scholarly journals Quantifying the entropic cost of cellular growth control

2017 ◽  
Author(s):  
Daniele De Martino ◽  
Fabrizio Capuani ◽  
Andrea De Martino
Keyword(s):  
Growth Control ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Initial Size ◽  
E Coli ◽  
Regulatory Strategies ◽  
Control Growth ◽  
Metabolic States ◽  
Scale Models ◽  
Genome Scale

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale models. Results obtained forE. coliand human cells are consistent with experiments and point to different regulatory strategies by which growth can be fostered or repressed. Moreover we explicitly connect the ‘inverse temperature’ that controls MaxEnt distributions to the growth dynamics, showing that the initial size of a colony may be crucial in determining how an exponentially growing population organizes the phenotypic space.

scholarly journals COBRAme: A Computational Framework for Genome-Scale Models of Metabolism and Gene Expression

2017 ◽  
Author(s):  
Colton J. Lloyd ◽  
Ali Ebrahim ◽  
Laurence Yang ◽  
Zachary King ◽  
Edward Catoiu ◽  
...  
Keyword(s):  
Marked Improvement ◽  
Thermotoga Maritima ◽  
Cellular Growth ◽  
Software Framework ◽  
Computational Framework ◽  
E Coli ◽  
Growing Cell ◽  
Scale Models ◽  
Model Size ◽  
Genome Scale

AbstractGenome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable new and exciting insights that are fundamental to understanding the basis of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives virtually identical solutions to previous E. coli ME-models while using ¼ the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be reconstructed and edited most efficiently using the software.

scholarly journals Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

scholarly journals Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models

2010 ◽  
Vol 6 (1) ◽  
pp. 390 ◽  
Author(s):  
Nathan E Lewis ◽  
Kim K Hixson ◽  
Tom M Conrad ◽  
Joshua A Lerman ◽  
Pep Charusanti ◽  
...  
Keyword(s):  
Optimal Growth ◽  
E Coli ◽  
Scale Models ◽  
Genome Scale ◽  
Omic Data

scholarly journals Integrating Genome-Scale and Superstructure Optimization Models in Techno-Economic Studies of Biorefineries

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 286
Author(s):  
Amir Akbari ◽  
Paul I. Barton
Keyword(s):  
Net Present Value ◽  
Metabolic Model ◽  
Computational Method ◽  
Cellular Growth ◽  
Mixed Integer ◽  
Design Parameters ◽  
Nonlinear Program ◽  
A Genome ◽  
Scale Models ◽  
Genome Scale

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.

scholarly journals Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

2016 ◽  
Author(s):  
Zachary A. King ◽  
Edward J. O’Brien ◽  
Adam M. Feist ◽  
Bernhard O. Palsson
Keyword(s):  
Cancer Biology ◽  
Metabolic Networks ◽  
Predictive Power ◽  
Literature Mining ◽  
Next Generation ◽  
E Coli ◽  
Wide Range ◽  
Scale Models ◽  
A Cell ◽  
Genome Scale

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology1, cancer biology2, and biotechnology3. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. Next-generation models of metabolism and gene expression are even more capable than previous models, but parameterization poses new challenges.

scholarly journals CONSERVATION OF NUCLEIC ACIDS DURING BACTERIAL GROWTH

1954 ◽  
Vol 38 (2) ◽  
pp. 145-148 ◽  
Author(s):  
A. D. Hershey
Keyword(s):  
Characteristic Feature ◽  
Nucleic Acids ◽  
Bacterial Growth ◽  
Cellular Growth ◽  
E Coli

In experiments of 6 hours duration, no replacement of phosphorus or purine and pyrimidine carbon in DNA, nor flow of these atoms from RNA to DNA, could be detected in rapidly growing cultures of E. coli. The slow replacement that has been demonstrated for many substances in non-proliferating tissues of other organisms, though it may occur also in bacteria, is not greatly accelerated under conditions of rapid cellular growth, and therefore cannot be a characteristic feature of synthetic processes.

Effects of Diarrhea Associated with Specific Enteropathogens on the Growth of Children in Rural Bangladesh

PEDIATRICS ◽  
1984 ◽  
Vol 73 (6) ◽  
pp. 799-805 ◽  
Author(s):  
Robert E. Black ◽  
Kenneth H. Brown ◽  
Stan Becker
Keyword(s):  
Linear Growth ◽  
Protein Energy Malnutrition ◽  
Growth Control ◽  
Reference Population ◽  
Enterotoxigenic Escherichia Coli ◽  
E Coli ◽  
Rural Bangladesh ◽  
Protein Energy ◽  
Negative Effect ◽  
The Difference

Village-based surveillance data from longitudinal studies in rural Bangladesh have been used to evaluate the nutritional consequences of infectious diseases, including diarrhea due to specific pathogens. The prevalences of specific illnesses were related to the ponderal and linear growth of young children for 2-month and 1-year periods. Of the common illnesses, only diarrhea had a significant inverse relationship with increments of weight during 2-month periods and of length during 1 year. Diarrhea accounted for 20% of the difference in linear growth between the study children and the international reference population during the first 5 years of life. Diarrhea associated with enterotoxigenic Escherichia coli had a significant negative effect on the bimonthly weight gain of children in this community and shigellosis had the strongest negative effect on bimonthly and annual linear growth. Control of diarrhea due to enterotoxigenic E coli and Shigella would not only substantially diminish diarrheal morbidity but would also improve the growth of children and thereby reduce the prevalence of protein-energy malnutrition.

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