing the stress levels. This feedback regulation can lead to adaptive and diverse biofilm formation guided by the environmental stresses. Our results suggest previously unappreciated mechanisms of the onset and progression of biofilm growth.

14Cell communication and coordinated cell behavior are hallmarks of multicellular behavior of 15 living systems. However, in many cases, including the ancient and archetypal example of 16 bacterial quorum sensing, the meaning of the communicated information remains a subject of 17 debate. It is commonly assumed that quorum sensing encodes the information on the current 18 state of the colony, including cell density and physical colony confinement. Here, we show that 19 quorum sensing can also be exquisitely sensitive to dynamic changes in the environment, 20including fluctuations of the prevailing nutrient source. We propose a new signaling mechanism 21 accounting for this sensory capability. This mechanism combines regulation by the commonly 22 studied lux operon-encoded network with the environmentally determined balance of protein 23 synthesis and dilution rates, dependent on the rate of cell proliferation. This regulatory 24 mechanism accounts for observed complex spatial distribution of quorum responses, and 25 emergence of sophisticated processing of dynamic inputs, including temporal thresholds and 26 persistent partial induction following a transient change in the environmental conditions. We 27 further show that, in this context, cell communication in quorum sensing acquires a new 28 meaning: education of cells within a population about the past history of transient exposure to 29 adverse conditions by a subset of induced cells. In combination, these signaling and 30 communication features may endow a cell population with increased fitness in diverse 31 fluctuating environments. 32 33 high cell density might also hamper nutrient availability 22 ; however, neither CD nor DF sensing 51 interpretations directly relate to the detection of changes in the environmental conditions, 52 particularly of subtle fluctuations in nutrient content. It is not clear, therefore, whether QS might 53in fact be a mechanism to sense dynamic changes in the potentially stressful, dynamic cell 54 microenvironment 23 , or it just enables this sensing (e.g., in a cell density-dependent fashion) 55 through alternative sensory mechanisms 24 . 56 3 57To explore the function of QS, it is important to investigate the dynamic features of QS 58 responses. However, the emphasis has traditionally been placed on the categorical on or off 59 description, stemming from the common feature of the molecular circuits underlying QS -the 60 positive feedback. Positive autoregulation is observed, for example, in a frequently studied QS 61 circuit of the marine bacterium V. fischeri, whose response is controlled by the lux operon 25 . It 62 involves AI, produced by the AI synthase LuxI, binding to its cognate cytoplasmic AI receptor 63LuxR, and positively auto-regulating the LuxI and LuxR in addition to driving bioluminescence 64 gene transcription 26-28 . This simple feedback regulatory circuit enables a switch-like increase in 65 the QS response after the threshold concentration of AI is exceeded 29 . However, the time scale of 66 this response and...

Purpose Troxacitabine (TROX) is a L‐cytidine analogue anticancer agent currently in phase II/III trials. The study's objective is to develop & validate a population pharmacokinetic (PPK) model for TROX. Methods Plasma samples from 111 cancer patients receiving IV doses of 0.12 – 12.5 mg/m2 were used to develop the PPK model with NONMEM. About 13 samples per patient were obtained from the 1st dose. 2 covariate groups (I: BSA, SEX, AGE, SCR; II: WT, HT, SEX, AGE, SCR) & PK parameters were evaluated by linear multiple regression. The 2 final PPK models were validated by internal & external methods. Results TROX PPK was characterized by a 3‐compartment model, exponential interpatient variability (IPV) error model, combination residual error model, & FOCE INTER estimator method. Clearance was influenced by BSA (27% decrease IPV) or WT (20% decrease IPV). Central compartmental volume was influenced by BSA (12% decrease IPV). Model validations reveal both final models accurate in predicting plasma TROX concentrations with improved PK parameter predictions with the addition of covariates. Conclusion Covariate modeling supports the use of BSA in current dosing strategies for TROX. Clinical Pharmacology & Therapeutics (2004) 75, P82–P82; doi: