Limits of feedback control in bacterial chemotaxis

Inputs to signaling pathways can have complex statistics that depend on the environment and on the behavioral response to previous stimuli. Such behavioral feedback is particularly important in navigation. Successful navigation relies on proper coupling between sensors, which gather information during motion, and actuators, which control behavior. Because reorientation conditions future inputs, behavioral feedback can place sensors and actuators in an operational regime different from the resting state. How then can organisms maintain proper information transfer through the pathway while navigating diverse environments? In bacterial chemotaxis, robust performance is often attributed to the zero integral feedback control of the sensor, which guarantees that activity returns to resting state when the input remains constant. While this property provides sensitivity over a wide range of signal intensities, it remains unclear how other parameters affect chemotactic performance, especially when considering that the swimming behavior of the cell determines the input signal. Using analytical models and simulations that incorporate recent experimental evidences about behavioral feedback and flagellar motor adaptation we identify an operational regime of the pathway that maximizes drift velocity for various environments and sensor adaptation rates. This optimal regime is outside the dynamic range of the motor response, but maximizes the contrast between run duration up and down gradients. In steep gradients, the feedback from chemotactic drift can push the system through a bifurcation. This creates a non-chemotactic state that traps cells unless the motor is allowed to adapt. Although motor adaptation helps, we find that as the strength of the feedback increases individual phenotypes cannot maintain the optimal operational regime in all environments, suggesting that diversity could be beneficial.Comment: Corrected one typo. First two authors contributed equally. Notably, there were various typos in the values of the parameters in the model of motor adaptation. The results remain unchange

Stripe formation in bacterial systems with density-suppressed motility

Engineered bacteria in which motility is reduced by local cell density generate periodic stripes of high and low density when spotted on agar plates. We study theoretically the origin and mechanism of this process in a kinetic model that includes growth and density-suppressed motility of the cells. The spreading of a region of immotile cells into an initially cell-free region is analyzed. From the calculated front profile we provide an analytic ansatz to determine the phase boundary between the stripe and the no-stripe phases. The influence of various parameters on the phase boundary is discussed.Comment: 5 pages, 3 figures. Phys. Rev. Lett. in press (2012

Building a global alliance of biofoundries (vol 10, 2040, 2019)

The original version of this Comment contained errors in the legend of Figure 2, in which the locations of the fifteenth and sixteenth GBA members were incorrectly given as '(15) Australian Genome Foundry, Macquarie University; (16) Australian Foundry for Advanced Biomanufacturing, University of Queensland.'. The correct version replaces this with '(15) Australian Foundry for Advanced Biomanufacturing (AusFAB), University of Queensland and (16) Australian Genome Foundry, Macquarie University'. This has been corrected in both the PDF and HTML versions of the Comment

Quantitative study of pattern formation on a density-dependent motility biological system

Quantitative biology is an emerging field that attracts intensive research interests. Pattern formation is a widely studied topic both in biology and physics. Scientists have been trying to figure out the basic principles behind the fascinating patterns in the nature. It’s still difficult to lift the complex veil on the underling mechanisms, especially in biology, although lots of the achievements have been achieved. The new developments in synthetic biology provide a different approach to study the natural systems, test the theories, and develop new ones. Biological systems have many unique features different from physics and chemistry, such as growth and active movement. In this project, a link between cell density and cell motility is established through cell-cell signaling. The genetic engineered Escherichia coli cell regulates its motility by sensing the local cell density. The regulation of cell motility by cell density leads to sequential and periodical stripe patterns when the cells grow and expand on a semi-solid agar plate. This synthetic stripe pattern formation system is quantitative studied by quantitative measurements, mathematical modeling and theoretical analysis. To characterize the stripe pattern, two novel methods have been developed to quantify the key parameters, including cell growth, spatiotemporal cell density profile and cell density-dependent motility, besides the standard molecular biological measurements. To better understand the underlying principle of the stripe pattern formation, a quantitative model is developed based on the experiments. The detailed dynamic process is studied by computer simulation. Besides, the model predicts that the number of stripes can be tuned by varying the parameters in the system. This has been tested by quantitatively modulation of the basal expression level of a single gene in the genetic circuit. Moreover, theoretical analysis of a simplified model provides us a clear picture of the stripe formation process. The steady state traveling wave solution is obtained, which leads to an analytic ansatz that can determine the phase boundary between the stripe and the no-stripe phases. This study does not only provide a quantitative understanding about the novel mechanism of stripe pattern formation, but also sets an good example of quantitative studies in biology. The techniques, methods and knowledge gleaned here may be applied in various interdisciplinary fields.published_or_final_versionPhysicsDoctoralDoctor of Philosoph

DeephageTP: a convolutional neural network framework for identifying phage-specific proteins from metagenomic sequencing data

