Low-dose probenecid selectively inhibits urinary excretion of phenolsulfonphthalein in rats without affecting biliary excretion.

Renal organic anion transport systems play an important role in the excretion of anionic drugs and toxic compounds. Probenecid has been used as a potent inhibitor of urinary and biliary excretion of anionic compounds mediated by transporters such as organic anion transporters and multidrug resistance-associated protein 2 (Mrp2). The purpose of this study was to optimize the dose of probenecid required for selective inhibition of urinary excretion of anionic compounds in rats, without inhibition of biliary excretion. Phenolsulfonphthalein (PSP), a model anionic compound that is excreted in urine and bile, was intravenously administered to rats after intraperitoneal injection of different doses of probenecid (0, 0.2, 2, 10, 100, 200 and 400 mg kg(-1) ). Treatment with 100, 200 or 400 mg kg(-1) probenecid decreased both renal clearance (CLr ) and biliary clearance (CLb ) of PSP, whereas 0.2 mg kg(-1) probenecid did not have any effect. Probenecid administered at doses of 2 and 10 mg kg(-1) decreased only CLr . The median effective doses of probenecid for inhibiting CLr and CLb were 0.925 and 23.9 mg kg(-1) , respectively. These data suggest that a low dose of probenecid selectively inhibits urinary excretion of PSP that may be mediated by organic anion transporters, without affecting biliary excretion that may be mediated by Mrp2.

Degradation-driven protein level oscillation in the yeast Saccharomyces cerevisiae.

Generating robust, predictable perturbations in cellular protein levels will advance our understanding of protein function and enable the control of physiological outcomes in biotechnology applications. Timed periodic changes in protein levels play a critical role in the cell division cycle, cellular stress response, and development. Here we report the generation of robust protein level oscillations by controlling the protein degradation rate in the yeast Saccharomyces cerevisiae. Using a photo-sensitive degron and red fluorescent proteins as reporters, we show that under constitutive transcriptional induction, repeated triangular protein level oscillations as fast as 5-10 min-scale can be generated by modulating the protein degradation rate. Consistent with oscillations generated though transcriptional control, we observed a continuous decrease in the magnitude of oscillations as the input modulation frequency increased, indicating low-pass filtering of input perturbation. By using two red fluorescent proteins with distinct maturation times, we show that the oscillations in protein level is largely unaffected by delays originating from functional protein formation. Our study demonstrates the potential for repeated control of protein levels by controlling the protein degradation rate without altering the transcription rate.

Digital Signal Processing and Control for the Study of Gene Networks.

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Modeling collective & intelligent decision making of multi-cellular populations.

In the presence of unpredictable disturbances and uncertainties, cells intelligently achieve their goals by sharing information via cell-cell communication and making collective decisions, which are more reliable compared to individual decisions. Inspired by adaptive sensor network algorithms studied in communication engineering, we propose that a multi-cellular adaptive network can convert unreliable decisions by individual cells into a more reliable cell-population decision. It is demonstrated using the effector T helper (a type of immune cell) population, which plays a critical role in initiating immune reactions in response to invading foreign agents (e.g., viruses, bacteria, etc.). While each individual cell follows a simple adaptation rule, it is the combined coordination among multiple cells that leads to the manifestation of "self-organizing" decision making via cell-cell communication.

Micelle-mediated extraction of dibenzocyclooctadiene lignans from Schisandra chinensis with analysis by high-performance liquid chromatography.

