Special Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes

Note: In lieu of an abstract, this is an excerpt from the first page. Chemical Processes A major focus of Processes is chemical process engineering with applications to both traditional industries and emerging industries for renewable chemicals and energy production. The Special Issue contains four papers in this area. The first paper describes the development and simulation of a model prediction controller for achieving the desired viscosity curve in the continuous production of a non-Newtonian fluid [1]. The second paper addresses the problem of minimizing fuel consumption in fuel gas networks through a superstructure-based method and mixed-integer nonlinear programming [2]. The interesting possibility of extending the favorable characteristics of cuboid packed-bed devices originally designed for chromatographic separations to packed-bed chemical reactors is explored in the third paper [3]. The fourth paper addresses the optimal design of ammonia production plants based on adsorption for unreacted nitrogen and hydrogen recovery and wind energy for electricity generation [4]

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability

A Multiscale Model to Investigate Circadian Rhythmicity of Pacemaker Neurons in the Suprachiasmatic Nucleus

The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels

Approaching the adiabatic timescale with machine-learning

The control and manipulation of quantum systems without excitation is challenging, due to the complexities in fully modeling such systems accurately and the difficulties in controlling these inherently fragile systems experimentally. For example, while protocols to decompress Bose-Einstein condensates (BEC) faster than the adiabatic timescale (without excitation or loss) have been well developed theoretically, experimental implementations of these protocols have yet to reach speeds faster than the adiabatic timescale. In this work, we experimentally demonstrate an alternative approach based on a machine learning algorithm which makes progress towards this goal. The algorithm is given control of the coupled decompression and transport of a metastable helium condensate, with its performance determined after each experimental iteration by measuring the excitations of the resultant BEC. After each iteration the algorithm adjusts its internal model of the system to create an improved control output for the next iteration. Given sufficient control over the decompression, the algorithm converges to a novel solution that sets the current speed record in relation to the adiabatic timescale, beating out other experimental realizations based on theoretical approaches. This method presents a feasible approach for implementing fast state preparations or transformations in other quantum systems, without requiring a solution to a theoretical model of the system. Implications for fundamental physics and cooling are discussed.Comment: 7 pages main text, 2 pages supporting informatio

Metabolic Modeling of Clostridium difficile Associated Dysbiosis of the Gut Microbiota

Recent in vitro experiments have demonstrated the ability of the pathogen Clostridium difficile and commensal gut bacteria to form biofilms on surfaces, and biofilm development in vivo is likely. Various studies have reported that 3%–15% of healthy adults are asymptomatically colonized with C. difficile, with commensal species providing resistance against C. difficile pathogenic colonization. C. difficile infection (CDI) is observed at a higher rate in immunocompromised patients previously treated with broad spectrum antibiotics that disrupt the commensal microbiota and reduce competition for available nutrients, resulting in imbalance among commensal species and dysbiosis conducive to C. difficile propagation. To investigate the metabolic interactions of C. difficile with commensal species from the three dominant phyla in the human gut, we developed a multispecies biofilm model by combining genome-scale metabolic reconstructions of C. difficile, Bacteroides thetaiotaomicron from the phylum Bacteroidetes, Faecalibacterium prausnitzii from the phylum Firmicutes, and Escherichia coli from the phylum Proteobacteria. The biofilm model was used to identify gut nutrient conditions that resulted in C. difficile-associated dysbiosis characterized by large increases in C. difficile and E. coli abundances and large decreases in F. prausnitzii abundance. We tuned the model to produce species abundances and short-chain fatty acid levels consistent with available data for healthy individuals. The model predicted that experimentally-observed host-microbiota perturbations resulting in decreased carbohydrate/increased amino acid levels and/or increased primary bile acid levels would induce large increases in C. difficile abundance and decreases in F. prausnitzii abundance. By adding the experimentally-observed perturbation of increased host nitrate secretion, the model also was able to predict increased E. coli abundance associated with C. difficile dysbiosis. In addition to rationalizing known connections between nutrient levels and disease progression, the model generated hypotheses for future testing and has the capability to support the development of new treatment strategies for C. difficile gut infections

Metabolic modeling of synthesis gas fermentation in bubble column reactors

Background A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors. Results We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. Conclusions Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors

Announcing the 2019 Processes Travel Awards for Post-Doctoral Fellows and Ph.D. Students

Note: In lieu of an abstract, this is an excerpt from the first page. In order to support the development of early career researchers involved in chemical and biological process/systems engineering, Processes launched the second Travel Awards for Post-doctoral Fellows and Ph.D. Students. We received a large number of highly meritorious applications from all over the world. On behalf of the Editors of Processes, we are pleased to announce the winners of Processes Travel Awards for 2019

Spatiotemporal modeling of microbial metabolism

Background Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention. Results We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution. Conclusions Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems

\u3cem\u3ePseudomonas aeruginosa\u3c/em\u3e reverse diauxie is a multidimensionl, optimized, resource utilization strategy

Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an ‘overflow’ metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as ‘reverse diauxie’. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence including its potentially, mutualistic interactions with microorganisms found commonly in the environment and in medical infections