Biology subjects

Swain, P. S.

Publications and source records attributed to Swain, P. S..

5 recordsLinked to original sources

bioRxiv2018

Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling

Synthetic biology is an emerging engineering discipline that aims at synthesising logical circuits into cells to accomplish new functions. Despite a thriving community and some notable successes, the basic task of assembling predictable gene circuits is still a key challenge. Mathematical models are uniquely suited to help solve this issue. Yet in biology they are perceived as expensive and laborious to obtain because low-information experiments have often been used to infer model parameters. How much additional information can be gained using optimally designed experiments? To tackle this question we consider a building block in Synthetic Biology, an inducible promoter in yeast S. cerevisiae. Using in vivo data we re-fit a mathematical model for such a system; we then compare in silico the quality of the parameter estimates when model calibration is done using typical (e.g. step inputs) and optimally designed experiments. We find that Optimal Experimental Design leads to ~70% improvement in the predictive ability of the inferred models. We conclude providing suggestions on how optimally designed experiments can be implemented in vivo.

bioengineering↗

bioRxiv2018

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

The impact of fluorescence microscopy has been limited by the difficulties of express-ing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we develop a general algorithm to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. To untangle measurement noise from stochastic fluctuations, we use the linear noise approximation and Kalman filtering within a framework of Bayesian inference. Not only do our results agree with biochemical measurements for multiple proteins in budding yeast, but we also provide a statistically verified model of measurement noise for fluorescence microscopes. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.

systems biology↗

bioRxiv2017

Distributed and dynamic intracellular organization of extracellular information

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for ten transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: generalists (Msn2/4, Tod6/Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. Each transcription factor reports differently, and it is only their collective response that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change, and we predict that such multi-dimensional representations are common in cellular decision-making.\n\nSignificance StatementTo thrive in diverse environments, cells must represent extracellular change intracellularly despite stochastic biochemistry. Here we introduce a quantitative framework for investigating the organization of information within a cell. Combining single-cell measurements of intracellular dynamics with a new, scalable methodology for estimating mutual information between time-series and a discrete input, we demonstrate that extracellular information is encoded in the dynamics of the nuclear localization of transcription factors and that information is lost with alternative static statistics. Any one transcription factor is usually insufficient, but the collective dynamics of multiple transcription factors can represent complex extracellular change. We therefore show that a cells internal representation of its environment can be both distributed across diverse proteins and dynamically encoded.

systems biology↗

bioRxiv2017

Predicting metabolic adaptation from networks of mutational paths

Competition for substrates is a ubiquitous selection pressure faced by microbes, yet intracellular trade-offs can prevent cells from metabolizing every type of available substrate. Adaptive evolution is constrained by these trade-offs, but their consequences for the repeatability and predictability of evolution are unclear. Here we develop an eco-evolutionary model with a metabolic trade-off to generate networks of mutational paths in microbial communities and show that these networks have descriptive and predictive information about the evolving communities. We find that long-term outcomes, including community collapse, diversity, and cycling, have characteristic evolutionary dynamics that determine the entropy, or repeatability, of mutational paths. Although reliable prediction of evolutionary outcomes from environmental conditions is difficult, graph-theoretic properties of the mutational networks enable accurate prediction even from incomplete observations. In conclusion, we present a novel methodology for analyzing adaptive evolution and report that the dynamics of adaptation are a key variable for predictive success.

evolutionary biology↗

bioRxiv2017

Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast

MotivationAlthough high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most segmentation techniques perform poorly. Incorporating information about the microfluidic features, media flow and morphology of the cells can substantially improve performance, though it may constrain the generalizability of software.\n\nResultsHere we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion, to allow tracking and segmentation of cells in micrflouidic devices. Using manually curated data sets, we demonstrate substantial improvements in both tracking and segmentation for this approach when compared with existing software.\n\nAvailabilityThe MATLAB(R) code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software. The test images and the curated ground truth results used for comparing the algorithms are available at http://swainlab.bio.ed.ac.uk/.

bioinformatics↗