Micelle-mediated extraction offers a convenient alternative to conventional extraction systems. A new method based on micelle-mediated extraction was developed for the separation and determination of dibenzocyclooctadiene lignans from Schisandra chinensis by high-performance liquid chromatography with photodiode array detection. Various experimental conditions using the micelle-mediated method were investigated to evaluate the extraction process. Ethylene glycol monoalkyl ether (Genapol X-080), a non-ionic surfactant oligoethylene glycol monoalkyl ether, was chosen as the extract solvent. The chromatographic separation was accomplished on a Shiseido Capcell Pak C18 analytical column (250 × 4.6mm i.d., 5 µm particle diameter), detected by ultraviolet absorption at 254 nm. The isocratic elution was achieved with a mobile phase composed of water-acetonitrile-formic acid (70:30:0.1) at a flow rate of 0.6 mL/min. The method was optimized and fully validated against dibenzocyclooctadiene lignans (schizandrin, gomisin A and gomisin N). With 15% Genapol X-080, a liquid to solid ratio of 100:1 (mL/g) and ultrasonic-assisted extraction for 60 min, the extraction percentage of total dibenzocyclooctadiene lignans reached the highest value. The non-ionic surfactant Genapol X-080 solution is an effective alternative for the extraction of bioactive lignans from S. chinensis.

Post-translational regulation enables robust p53 regulation.

BACKGROUND: The tumor suppressor protein p53 plays important roles in DNA damage repair, cell cycle arrest and apoptosis. Due to its critical functions, the level of p53 is tightly regulated by a negative feedback mechanism to increase its tolerance towards fluctuations and disturbances. Interestingly, the p53 level is controlled by post-translational regulation rather than transcriptional regulation in this feedback mechanism. RESULTS: We analyzed the dynamics of this feedback to understand whether post-translational regulation provides any advantages over transcriptional regulation in regard to disturbance rejection. When a disturbance happens, even though negative feedback reduces the steady-state error, it can cause a system to become less stable and transiently overshoots, which may erroneously trigger downstream reactions. Therefore, the system needs to balance the trade-off between steady-state and transient errors. Feedback control and adaptive estimation theories revealed that post-translational regulation achieves a better trade-off than transcriptional regulation, contributing to a more steady level of p53 under the influence of noise and disturbances. Furthermore, post-translational regulation enables cells to respond more promptly to stress conditions with consistent amplitude. However, for better disturbance rejection, the p53- Mdm2 negative feedback has to pay a price of higher stochastic noise. CONCLUSIONS: Our analyses suggest that the p53-Mdm2 feedback favors regulatory mechanisms that provide the optimal trade-offs for dynamic control.

A microRNA miR-34a-regulated bimodal switch targets Notch in colon cancer stem cells.

microRNAs regulate developmental cell-fate decisions, tissue homeostasis, and oncogenesis in distinct ways relative to proteins. Here, we show that the tumor suppressor microRNA miR-34a is a cell-fate determinant in early-stage dividing colon cancer stem cells (CCSCs). In pair-cell assays, miR-34a distributes at high levels in differentiating progeny, whereas low levels of miR-34a demarcate self-renewing CCSCs. Moreover, miR-34a loss of function and gain of function alter the balance between self-renewal versus differentiation both in vitro and in vivo. Mechanistically, miR-34a sequesters Notch1 mRNA to generate a sharp threshold response where a bimodal Notch signal specifies the choice between self-renewal and differentiation. In contrast, the canonical cell-fate determinant Numb regulates Notch levels in a continuously graded manner. Altogether, our findings highlight a unique microRNA-regulated mechanism that converts noisy input into a toggle switch for robust cell-fate decisions in CCSCs.

Adaptive models for gene networks.

Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.

Using an adaptive gene network model for self-organizing multicellular behavior.

Using the transient interleukin (IL)-2 secretion of effector T helper (T(eff)) cells as an example, we show that self-organizing multicellular behavior can be modeled and predicted by an adaptive gene network model. Incorporating an adaptation algorithm we established previously, we construct a network model that has the parameter values iteratively updated to cope with environmental change governed by diffusion and cell-cell interactions. In contrast to non-adaptive models, we find that the proposed adaptive model for individual T(eff) cells can generate transient IL-2 secretory behavior that is observed experimentally at the population level. The proposed adaptive modeling approach can be a useful tool in the study of self-organizing behavior observed in other contexts in biology, including microbial pathogenesis, antibiotic resistance, embryonic development, tumor formation, etc.

