scholarly journals Understanding Metabolic Flux Behaviour in Whole-Cell Model Output

2021 ◽  
Vol 8 ◽  
Author(s):  
Sophie Landon ◽  
Oliver Chalkley ◽  
Gus Breese ◽  
Claire Grierson ◽  
Lucia Marucci

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.

2020 ◽  
Author(s):  
Sophie Landon ◽  
Oliver Chalkley ◽  
Gus Breese ◽  
Claire Grierson ◽  
Lucia Marucci

SummaryWhole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional, thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts, and, as whole-cell models become more widely built and used, aid genome design.


eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Daniel R Larson ◽  
Christoph Fritzsch ◽  
Liang Sun ◽  
Xiuhau Meng ◽  
David S Lawrence ◽  
...  

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus.


2020 ◽  
Vol 11 ◽  
Author(s):  
Alejandro Abdala Asbun ◽  
Marc A. Besseling ◽  
Sergio Balzano ◽  
Judith D. L. van Bleijswijk ◽  
Harry J. Witte ◽  
...  

Marker gene sequencing of the rRNA operon (16S, 18S, ITS) or cytochrome c oxidase I (CO1) is a popular means to assess microbial communities of the environment, microbiomes associated with plants and animals, as well as communities of multicellular organisms via environmental DNA sequencing. Since this technique is based on sequencing a single gene, or even only parts of a single gene rather than the entire genome, the number of reads needed per sample to assess the microbial community structure is lower than that required for metagenome sequencing. This makes marker gene sequencing affordable to nearly any laboratory. Despite the relative ease and cost-efficiency of data generation, analyzing the resulting sequence data requires computational skills that may go beyond the standard repertoire of a current molecular biologist/ecologist. We have developed Cascabel, a scalable, flexible, and easy-to-use amplicon sequence data analysis pipeline, which uses Snakemake and a combination of existing and newly developed solutions for its computational steps. Cascabel takes the raw data as input and delivers a table of operational taxonomic units (OTUs) or Amplicon Sequence Variants (ASVs) in BIOM and text format and representative sequences. Cascabel is a highly versatile software that allows users to customize several steps of the pipeline, such as selecting from a set of OTU clustering methods or performing ASV analysis. In addition, we designed Cascabel to run in any linux/unix computing environment from desktop computers to computing servers making use of parallel processing if possible. The analyses and results are fully reproducible and documented in an HTML and optional pdf report. Cascabel is freely available at Github: https://github.com/AlejandroAb/CASCABEL.


2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.


2015 ◽  
Vol 308 (5) ◽  
pp. H510-H523 ◽  
Author(s):  
Xiao Wang ◽  
Seth H. Weinberg ◽  
Yan Hao ◽  
Eric A. Sobie ◽  
Gregory D. Smith

Population density approaches to modeling local control of Ca2+-induced Ca2+ release in cardiac myocytes can be used to construct minimal whole cell models that accurately represent heterogeneous local Ca2+ signals. Unfortunately, the computational complexity of such “local/global” whole cell models scales with the number of Ca2+ release unit (CaRU) states, which is a rapidly increasing function of the number of ryanodine receptors (RyRs) per CaRU. Here we present an alternative approach based on a Langevin description of the collective gating of RyRs coupled by local Ca2+ concentration ([Ca2+]). The computational efficiency of this approach no longer depends on the number of RyRs per CaRU. When the RyR model is minimal, Langevin equations may be replaced by a single Fokker-Planck equation, yielding an extremely compact and efficient local/global whole cell model that reproduces and helps interpret recent experiments that investigate Ca2+ homeostasis in permeabilized ventricular myocytes. Our calculations show that elevated myoplasmic [Ca2+] promotes elevated network sarcoplasmic reticulum (SR) [Ca2+] via SR Ca2+-ATPase-mediated Ca2+ uptake. However, elevated myoplasmic [Ca2+] may also activate RyRs and promote stochastic SR Ca2+ release, which can in turn decrease SR [Ca2+]. Increasing myoplasmic [Ca2+] results in an exponential increase in spark-mediated release and a linear increase in nonspark-mediated release, consistent with recent experiments. The model exhibits two steady-state release fluxes for the same network SR [Ca2+] depending on whether myoplasmic [Ca2+] is low or high. In the later case, spontaneous release decreases SR [Ca2+] in a manner that maintains robust Ca2+ sparks.


