Opini Komunitas Warga Sekitar Tentang Maraknya Pedagang Kaki Lima (PKL) (Studi Deskriptif Analitis Tentang Opini Komunitas Warga Sekitar Pkl – Tamansari, Kepatihan, dan Dalem Kaum – Kota Bandung)

Penelitian dengan judul “Opini komunitas warga sekitar tentang maraknya Pedagang Kaki Lima (PKL)” ini, dilakukan oleh pengajar/dosen tetap Fakultas Ilmu Komunikasi (FIK). Permasalahan penelitian adalah tentang bagaimana opini komunitas warga sekitar PKL mengenai keamanan, ketertiban, ketenangan, Kenyamanan, keindahan, kebersihan, dan keramah-tamahan (7“K”) akibat maraknya PKL. Sasaran strategis dalam penelitian ini adalah komunitas warga di sekitar lingkungan PKL Jalan Kepatihan, Dalem Kaum, dan Tamansari.Tujuan penelitian adalah untuk mengetahui, mengkaji, dan menganalisis faktor 7“K” yang dirasakan komunitas warga sekitar, akibat maraknya PKL, sehingga tanggapan yang diekspresikan mereka dapat menjadi masukan bagi Humas Pemerintah Kota Bandung dalam upaya mensosialisasikan kebijakan pemerintah tentang PKL khususnya dalam merumuskan konsep community relations berkaitan dengan 7 “K” yang dirasakan oleh komunitas warga sekitar terhadap maraknya PKL tersebut. Kesimpulan hasil penelitian ini adalah: pada umumnya opini komunitas warga sekitar terhadap maraknya PKL, dilihat dari faktor 7“K” sangatlah bervariasi di antara opini positif dan negatif, Dalam arti, untuk responden tertentu penilaiannya sangat relatif tergantung dari persepsi masing-masing dan atas dasar pengalaman masing-masing dengan para PKL tersebut. Dengan demikian tidak sepenuhnya berada pada kecenderungan tertentu yang bersifat negatif atau positif. Oleh karena itu dari opini tersebut selanjutnya dapat berkembang untuk diyakini tentang adanya kemungkinan di antara kedua belah pihak saling membina hubungan, dan pemerintah memfasilitasi hubungan tersebut dalam kebijakan-kebijakannya

Measurements of Gene Expression at Steady State Improve the Predictability of Part Assembly

Mathematical modeling of genetic circuits generally assumes that gene expression is at steady state when measurements are performed. However, conventional methods of measurement do not necessarily guarantee that this assumption is satisfied. In this study, we reveal a bi-plateau mode of gene expression at the single-cell level in bacterial batch cultures. The first plateau is dynamically active, where gene expression is at steady state; the second plateau, however, is dynamically inactive. We further demonstrate that the predictability of assembled genetic circuits in the first plateau (steady state) is much higher than that in the second plateau where conventional measurements are often performed. By taking the nature of steady state into consideration, our method of measurement promises to directly capture the intrinsic property of biological parts/circuits regardless of circuit–host or circuit–environment interactions

Automated Design of Genetic Toggle Switches with Predetermined Bistability

Synthetic biology aims to rationally construct biological devices with required functionalities. Methods that automate the design of genetic devices without post-hoc adjustment are therefore highly desired. Here we provide a method to predictably design genetic toggle switches with predetermined bistability. To accomplish this task, a biophysical model that links ribosome binding site (RBS) DNA sequence to toggle switch bistability was first developed by integrating a stochastic model with RBS design method. Then, to parametrize the model, a library of genetic toggle switch mutants was experimentally built, followed by establishing the equivalence between RBS DNA sequences and switch bistability. To test this equivalence, RBS nucleotide sequences for different specified bistabilities were <i>in silico</i> designed and experimentally verified. Results show that the deciphered equivalence is highly predictive for the toggle switch design with predetermined bistability. This method can be generalized to quantitative design of other probabilistic genetic devices in synthetic biology

Additional file 4: Figure S4. of Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library

Morphological analysis of the Keio collection reveals correlations between cell width and intracellular width variability. Contours from cells from each Keio deletion strain were extracted from images acquired from the NBRP repository and used to compute the mean width and width profile across each cell. For each cell, we then computed the standard deviation of the width profile divided by the mean width to obtain the intracellular width variability. White circles and error bars were obtained by binning strains by mean width; blue lines are the fit to binned averages. R is Pearson’s correlation coefficient; p-value was computed with Student’s t-test. (PDF 111 kb

A Formalized Design Process for Bacterial Consortia That Perform Logic Computing

<div><p>The concept of microbial consortia is of great attractiveness in synthetic biology. Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules. To manage such problem, here we propose a formalized design process: (i) determine the basic logic units (AND, OR and NOT gates) based on mathematical and biological considerations; (ii) establish rules to search and distribute simplest logic design; (iii) assemble assigned basic logic units in each logic operating cell; and (iv) fine-tune the circuiting interface between logic operators. We <i>in silico</i> analyzed gene circuits with inputs ranging from two to four, comparing our method with the pre-existing ones. Results showed that this formalized design process is more feasible concerning numbers of cells required. Furthermore, as a proof of principle, an <i>Escherichia coli</i> consortium that performs XOR function, a typical complex computing operation, was designed. The construction and characterization of logic operators is independent of “wiring” and provides predictive information for fine-tuning. This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.</p> </div

