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

Research Priorities for Economic Analyses of Prevention: Current Issues and Future Directions

In response to growing interest in economic analyses of prevention efforts, a diverse group of prevention researchers, economists, and policy analysts convened a scientific panel, on “Research Priorities in Economic Analysis of Prevention” at the 19(th) annual conference of the Society for Prevention Research. The panel articulated four priorities that, if followed in future research, would make economic analyses of prevention efforts easier to compare and more relevant to policymakers, and community stakeholders. These priorities are: (1) increased standardization of evaluation methods, (2) improved economic valuation of common prevention outcomes, (3) expanded efforts to maximize evaluation generalizability and impact, as well as (4) enhanced transparency and communicability of economic evaluations. In this paper we define three types of economic analyses in prevention, provide context and rationale for these four priorities as well as related sub-priorities, and discuss the challenges inherent in meeting them