1993 ◽  
Vol 69 (6) ◽  
pp. 1948-1965 ◽  
Author(s):  
U. S. Bhalla ◽  
J. M. Bower

1. Detailed compartmental computer simulations of single mitral and granule cells of the vertebrate olfactory bulb were constructed using previously published geometric data. Electrophysiological properties were determined by comparing model output to previously published experimental data, mainly current-clamp recordings. 2. The passive electrical properties of each model were explored by comparing model output with intracellular potential data from hyperpolarizing current injection experiments. The results suggest that membrane resistivity in both cells is nonuniform, with somatas having a substantially lower resistivity than the dendrites. 3. The active properties of these cells were explored by incorporating active ion channels into modeled compartments. On the basis of evidence from the literature, the mitral cell model included six channel types: fast sodium, fast delayed rectifier (Kfast), slow delayed rectifier (K), transient outward potassium current (KA), voltage- and calcium-dependent potassium current (KCa), and L-type calcium current. The granule cell model included four channel types: rat brain sodium, K, KA, and the non-inactivating muscarinic potassium current (KM). Modeled channels were based on the Hodgkin-Huxley formalism. 4. Representative kinetics for each of the channel classes above were obtained from the literature. The experimentally unknown spatial distributions of each included channel were obtained by systematic parameter searches. These were conducted in two ways: large-scale simulation series, in which each parameter was varied in turn, and an adaptation of a multidimensional conjugate gradient method. In each case, the simulated results were compared wtih experimental data using a curve-matching function evaluating mean squared differences of several aspects of the simulated and experimental voltage waveforms. 5. Systematic parameter variations revealed a single distinct region of parameter space in which the mitral cell model best fit the data. This region of parameter space was also very robust to parameter variations. Specifically, optimum performance was obtained when calcium and slow K channels were concentrated in the glomeruli, with a lower density in the soma and proximal secondary dendrites. The distribution of sodium and fast potassium channels, on the other hand, was highest at the soma and axon, with a much lighter distribution throughout the secondary dendrites. The KA and KCa channels were also concentrated near the soma. 6. The parameter search of the granule cell model was much less restrained by experimental data. Several parameter regimes were found that gave a good match to the data.(ABSTRACT TRUNCATED AT 400 WORDS)


1999 ◽  
Vol 1 (1) ◽  
pp. 20-29 ◽  
Author(s):  
Lynn M. Baxendale-Cox ◽  
Randall L. Duncan

Essential or primary hypertension is a multifactorial disease that is expressed as a result of complex interactions between genes and environmental influences. Several mutations in many different proteins are associated with expression of hypertension, including abnormalities in the epithelial sodium channel (ENaC) found in absorptive organs (i.e., distal colon, distal tubule of the nephron). Some of these mutations result in structural and/or functional alterations in ENaC-mediated Na+ entry in epithelia responsible for fluid and electrolyte balance and are associated with expression of hypertension. Studies support the notion that there is a link between ENaC and hypertension of both the monogenic (single gene mutation) and primary or essential type (a multifactorial disease). Alterations of other aspects of the environment of absorptive cells (e.g., hyperinsulinemia, hyperaldosteronemia, high plasma cortisol, high plasma Na+) have also been shown to elicit hyperabsorption of Na+ via ENaC and therefore could contribute significantly to expression of hypertension in people with intermediate phenotypes. This article describes an initial study in which the effects of an environmental factor, extracellular levels of insulin, on ENaC were examined in a normal kidney cell model. Electrophysiologic techniques revealed that ENaC density rapidly increased in response to addition of insulin to the basolateral bath. This autoregulatory recruitment of Na+ total channel density masked a slight decrease in open channel probability. Insulin’s effect on ENaC function and implications on fluid and electrolyte balance and expression of primary hypertension is discussed.


2015 ◽  
Vol 108 (2) ◽  
pp. 390a
Author(s):  
Tyler M. Earnest ◽  
Ke Chen ◽  
Jonathan Lai ◽  
Zan Luthey-Schulten

Author(s):  
Xuejin Li ◽  
Zhangli Peng ◽  
Huan Lei ◽  
Ming Dao ◽  
George Em Karniadakis

This study is partially motivated by the validation of a new two-component multi-scale cell model we developed recently that treats the lipid bilayer and the cytoskeleton as two distinct components. Here, the whole cell model is validated and compared against several available experiments that examine red blood cell (RBC) mechanics, rheology and dynamics. First, we investigated RBC deformability in a microfluidic channel with a very small cross-sectional area and quantified the mechanical properties of the RBC membrane. Second, we simulated twisting torque cytometry and compared predicted rheological properties of the RBC membrane with experimental measurements. Finally, we modelled the tank-treading (TT) motion of a RBC in a shear flow and explored the effect of channel width variation on the TT frequency. We also investigated the effects of bilayer–cytoskeletal interactions on these experiments and our simulations clearly indicated that they play key roles in the determination of cell membrane mechanical, rheological and dynamical properties. These simulations serve as validation tests and moreover reveal the capabilities and limitations of the new whole cell model.


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