Using synthetic biological parts and microbioreactors to explore the protein expression characteristics of Escherichia coli

Synthetic biology has developed numerous parts for the precise control of protein expression. However, relatively little is known about the burden these place on a host, or their reliability under varying environmental conditions. To address this, we made use of synthetic transcriptional and translational elements to create a combinatorial library of constructs that modulated expression strength of a green fluorescent protein. Combining this library with a microbioreactor platform, we were able to perform a detailed large-scale assessment of transient expression and growth characteristics of two <i>Escherichia coli</i> strains across several temperatures. This revealed significant differences in the robustness of both strains to differing types of protein expression, and a complex response of transcriptional and translational elements to differing temperatures. This study supports the development of reliable synthetic biological systems capable of working across different hosts and environmental contexts. Plasmids developed during this work have been made publicly available to act as a reference set for future research

Advanced optical imaging in living embryos

Developmental biology investigations have evolved from static studies of embryo anatomy and into dynamic studies of the genetic and cellular mechanisms responsible for shaping the embryo anatomy. With the advancement of fluorescent protein fusions, the ability to visualize and comprehend how thousands to millions of cells interact with one another to form tissues and organs in three dimensions (xyz) over time (t) is just beginning to be realized and exploited. In this review, we explore recent advances utilizing confocal and multi-photon time-lapse microscopy to capture gene expression, cell behavior, and embryo development. From choosing the appropriate fluorophore, to labeling strategy, to experimental set-up, and data pipeline handling, this review covers the various aspects related to acquiring and analyzing multi-dimensional data sets. These innovative techniques in multi-dimensional imaging and analysis can be applied across a number of fields in time and space including protein dynamics to cell biology to morphogenesis

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell - two important parameters for systems biology model development

Using buoyant mass to measure the growth of single cells

We used a suspended microchannel resonator (SMR) combined with picoliter-scale microfluidic control to measure buoyant mass and determine the 'instantaneous' growth rates of individual cells. The SMR measures mass with femtogram precision, allowing rapid determination of the growth rate in a fraction of a complete cell cycle. We found that for individual cells of Bacillus subtilis, Escherichia coli, Saccharomyces cerevisiae and mouse lymphoblasts, heavier cells grew faster than lighter cells.National Institutes of Health (U.S.) (MIT Center for Cell Decision Processes Grant P50GM68762)United States. Army Research Office (Institute for Collaborative Biotechnologies Grant DAAD1903D0004