Black box modeling approaches to judge the influence of a yeast extract on microbial growth and production

2020 ◽  
Vol 92 (9) ◽  
pp. 1338-1338
Author(s):  
S. Kaul ◽  
N. Esfandiari ◽  
M. Scheunemann ◽  
G. Cornelissen
Keyword(s):  
Yeast Extract ◽  
Microbial Growth ◽  
Black Box ◽  
Modeling Approaches

scholarly journals Thermodynamic Limits and Optimality of Microbial Growth

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 277
Author(s):  
Nima Saadat ◽  
Tim Nies ◽  
Yvan Rousset ◽  
Oliver Ebenhöh
Keyword(s):  
Microbial Growth ◽  
Black Box ◽  
Cellular Growth ◽  
Modeling Approaches ◽  
Theoretical Approaches ◽  
Growing Cell ◽  
Limiting Nutrient ◽  
Growth Laws ◽  
Thermodynamic Limits ◽  
Optimality Principles

Understanding microbial growth with the use of mathematical models has a long history that dates back to the pioneering work of Jacques Monod in the 1940s. Monod’s famous growth law expressed microbial growth rate as a simple function of the limiting nutrient concentration. However, to explain growth laws from underlying principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified the principal limitations of the growth process. Although these approaches ignored metabolic details and instead considered microbial metabolism as a black box, modern theories heavily rely on genomic resources to describe and model metabolism in great detail to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modeling approaches only in a rather superficial fashion, and it appears that recent modeling approaches and classical theories are rather disconnected fields. To stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamics and outline the resulting thermodynamic limits and optimality principles. We start with classical black box models of cellular growth, and continue with recent metabolic modeling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer key black box parameters, such as the energy of formation or the degree of reduction of biomass. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

scholarly journals The view of microbes as energy converters illustrates the trade-off between growth rate and yield

2021 ◽  
Author(s):  
St. Elmo Wilken ◽  
Victor Vera Frazão ◽  
Nima P. Saadat ◽  
Oliver Ebenhöh
Keyword(s):  
Growth Rate ◽  
Microbial Growth ◽  
High Throughput Sequencing ◽  
Measurement Techniques ◽  
Black Box ◽  
Trade Off ◽  
Box Models ◽  
Self Replication ◽  
Black Box Models ◽  
Energy Converters

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black-box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models (GEMs) provide us with unprecedented detail about metabolic processes inside the cell. However, mostly due to computational complexity issues, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black-box models and modern approaches that integrate thermodynamics into GEMs. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

scholarly journals The view of microbes as energy converters illustrates the trade-off between growth rate and yield

2021 ◽  
Author(s):  
St. Elmo Wilken ◽  
Victor Vera Frazão ◽  
Nima P. Saadat ◽  
Oliver Ebenhoeh
Keyword(s):  
Growth Rate ◽  
Microbial Growth ◽  
Black Box ◽  
Trade Off ◽  
Box Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
Self Replication ◽  
Black Box Models ◽  
Energy Converters

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy, but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models provide us with unprecedented detail about metabolic processes inside the cell. However, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black box models and modern approaches that integrate thermodynamics into genome-scale metabolic models. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks

2000 ◽  
Vol 27 (4) ◽  
pp. 671-682 ◽  
Author(s):  
N Lauzon ◽  
J Rousselle ◽  
S Birikundavyi ◽  
H T Trung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diffusion Process ◽  
Diffusion Processes ◽  
Black Box ◽  
Linear Type ◽  
Modeling Approaches ◽  
Box Models ◽  
Almost All ◽  
Black Box Models

The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.

scholarly journals Which Microbial Growth Model Best Fits to Fusarium graminearum?

2018 ◽  
Author(s):  
Edgar Cambaza
Keyword(s):  
Linear Model ◽  
Fusarium Graminearum ◽  
Growth Model ◽  
Yeast Extract ◽  
Microbial Growth ◽  
Growth Models ◽  
Head Blight ◽  
Actual Growth ◽  
Imagej Software ◽  
Yeast Extract Agar

Fusarium graminearum causes head blight in wheat and corn, and produces chemicals harmful for humans and other animals. It is important to understand how it grows in order to prevent outbreaks. There are 3 well-known growth models for microorganisms and they seem applicable to molds: linear, Gompertz and Baranyi. This study aimed to see which could better represent F. graminearum growth. Three replicates were grown in yeast extract agar (YEA) for 20 days. The Feret’s radius was measured in ImageJ software, and then related to the models. Linear model was the most closely correlated to the actual growth. Thus, considering that it was the most representative of the reality and it is easier to use, it seems to be the best logical choice for F. graminearum growth studies.

scholarly journals Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ophélie Lo-Thong ◽  
Philippe Charton ◽  
Xavier F. Cadet ◽  
Brigitte Grondin-Perez ◽  
Emma Saavedra ◽  
...  
Keyword(s):  
Metabolic Pathway ◽  
Black Box ◽  
Modeling Approaches
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