Stochastic Sensitivity Analysis and Kernel Inference via Distributional Data

AbstractCellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing

Origin of bistability underlying mammalian cell cycle entry

Mammalian cell cycle entry is controlled at the restriction point by a bistable and resettable switch, which is shown to emerge from a minimal gene circuit containing a mutual-inhibition feedback loop between Rb and E2F modules, coupled with a feed-forward loop between Myc and E2F modules

A synthetic Escherichia coli predator–prey ecosystem

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner

Programmed Allee Effect in Bacteria Causes a Tradeoff Between Population Spread and Survival

Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms

Gambaran Pelaksanaan Problem-Based Learning Pada Mahasiswa Program Studi Pendidikan Dokter Fakultas Kedokteran Dan Ilmu Kesehatan Universitas Jambi

Background: Problem-Based Learning (PBL) is a new learning strategy that is focused on students, where they learn based on problems. Faculty of Medicine and Health Sciences UNJA (FKIK UNJA) have implemented PBL as a learning strategy in the Competence based Curriculum since 2007, however, there are no studies that measure the implementation of PBL based on its four theories in FKIK UNJA. Methods: This descriptive cross-sectional study design was conducted in April-May 2014 in FKIK UNJA. The number of respondents are 184 students from the class of 2010, 2011 and 2012. This research employed a questionnaire developed by Romauli et al. Then the average analysis is utilized to obtain the level of implementation of PBL based on the four theories. Results: The implementation level of PBL in FKIK UNJA that based on learning constructive, independent, collaborative and contextual was moderate (1,94). The implementation level of constructive learning process based on class of 2010, 2012 was high (2,02 and 2,13) and the class of 2011 was moderate (1,98). The implementation level of self-learning process based on the class of 2010, 2011 and 2012 was moderate (1,89; 1,87; 1,96). The implementation level of collaborative learning based on the class of 2010, 2011 was high (2,16 and 2,09) and the class of 2011 was moderate (1,97). The implementation level of contextual learning based on the class of 2010, 2011, and 2012 was moderate (1,78; 1,80; 1,82). Conclusions: The implementation of PBL on students of Medical Education FKIK UNJA in each class and all students, have stimulated students to develop their knowledge, stimulate control of the learning process in the student itself, stimulate the interaction between students and stimulate the learning process which reflects the situation and environment, where the knowledge will be used

Long-Term Monitoring of Bacteria Undergoing Programmed Population Control in a Microchemostat

Using an active approach to preventing biofilm formation, we implemented a microfluidic bioreactor that enables long-term culture and monitoring of extremely small populations of bacteria with single-cell resolution. We used this device to observe the dynamics of Escherichia coli carrying a synthetic “population control” circuit that regulates cell density through a feedback mechanism based on quorum sensing. The microfluidic bioreactor enabled long-term monitoring of unnatural behavior programmed by the synthetic circuit, which included sustained oscillations in cell density and associated morphological changes, over hundreds of hours

Phenotypic Signatures Arising from Unbalanced Bacterial Growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains

Noise Reduction by Diffusional Dissipation in a Minimal Quorum Sensing Motif

Cellular interactions are subject to random fluctuations (noise) in quantities of interacting molecules. Noise presents a major challenge for the robust function of natural and engineered cellular networks. Past studies have analyzed how noise is regulated at the intracellular level. Cell–cell communication, however, may provide a complementary strategy to achieve robust gene expression by enabling the coupling of a cell with its environment and other cells. To gain insight into this issue, we have examined noise regulation by quorum sensing (QS), a mechanism by which many bacteria communicate through production and sensing of small diffusible signals. Using a stochastic model, we analyze a minimal QS motif in Gram-negative bacteria. Our analysis shows that diffusion of the QS signal, together with fast turnover of its transcriptional regulator, attenuates low-frequency components of extrinsic noise. We term this unique mechanism “diffusional dissipation” to emphasize the importance of fast signal turnover (or dissipation) by diffusion. We further show that this noise attenuation is a property of a more generic regulatory motif, of which QS is an implementation. Our results suggest that, in a QS system, an unstable transcriptional regulator may be favored for regulating expression of costly proteins that generate public goods

Oscillations by Minimal Bacterial Suicide Circuits Reveal Hidden Facets of Host-Circuit Physiology

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the PluxI promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of “hidden interactions” on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context