Bacteriophages (phages) are the most abundant and diverse biological entity on Earth. Due to the lack of universal gene markers and database representatives, there about 50–90% of genes of phages are unable to assign functions. This makes it a challenge to identify phage genomes and annotate functions of phage genes efficiently by homology search on a large scale, especially for newly phages. Portal (portal protein), TerL (large terminase subunit protein), and TerS (small terminase subunit protein) are three specific proteins of Caudovirales phage. Here, we developed a CNN (convolutional neural network)-based framework, DeephageTP, to identify the three specific proteins from metagenomic data. The framework takes one-hot encoding data of original protein sequences as the input and automatically extracts predictive features in the process of modeling. To overcome the false positive problem, a cutoff-loss-value strategy is introduced based on the distributions of the loss values of protein sequences within the same category. The proposed model with a set of cutoff-loss-values demonstrates high performance in terms of Precision in identifying TerL and Portal sequences (94% and 90%, respectively) from the mimic metagenomic dataset. Finally, we tested the efficacy of the framework using three real metagenomic datasets, and the results shown that compared to the conventional alignment-based methods, our proposed framework had a particular advantage in identifying the novel phage-specific protein sequences of portal and TerL with remote homology to their counterparts in the training datasets. In summary, our study for the first time develops a CNN-based framework for identifying the phage-specific protein sequences with high complexity and low conservation, and this framework will help us find novel phages in metagenomic sequencing data. The DeephageTP is available at https://github.com/chuym726/DeephageTP

Enantioselective Recognition Mechanism of Ofloxacin via Cu(II)-Modulated DNA

The specific interactions of Cu²⁺ with self-complementary DNA sequences involving d­[G₄C₄(GC)₂G₄C₄], d­[(GC)₁₀], and d­[(AT)₁₀], as well as the chiral recognition mechanism of ofloxacin enantiomers via the Cu^II-modulated DNAs, were investigated using characterizations of circular dichroism, gel electrophoresis, FT-IR spectroscopy, UV melting measurement, electron paramagnetic resonance, and HPLC. The Cu^II-coordinated GC-rich DNAs exhibit amplified enantioselectivity toward the <i>S</i>-enantiomer of ofloxacin. Especially in the case of d­[G₄C₄(GC)₂G₄C₄], ofloxacin enantiomers intercalate into the two adjacent guanine bases through the minor groove mediated by Cu²⁺, which leads to a more favorable binding between <i>S</i>-ofloxacin and DNA. The highest ee value of ofloxacin enantiomers in the permeate after being adsorbed by the Cu^II–DNA complex is obtained as 49.2% in the <i>R</i>-enantiomer at the [Cu²⁺]/[base] molar ratio of 0.25, while at the [Cu²⁺]/[base] molar ratio of 0.05 the highest ee value of ofloxacin enantiomers in the retentate reaches 26.3% in the <i>S</i>-enantiomer. This work illustrates a novel promising route to construct DNA-based chiral selectors toward certain drug enantiomers through the programmable enantioselective recognition on the basis of DNA chirality and the specific binding of transition metal ions

Effect of Gd on microstructure and corrosion behavior of Mg-xGd-1Er-1Zn-0.6Zr alloys

The Mg-xGd-1Er-1Zn-0.6Zr alloys with Gd contents of 7%(mass fraction), 9% and 11% were prepared by gravity casting method.The microstructure of the alloys was studied by means of optical microscope, scanning electron microscope and X-ray diffractometer.The corrosion behavior of the alloys were evaluated by means of open circuit potential, potentiodynamic polarization and electrochemical impedance spectroscopy measurements in 3.5%NaCl solution.The results show that when Gd content increases from 7% to 11%, the peak time of open circuit potential decreases from 1609 s to 851 s, the charge transfer resistance decreases from 588.50 Ω to 31.9 Ω, the corrosion current density increases from 2.21×10-5 A/cm2 to 3.97×10-5 A/cm2, indicating that the corrosion resistance of the alloys decreases with the increase of Gd content.It is attributed to the combined operation of the micro-galvanic corrosion effect as well as corrosion barrier effect of second phase.When the Gd content increases from 7% to 11%, the volume fraction of (Mg, Zn)3(Gd, Er) phase increases from 1.9% to 5.2%, and changes from discontinuous distribution to semi-continuous distribution along grain boundaries, the volume fraction of the lamellar-shape LPSO phase increases from 11.7% to 26.7% and penetrates into grains.The increase in the volume fraction of the (Mg, Zn)3(Gd, Er) phase and the lamellar-shape LPSO phase results in the decrease of corrosion resistance, however, a large number of fine lamellar-shape LPSO phases is able to prevent the corrosion from spreading and slow down the growth of corrosion rate of the alloy with 11%Gd content in 8-24 h

CK20 mRNA expression in serum as a biomarker for colorectal cancer diagnosis: A meta-analysis

The aim of this study was to evaluate the diagnostic value of serumCK20 mRNA as a biomarker for colorectal cancer diagnosis by meta-analysis