Endothermic microbial growth. A calorimetric investigation of an extreme case of entropy-driven microbial growth

Life is almost always associated with the generation of heat. Thus far, all chemotrophic life forms that have been studied in calorimeters were found to be exothermic. Certain literature reports have even cast doubt on the existence of endothermic growth, even though thermodynamic principles do not rule it out. The present report describes the first experiments demonstrating the actual existence of chemotrophic life forms that take up heat rather than produce i

A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound

In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a Branch & Bound (B&B) algorithm to find the globally optimal solution to the heterogeneous fleet composition problem while considering vehicle routing. Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW). The metaheuristic and exact algorithms are implemented in a parallel hybrid optimization algorithm where the metaheuristic rapidly finds feasible solutions that provide candidate upper bounds for the B&B algorithm which runs simultaneously. The MCTS additionally provides a candidate fleet composition to initiate the B&B search. Experiments show that the proposed approach results in significant improvements in computation time and convergence to the optimal solution.Comment: Submitted to the IEEE Intelligent Vehicles Symposium 202

Optimal Control of Fed-Batch Fermenters

Optimal control of fed-batch fermenters S. Valentinotti† C. Cannizzaro‡ M.Rhiel‡ U. Holmberg† U. von Stockar‡ D. Bonvin† †Institut d’Automatique, EPFL, 1015 Lausanne, Switzerland ‡Institut de Genie Chimique, EPFL, 1015 Lausanne, Switzerland Fermentors are often run in a fed-batch manner to avoid the formation of overflow metabolites. At a high growth rate, the most efficient metabolic pathway(s) of certain microorganisms become saturated resulting in overflow metabolite production. These byproducts are undesirable since their accumulation in the reactor may be inhibitory and the productivity of biomass and growth-associated products is reduced. The ideal way to run such fed-batch fermentation is to grow the cells in the reactor at the critical growth rate, i.e., the point at which overflow metabolite production begins. However, since this value changes from run to run, or even during a given fermentation, its identification is not trivial. A simple way to overcome this difficulty is to maintain a very small, but constant overflow metabolite concentration in the reactor, ensuring that most of the substrate is consumed efficiently. However due to exponential cell growth, standard controllers can maintain a constant concentration only for a limited time period. In this work an adaptive control strategy to maintain a constant overflow metabolite concentration in fed-batch fermentation is presented. The proposed approach requires the knowledge of only two system parameters: the yield coefficient, expressing the relation between overflow metabolite and substrate, and the instantaneous concentration of the overflow metabolite. Baker’s yeast fed-batch experiments were performed with the ob jective of maximizing biomass productivity and minimizing ethanol production. Mid-infrared spectroscopy was used to measure the ethanol concentration that was provided on-line to the controller. The results from numerous experiments have demonstrated the effectiveness of the proposed control strategy. The specific growth rate was maintained constant, at a value close to the critical point, until oxygen transfer limitation occurred. Then, the controller automatically reduced the feed rate to prevent excess ethanol production. The biomass increased from 0.5 to 65 grams per liter during the exponential growth phase. Simulation results based on this control strategy show its applicability to other overflow metabolite organisms, such as Escherichia coli

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomass-specific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate

Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter

Microbial carbon use efficiency (CUE) is a critical regulator of soil organic matter dynamics and terrestrial carbon fluxes, with strong implications for soil biogeochemistry models. While ecologists increasingly appreciate the importance of CUE, its core concepts remain ambiguous: terminology is inconsistent and confusing, methods capture variable temporal and spatial scales, and the significance of many fundamental drivers remains inconclusive. Here we outline the processes underlying microbial efficiency and propose a conceptual framework that structures the definition of CUE according to increasingly broad temporal and spatial drivers where (1) CUEP reflects population-scale carbon use efficiency of microbes governed by species-specific metabolic and thermodynamic constraints, (2) CUEC defines community-scale microbial efficiency as gross biomass production per unit substrate taken up over short time scales, largely excluding recycling of microbial necromass and exudates, and (3) CUEE reflects the ecosystem-scale efficiency of net microbial biomass production (growth) per unit substrate taken up as iterative breakdown and recycling of microbial products occurs. CUEE integrates all internal and extracellular constraints on CUE and hence embodies an ecosystem perspective that fully captures all drivers of microbial biomass synthesis and decay. These three definitions are distinct yet complementary, capturing the capacity for carbon storage in microbial biomass across different ecological scales. By unifying the existing concepts and terminology underlying microbial efficiency, our framework enhances data interpretation and theoretical advances