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

Lloyd, Colton J.; Ebrahim, Ali; Yang, Laurence T.; King, Zachary A.; Catoiu, Edward; O’Brien, Edward J.; Liu, Joanne K.; Kim, Jaehyung

doi:10.1371/journal.pcbi.1006302

Cited by 141 publications

(200 citation statements)

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“…To complement the MOMENT-based validation of the computed turnover numbers, a similar validation approach was employed with the iJL1678b-ME genome-scale model of E. coli metabolism and gene expression 49 . The kapps were mapped to iJL1678b-ME as previously described 16 .…”

Section: Me Modelingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, nonphysiological, inconsistent, and labor-intensive to obtain. We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

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Section: Me Modelingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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“…Nevertheless, the optimal solution from these GEMs can easily deviate from the real flux distribution. To address this issue, it is advisable to include extra information/ constraints, such as protein concentration (Lloyd et al, 2018;Sánchez et al, 2017), transcriptional regulation (Lerman et al, 2012), metabolic regulation, thermodynamics (Canelas, Ras, ten Pierick, van Gulik, & Heijnen, 2011;Niebel et al, 2019;Saa & Nielsen, 2015) and enzyme kinetics, and so forth. For instance, Sánchez et al (2017) presented GEKCO which accounts for enzymes as a part of reactions in a GEM, allowing that the simulated flux of each reaction does not outstrip its maximum capacity.…”

Section: Integration Of Metabolomics Data Into Metabolic Network: mentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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“…The main algorithm 317 begins by computing an in initial optimal internal flux vector each species according to 318 Eq. (10). A secondary optimization is then carried out, which can be specified by the 319 user.…”

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confidence: 99%

“…3 provides 354 exact solutions to the dynamic FBA problem Eqs. (6), (7) and (10). Of course, 355 numerical limitations imply that we will not re-optimize precisely at each t l , and so we 356 must investigate the impact of this error.…”

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Minimizing the number of optimizations for efficient community dynamic flux balance analysis

Brunner

Chia

2020

Preprint

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Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-know technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps.Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps, instead of the full optimization problem. Using our method, we can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution remains feasible). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities of organisms. We demonstrate that the method gives an approximately 85% speed-up per organism over the standard and widely used method. Our method has been implemented in the Python language and source code is available at https://github.com/jdbrunner/surfin_fba and in the Python Package Index (PyPI) as surfinFBA. Author summary.The standard method in the field for dynamic flux balance analysis carries a prohibitively high computational cost because it requires solving a linear optimization problem at each time-step. We have developed a novel method for producing solutions to this dynamical system which greatly reduces the number of optimization problems that must be solved. We prove mathematically that we can solve the optimization problem once and simulate the system forward as an ordinary differential equation for some time interval, and solutions to this ODE provide solutions to the optimization problem. Eventually, the system reaches an easily checkable condition which implies that another optimization problem must be solved. We compare our method with the classical method to validate that it provides equivalent solutions in much lower computational time.March 7, 2020 1/18 Introduction. 1 Microbial communities and human health. 2 The makeup of microbial communities is often complex, dynamic, and hard to predict. 3 However, microbial community structure has a profound effect on human health and 4 disease [1-7]. These two facts have lead to significant interest in mathematical models 5 which can predict relative abundances among microbes in a community. Recently, 6 advances in genetic sequencing have allowed the creation of genome-scale models 7 (GEMs) wh...

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

Cited by 141 publications

References 29 publications

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Minimizing the number of optimizations for efficient community dynamic flux balance analysis

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