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 Are Microbes Thermodynamically Optimised Self-Reproducing Machines?

2019 ◽  
Author(s):  
Nima P. Saadat ◽  
Tim Nies ◽  
Yvan Rousset ◽  
Oliver Ebenhöh
Keyword(s):  
Microbial Growth ◽  
Black Box ◽  
Cellular Growth ◽  
Nutrient Concentrations ◽  
Theoretical Approaches ◽  
Growing Cell ◽  
Scale Modelling ◽  
Growth Laws ◽  
Genome Scale ◽  
Modelling Approaches

To understand microbial growth with mathematical models has a long tradition that dates back to the pioneering work of Jacques Monod in the 1940s. Growth laws are simple mathematical expressions that aim at describing growth rates of microbes as functions of external parameters, in particular nutrient concentrations. These laws are now widely applied to construct, e.g., dynamic ecosystem models. However, to explain the growth laws from underlying (first) 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 principle limitations of the growth process. Whereas these approaches considered a growing microbe as a black box, modern theories heavily rely on genomic resources to describe and model genome-scale networks to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modelling approaches only in a rather superficial fashion, and it appears that recent modelling approaches and classical theories are disconnected fields. In order to stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamic principles. We start with classical black-box models of cellular growth, and continue with genome-scale modelling 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 classical black-box parameters, which are experimentally accessible in growth experiments. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

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

Ceramic studies: examining the full spectrum

Antiquity ◽  
2018 ◽  
Vol 92 (362) ◽  
pp. 531-533 ◽  
Author(s):  
Rob A. Ixer
Keyword(s):  
Data Analysis ◽  
Analytical Techniques ◽  
Black Box ◽  
Full Spectrum ◽  
Theoretical Approaches ◽  
Ceramic Studies

These three books range from the clinical (Hunt) to the folksy (Woodward and Hill), and might be seen as a progression. One travelling from the Hunt-edited encyclopaedia with its emphasis on new and exotic scientific analytical techniques, rigorous theoretical approaches and data analysis, through the Integrative approaches book using techniques and ideas that have proved effective for decades (this book is firmly within the mainstream of recent excellent pot books that have a very strong US contribution, as exemplified by Quinn 2009), to the English, and almost quaint, re-issue of Woodward and Hill outlining post-processualist concerns and quite devoid of any black box ‘gee-whiz’. Their combined 1200 pages, heavily featuring petrography, often alongside geochemistry, show that these sorts of ceramic studies, although often regarded as comatose-inducing, are in favour again.

Copper (II) Accumulation And Superoxide Dismutase Activity During Growth Of Aspergillus Niger B-77

2002 ◽  
Vol 57 (3-4) ◽  
pp. 319-322 ◽  
Author(s):  
Kolishka Tsekova ◽  
Dessislava Todorova
Keyword(s):  
Heavy Metal ◽  
Cell Culture ◽  
Superoxide Dismutase ◽  
Aspergillus Niger ◽  
Heavy Metal Ions ◽  
Microbial Growth ◽  
Biological Activities ◽  
Sod Activity ◽  
Growing Cell ◽  
A Cell

The influence of copper (II) ions on the growth, accumulation properties and superoxide dismutase (SOD) activity of a growing culture of Aspergillus niger B-77 were studied. Microbial growth, the level of copper (II) accumulation and SOD activity depended on the initial copper (II) concentration. Aspergillus niger is able to accumulate large amounts of copper (II) from the nutrient medium with 200 mg.l-1 copper (II) ions without loosing its biological activities. Addition of copper (II) ions increased the SOD activity in the growing cell cultures. The changes in enzyme activity induced by heavy metal ions might be used as an indicator of intracellular oxy-intermediate generation in a cell culture growing under stress conditions

scholarly journals The role of topology and mechanics in uniaxially growing cell networks

2020 ◽  
Vol 476 (2233) ◽  
pp. 20190523
Author(s):  
Alexander Erlich ◽  
Gareth W. Jones ◽  
Françoise Tisseur ◽  
Derek E. Moulton ◽  
Alain Goriely
Keyword(s):  
Network Connectivity ◽  
Tissue Level ◽  
Tissue Response ◽  
Cell Network ◽  
Growing Cell ◽  
A Cell ◽  
Growth Laws ◽  
Parallel Connections ◽  
Cell Networks

In biological systems, the growth of cells, tissues and organs is influenced by mechanical cues. Locally, cell growth leads to a mechanically heterogeneous environment as cells pull and push their neighbours in a cell network. Despite this local heterogeneity, at the tissue level, the cell network is remarkably robust, as it is not easily perturbed by changes in the mechanical environment or the network connectivity. Through a network model, we relate global tissue structure (i.e. the cell network topology) and local growth mechanisms (growth laws) to the overall tissue response. Within this framework, we investigate the two main mechanical growth laws that have been proposed: stress-driven or strain-driven growth. We show that in order to create a robust and stable tissue environment, networks with predominantly series connections are naturally driven by stress-driven growth, whereas networks with predominantly parallel connections are associated with strain-driven growth.

scholarly journals Competition delays multi-drug resistance evolution during combination therapy

2020 ◽  
Author(s):  
Ernesto Berríos-Caro ◽  
Danna R. Gifford ◽  
Tobias Galla
Keyword(s):  
Drug Resistance ◽  
Combination Therapy ◽  
Exponential Growth ◽  
Logistic Growth ◽  
Theoretical Approaches ◽  
Strain Competition ◽  
Growth Laws ◽  
Resistant Strains ◽  
Multiple Drugs ◽  
Emergence Of Resistance

ABSTRACTCombination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

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.

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