scholarly journals Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

2018 ◽  
Author(s):  
Tuure Hameri ◽  
Georgios Fengos ◽  
Meric Ataman ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Steady State ◽  
Metabolic Engineering ◽  
Kinetic Models ◽  
Large Scale ◽  
Cellular Growth ◽  
Reverse Direction ◽  
Control Analysis ◽  
Omics Data ◽  
E Coli ◽  
Steady State Solutions

AbstractLarge-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grownEscherichia coliinto a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of theE. coliaerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grownE. coli.

scholarly journals K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

2019 ◽  
Author(s):  
Saratram Gopalakrishnan ◽  
Satyakam Dash ◽  
Costas Maranas
Keyword(s):  
Steady State ◽  
Kinetic Models ◽  
Large Scale ◽  
Metabolic Networks ◽  
Decomposition Approach ◽  
E Coli ◽  
Environmental Perturbations ◽  
Genetic Perturbations ◽  
Gradient Based ◽  
Speed Up

AbstractKinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces K-FIT, an accelerated kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model forE. coli(307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.

scholarly journals Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

2016 ◽  
Vol 35 ◽  
pp. 148-159 ◽  
Author(s):  
Stefano Andreozzi ◽  
Anirikh Chakrabarti ◽  
Keng Cher Soh ◽  
Anthony Burgard ◽  
Tae Hoon Yang ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Kinetic Models ◽  
Large Scale ◽  
E Coli

scholarly journals Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

2019 ◽  
Author(s):  
Milenko Tokic ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Metabolic Engineering ◽  
Kinetic Models ◽  
Rational Design ◽  
Large Scale ◽  
Single Gene ◽  
Central Carbon Metabolism ◽  
Scale Model ◽  
Metabolic Responses ◽  
Design And Optimization ◽  
Genetic Perturbations

AbstractA high tolerance ofPseudomonas putidato toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed large-scale kinetic models ofP. putidato predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model ofP. putidaKT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism ofP. putida. Using the medium complexity core model as a scaffold, we employed the ORACLE framework to generate populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain ofP. putidaKT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinantP. putidastrains for improved production of biofuels and biochemicals.

scholarly journals Choked accretion: from radial infall to bipolar outflows by breaking spherical symmetry

2019 ◽  
Vol 490 (4) ◽  
pp. 5078-5087 ◽  
Author(s):  
Alejandro Aguayo-Ortiz ◽  
Emilio Tejeda ◽  
X Hernandez
Keyword(s):  
Steady State ◽  
Spherical Symmetry ◽  
Large Scale ◽  
Accretion Rate ◽  
Polar Regions ◽  
Bipolar Outflows ◽  
Accretion Flows ◽  
A Value ◽  
Outflow Region ◽  
Steady State Solutions

ABSTRACT Steady-state, spherically symmetric accretion flows are well understood in terms of the Bondi solution. Spherical symmetry, however, is necessarily an idealized approximation to reality. Here we explore the consequences of deviations away from spherical symmetry, first through a simple analytic model to motivate the physical processes involved, and then through hydrodynamical, numerical simulations of an ideal fluid accreting on to a Newtonian gravitating object. Specifically, we consider axisymmetric, large-scale, small-amplitude deviations in the density field such that the equatorial plane is overdense as compared to the polar regions. We find that the resulting polar density gradient dramatically alters the Bondi result and gives rise to steady-state solutions presenting bipolar outflows. As the density contrast increases, more and more material is ejected from the system, attaining speeds larger than the local escape velocities for even modest density contrasts. Interestingly, interior to the outflow region, the flow tends locally towards the Bondi solution, with a resulting total mass accretion rate through the inner boundary choking at a value very close to the corresponding Bondi one. Thus, the numerical experiments performed suggest the appearance of a maximum achievable accretion rate, with any extra material being ejected, even for very small departures from spherical symmetry.

scholarly journals Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

2019 ◽  
Vol 52 ◽  
pp. 29-41 ◽  
Author(s):  
Tuure Hameri ◽  
Georgios Fengos ◽  
Meric Ataman ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Steady State ◽  
Kinetic Models ◽  
Steady State Solutions

scholarly journals Self-regulating genes. Exact steady state solution by using Poisson representation

Open Physics ◽  
2014 ◽  
Vol 12 (9) ◽  
Author(s):  
István Sugár ◽  
István Simon
Keyword(s):  
Steady State ◽  
Cell Biology ◽  
Regulatory Networks ◽  
Steady State Solution ◽  
E Coli ◽  
Feedback Model ◽  
Quantitative Understanding ◽  
Poisson Representation ◽  
And Behavior ◽  
Steady State Solutions

AbstractSystems biology studies the structure and behavior of complex gene regulatory networks. One of its aims is to develop a quantitative understanding of the modular components that constitute such networks. The self-regulating gene is a type of auto regulatory genetic modules which appears in over 40% of known transcription factors in E. coli. In this work, using the technique of Poisson Representation, we are able to provide exact steady state solutions for this feedback model. By using the methods of synthetic biology (P.E.M. Purnick and Weiss, R., Nature Reviews, Molecular Cell Biology, 2009, 10: 410–422) one can build the system itself from modules like this.

scholarly journals Automation assisted anaerobic phenotyping for metabolic engineering

2021 ◽  
Author(s):  
Kaushik Raj ◽  
Naveen Venayak ◽  
Patrick Diep ◽  
Sai Akhil Golla ◽  
Alexander F. Yakunin ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
High Throughput ◽  
Large Scale ◽  
Small Scale ◽  
Chemical Production ◽  
E Coli ◽  
Strain Design ◽  
Phenotypic Screen ◽  
Wide Range ◽  
Scale Down

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for a high-throughput, laboratory scale methods to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we assess its performance in two case studies - an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. Further, we propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding, in conjunction with our phenotyping platform to serve as an effective scale-down model for bioreactor phenotypes. By integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Efficient long fragment editing technique enables large-scale and scarless bacterial genome engineering

2020 ◽  
Author(s):  
Chaoyong Huang ◽  
Liwei Guo ◽  
Jingge Wang ◽  
Ning Wang ◽  
Yi-Xin Huo
Keyword(s):  
Metabolic Engineering ◽  
Large Scale ◽  
Genome Engineering ◽  
Bacterial Genome ◽  
Open Reading Frames ◽  
Deletion Mutants ◽  
Dna Fragments ◽  
E Coli ◽  
Stable Plasmid ◽  
Reading Frames

ABSTRACTBacteria are versatile living systems that enhance our understanding of nature and enable biosynthesis of valuable chemicals. Long fragment editing techniques are of great importance for accelerating bacterial genome engineering to obtain desirable and genetically stable strains. However, the existing genome editing methods cannot meet the needs of engineers. We herein report an efficient long fragment editing method for large-scale and scarless genome engineering in Escherichia coli. The method enabled us to insert DNA fragments up to 12 kb into the genome and to delete DNA fragments up to 186.7 kb from the genome, with positive rates over 95%. We applied this method for E. coli genome simplification, resulting in 12 individual deletion mutants and four cumulative deletion mutants. The simplest genome lost a total of 370.6 kb of DNA sequence containing 364 open reading frames. Additionally, we applied this technique to metabolic engineering and obtained a genetically stable plasmid-independent isobutanol production strain that produced 1.3 g/L isobutanol via shake-flask fermentation. These results suggest that the method is a powerful genome engineering tool, highlighting its potential to be applied in synthetic biology and metabolic engineering.

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