Computational models of populations of bacteria and lytic phage.

The use of phages to control and reduce numbers of unwanted bacteria can be traced back to the early 1900s, when phages were explored as a tool to treat infections before the wide scale use of antibiotics. Recently, phage therapy has received renewed interest as a method to treat multiresistant bacteria. Phages are also widely used in the food industry to prevent the growth of certain bacteria in foods, and are currently being explored as a tool for use in bioremediation and wastewater treatment. Despite the large body of biological research on phages, relatively little attention has been given to computational modeling of the population dynamics of phage and bacterial interactions. The earliest model was described by Campbell in the 1960s. Subsequent modifications to this model include partial or complete resistance, multiple phage binding sites, and spatial heterogeneity. This review provides a general introduction to modeling of the population dynamics of bacteria and phage. The review introduces the basic model and relevant concepts and evaluates more complex variations of the basic model published to date, including a model of disease epidemics caused by infectious bacteria. Finally, the shortcomings and potential ways to improve the models are discussed.

Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks.

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types.

High-Efficiency Biocatalytic Conversion of Thebaine to Codeine.

An enzymatic biosynthesis approach is described for codeine, the most widely used medicinal opiate, providing a more environmentally sustainable alternative to current chemical conversion, with yields and productivity compatible with industrial production. Escherichia coli strains were engineered to express key enzymes from poppy, including the recently discovered neopinone isomerase, producing codeine from thebaine. We show that compartmentalization of these enzymes in different cells is an effective strategy that allows active spatial and temporal control of reactions, increasing yield and volumetric productivity and reducing byproduct generation. Codeine is produced at a yield of 64% and a volumetric productivity of 0.19 g/(L·h), providing the basis for an industrially applicable aqueous whole-cell biotransformation process. This approach could be used to redirect thebaine-rich feedstocks arising from the U.S. reduction of opioid manufacturing quotas or applied to enable total biosynthesis and may have broader applicability to other medicinal plant compounds.

Computational Modeling of Bacteriophage Production for Process Optimization.

Computational models can be used to optimize the production of bacteriophages. Here a model is described for production in a two-stage self-cycling process. Theoretical and practical considerations for modeling bacteriophage production are first introduced. The key experimental protocols required to estimate key kinetic parameters for the model, including determining variable infection rates as a function of substrate concentration, are described. ppSim is an open-source R-script that can simulate bacteriophage production to optimize productivity or minimize costs. The steps included to run the simulation using the experimentally determined infection parameters are described. An example is also presented, where a level sensor and cycle time are optimized to maximize bacteriophage productivity in two sequential 1-L bioreactors, resulting in a production rate of 4.46 × 1010 bacteriophage particles/hour. The protocols and programs described here will allow users to potentially optimize production of their own bacteriophage-bacteria pairing by effectively applying bacteriophage modeling.

Computational Modelling of Large Scale Phage Production Using a Two-Stage Batch Process.

Cost effective and scalable methods for phage production are required to meet an increasing demand for phage, as an alternative to antibiotics. Computational models can assist the optimization of such production processes. A model is developed here that can simulate the dynamics of phage population growth and production in a two-stage, self-cycling process. The model incorporates variable infection parameters as a function of bacterial growth rate and employs ordinary differential equations, allowing application to a setup with multiple reactors. The model provides simple cost estimates as a function of key operational parameters including substrate concentration, feed volume and cycling times. For the phage and bacteria pairing examined, costs and productivity varied by three orders of magnitude, with the lowest cost found to be most sensitive to the influent substrate concentration and low level setting in the first vessel. An example case study of phage production is also presented, showing how parameter values affect the production costs and estimating production times. The approach presented is flexible and can be used to optimize phage production at laboratory or factory scale by minimizing costs or maximizing productivity.

The influence of protruding filamentous bacteria on floc stability and solid-liquid separation in the activated sludge process.

Filamentous bacteria can impact on the physical properties of flocs in the activated sludge process assisting solid-liquid separation or inducing problems when bacteria are overabundant. While filamentous bacteria within the flocs are understood to increase floc tensile strength, the relationship between protruding external filaments, dewatering characteristics and floc stability is unclear. Here, a quantitative methodology was applied to determine the abundance of filamentous bacteria in activated sludge samples from four wastewater treatment plants. An automated image analysis procedure was applied to identify filaments and flocs and calculate the length of the protruding filamentous bacteria (PFB) relative to the floc size. The correlation between PFB and floc behavior was then assessed. Increased filament abundance was found to increase interphase drag on the settling flocs, as quantified by the hindered settling function. Additionally, increased filament abundance was correlated with a lower gel point concentration leading to poorer sludge compactability. The floc strength factor, defined as the relative change in floc size upon shearing, correlated positively with filament abundance. This influence of external protruding filamentous bacteria on floc stability is consistent with the filamentous backbone theory, where filamentous bacteria within flocs increase floc resistance to shear-induced breakup. A qualitative correlation was also observed between protruding and internal filamentous structure. This study confirms that filamentous bacteria are necessary to enhance floc stability but if excessively abundant will adversely affect solid-liquid separation. The tools developed here will allow quantitative analysis of filament abundance, which is an improvement on current qualitative methods and the improved method could be used to assist and optimize the operation of waste water treatment plants.