Pharmacokinetic interactions of clopidogrel with quercetin, telmisartan, and cyclosporine A in rats and dogs.

In this study, we investigated pharmacokinetic drug interactions of clopidogrel with P-gp inhibitors in rats and dogs. Following the oral administration of clopidogrel with or without the P-gp inhibitors, quercetin (250 mg/kg), telmisartan (8 mg/kg), and cyclosporine A (10 mg/kg), in rats and dogs, the plasma concentration-time profiles of clopidogrel carboxylic acid, a surrogate marker for the bioavailability of clopidogrel, were determined. Co-administration of the quercetin, telmisartan and cyclosporine A significantly increased the area under the curve and peak plasma concentration of clopidogrel carboxylic acid in rats. However, in dogs, the plasma concentrations of clopidogrel carboxylic acid were not considerably changed by the coadministration of three different kinds of P-gp inhibitors. These findings suggest potential interaction of clopidogrel with quercetin, telmisartan, and cyclosporine A, although there are differences between animal models. Follow-up clinical study is needed to explore the meaning of this remarkable species differences in the P-gp-mediated interaction.

Frequency domain analysis reveals external periodic fluctuations can generate sustained p53 oscillation.

p53 is a well-known tumor suppressor protein that regulates many pathways, such as ones involved in cell cycle and apoptosis. The p53 levels are known to oscillate without damping after DNA damage, which has been a focus of many recent studies. A negative feedback loop involving p53 and MDM2 has been reported to be responsible for this oscillatory behavior, but questions remain as how the dynamics of this loop alter in order to initiate and maintain the sustained or undamped p53 oscillation. Our frequency domain analysis suggests that the sustained p53 oscillation is not completely dictated by the negative feedback loop; instead, it is likely to be also modulated by periodic DNA repair-related fluctuations that are triggered by DNA damage. According to our analysis, the p53-MDM2 feedback mechanism exhibits adaptability in different cellular contexts. It normally filters noise and fluctuations exerted on p53, but upon DNA damage, it stops performing the filtering function so that DNA repair-related oscillatory signals can modulate the p53 oscillation. Furthermore, it is shown that the p53-MDM2 feedback loop increases its damping ratio allowing p53 to oscillate at a frequency more synchronized with the other cellular efforts to repair the damaged DNA, while suppressing its inherent oscillation-generating capability. Our analysis suggests that the overexpression of MDM2, observed in many types of cancer, can disrupt the operation of this adaptive mechanism by making it less responsive to the modulating signals after DNA damage occurs.

Danshen extract does not alter pharmacokinetics of docetaxel and clopidogrel, reflecting its negligible potential in P-glycoprotein- and cytochrome P4503A-mediated herb-drug interactions.

Danshen (Salvia miltiorrhiza) contains tanshinones, which inhibit P-glycoprotein (P-gp) and the cytochrome P450 (CYP) system. In the present study, we evaluated the possible pharmacokinetic interactions of Danshen extract with docetaxel and clopidogrel in rats. Docetaxel (5 mg/kg intravenously and 40 mg/kg orally) or clopidogrel (30 mg/kg orally) was administered to rats with or without oral co-administration of Danshen (400 mg/kg). Co-administration of Danshen did not affect the plasma concentration profiles and pharmacokinetic parameters of docetaxel and clopidogrel, whereas cyclosporine A, a P-gp and CYP3A inhibitor, significantly influenced the pharmacokinetics of co-administered docetaxel and clopidogrel. Orally administered Danshen had no substantial effect on the pharmacokinetics of docetaxel and clopidogrel, suggesting the negligible safety concern of Danshen in P-gp- and CYP3A-mediated interactions in vivo.

Linear control theory for gene network modeling.

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Statecharts for gene network modeling.

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks.

Machine vision for digital microfluidics.

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.