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 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 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 COBRAme: A computational framework for genome-scale models of metabolism and gene expression

2018 ◽  
Vol 14 (7) ◽  
pp. e1006302 ◽  
Author(s):  
Colton J. Lloyd ◽  
Ali Ebrahim ◽  
Laurence Yang ◽  
Zachary A. King ◽  
Edward Catoiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Computational Framework ◽  
Scale Models ◽  
Genome Scale

scholarly journals ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models

2019 ◽  
Author(s):  
Nikolay Martyushenko ◽  
Eivind Almaas
Keyword(s):  
Large Scale ◽  
Work Flow ◽  
Supplementary Information ◽  
Scale Models ◽  
Model Size ◽  
Metabolic Models ◽  
Model Publication ◽  
Genome Scale ◽  
Model Visualization ◽  
Community Standard

Abstract Motivation The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model’s inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated model-validation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. Results We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is ∼2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. Availability and implementation Windows and Linux executables and source code are available under the EPL 2.0 Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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.

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