Additional file 6: Table S1. of Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library

Conditions in chemical genomics screen from [29] that exhibit negative correlation between mean cell width and S-score with p-value less than 0.000154 (Bonferroni multiple-hypothesis correction to p < 0.05 across 324 conditions; see Methods). Table S2. Conditions in chemical genomics screen from [29] that exhibit positive correlation between mean cell width and S-score with p-value less than 0.000154 (Bonferroni multiple-hypothesis correction to p < 0.05 across 324 conditions; see Methods). Table S3. Pairs of COGs and conditions in chemical genomics screen from [29] that exhibit correlations between mean cell width and S-scores with p-value less than 0.000154 (Bonferroni multiple-hypothesis correction to p < 0.05 across 324 conditions; see Methods). *: description from [29] and generously provided by Athanasios Typas. Table S4. Pairs of COGs and conditions in chemical genomics screen from [29] that exhibit correlations between mean cell length and S-scores with p-value less than 0.000154 (Bonferroni multiple-hypothesis correction to p < 0.05 across 324 conditions; see Methods). *: description from [29] and generously provided by Athanasios Typas. (DOCX 101 kb

The simplest logic of XOR function, its distribution into separate logic operating cells, and characterization of two logic operators.

<p>(A). XOR function and its distribution. Left: The simplest logic of XOR gate expressed as the combination of basic logic units, according to the four rules in main text. XOR gate is distributed into two different logic-operating cells, USC and DSC. USC bears a genetic AND gate, with the output signal linked to DSC. DSC processes three inputs; two environmental inputs and an intermediate signal from USC. NOT gate does not belong to either cell, but is realized by transcription-inhibitory “chemical wire”. Such construction satisfies truth table of XOR gate presented in the right panel. (B). Gene circuit to characterize transfer function of USC. Only when both inputs exist, functional T7 polymerase would activate T7 promoter and produce output, GFP. (C). Left: Experimental results for transfer function of USC. Florescence was measured and normalized by cell density. The measured sets are for 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷ and 10⁻⁸ M arabinose, and 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹ and 10⁻¹⁰ M salicylate. Right: Corresponding simulation prediction. (D). Gene circuit of DSC. Both environmental inputs can drive the expression <i>supD</i> tRNA through corresponding promoters, composing an OR gate. With no AHL, <i>T7ptag</i> would be expressed, and thereby GFP could be produced when either arabinose or salicylate (or both of them) present. (E). Transfer function of DSC, showing combinations of every two inputs. Columns from left to right: arabinose and salicylate, arabinose and AHL, and salicylate and AHL. Upper panels show experimental data compared with corresponding simulation prediction (lower panel). The data are for 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, and 10⁻⁷ M arabinose, 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, and 10⁻⁹ M salicylate, and 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹, 10⁻¹⁰, and 10⁻¹¹ M AHL. AHL was artificially supplied to DSC rather than a signal from USC.</p

Work flow for the formalized design process and <i>in silico</i> analysis of different approaches in multicellular logic circuits.

<p>(A). Schematic view of the work flow for formalized design process. (B). Number of permissible 2-input 1-output Boolean functions versus the number of cells required for their implementation. Each bar represents number of functions that can be implemented within a certain number of cells. Different colors denote different approaches: orange for Standard NOR/NAND, blue for Modular Cells, and gray for our approach of combinational design. (C). Number of permissible 2-input 1-output Boolean functions versus the number of chemical wires required for their implementation. (D) and (E) show the results for 3-input 1-output Boolean functions.</p

XOR computation operates robustly.

<p>(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, <i>P</i><0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using <i>χ²</i> test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.</p

Fine-tuning of circuiting interface between USC and DSC.

<p>(A). Schematics of XOR-function gene circuit encoded within the entire microbial consortium. LuxI, a synthase of AHL, works as output of USC. AHL transduces a repressive signal to DSC. (B). Upper panels: experimental results using diluted filtrate from induced USC. Four histograms represent results for 4 different RBS sequences: AAAGAGGAGAAA (BBa_B0034), ATTAAAGTTGAGAAA (Mutant 1), GCTCCATCCCCG (Mutant 2), and GCTCCTCCGATC (Mutant 3), with RBS strength 9-, 108- and 150-fold attenuated, respectively, predicted by RBS Calculator. In each histogram, corresponding inputs are: (left to right) no inducers (blank), arabinose only (Ara), salicylate only (Sal), and both inducers (Ara+Sal). Error bars are calculated as mean ± s. d. Lower panels: phase diagrams of the entire circuit predicted by model using characterization data for individual logic operating cells.</p