scholarly journals Gazing into the Metaboverse: Automated exploration and contextualization of metabolic data

2020 ◽  
Author(s):  
Jordan A. Berg ◽  
Youjia Zhou ◽  
T. Cameron Waller ◽  
Yeyun Ouyang ◽  
Sara M. Nowinski ◽  
...  
Keyword(s):  
Reaction Network ◽  
Reaction Networks ◽  
Metabolic Reaction ◽  
Automated Extraction ◽  
Reaction Systems ◽  
High Magnitude ◽  
Downstream Effects ◽  
A Cell ◽  
Metabolic Data ◽  
Omic Data

AbstractMetabolism and its component reactions are complex, each with variable inputs, outputs, and modifiers. The harmony between these factors consequently determines the health and stability of a cell or an organism. Perturbations to any reaction or set of reactions can have rippling downstream effects, which can be challenging to trace across the total reaction network, particularly when the effects occur between complex interconnected pathways. Researchers have primarily utilized reductionist approaches to understand metabolic reaction systems; however, these simplistic methods often limit the scope of the analysis. Even systems-centric omics approaches can be limited when only a handful of high magnitude signals in the data are prioritized for interpretation. To address these challenges, we developed Metaboverse, an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other reaction networks. This framework will be foundational in increasing our ability to holistically understand static, temporal, and multi-condition metabolic events and perturbations as well as gene-metabolite intra-cooperativity. Metaboverse is freely available under a GPL-3.0 license at https://github.com/Metaboverse/.

scholarly journals Molecular Diversity and Network Complexity in Growing Protocells

Life ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 53
Author(s):  
Atsushi Kamimura ◽  
Kunihiko Kaneko
Keyword(s):  
Molecular Species ◽  
Catalytic Reaction ◽  
Molecular Diversity ◽  
Reaction Network ◽  
Cellular Growth ◽  
Reaction Networks ◽  
Cell Reproduction ◽  
A Cell ◽  
Molecular Components ◽  
Cellular Components

A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address the essential role of the huge diversity of cellular components, we studied a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources were limited, the diversity in the intracellular components was found to be increased to allow the use of diverse resources for cellular growth. A scaling relation was demonstrated between resource abundances and molecular diversity. In the present study, we examined how the molecular species diversify and how complex catalytic reaction networks develop through an evolutionary course. At some generations, molecular species first appear as parasites that do not contribute to the replication of other molecules. Later, the species turn into host species that contribute to the replication of other species, with further diversification of molecular species. Thus, a complex joint network evolves with this successive increase in species. The present study sheds new light on the origin of molecular diversity and complex reaction networks at the primitive stage of a cell.

scholarly journals Uncertainty quantification for quantum chemical models of complex reaction networks

2016 ◽  
Vol 195 ◽  
pp. 497-520 ◽  
Author(s):  
Jonny Proppe ◽  
Tamara Husch ◽  
Gregor N. Simm ◽  
Markus Reiher
Keyword(s):  
Free Energy ◽  
Electronic Structure ◽  
State Space ◽  
Time Scales ◽  
Reaction Network ◽  
Reaction Networks ◽  
Multiple Time Scales ◽  
Complex Reaction ◽  
Discrete State ◽  
Continuous State

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

Feedback mechanisms in mineral replacement networks: an experimental investigation of the ultramafic model system

2021 ◽  
Author(s):  
Ingvild Aarrestad ◽  
Oliver Plümper ◽  
Desiree Roerdink ◽  
Andreas Beinlich
Keyword(s):  
Reaction Rate ◽  
Energy Transport ◽  
Kinetic Modelling ◽  
Indirect Evidence ◽  
Reaction Network ◽  
Reaction Networks ◽  
Silicate System ◽  
Rock Interaction ◽  
Feedback Mechanisms ◽  
Salinity Effect

<p>The overall rates of multi-component reaction networks are known to be controlled by feedback mechanisms. Feedback mechanisms represent loop systems where the output of the system is conveyed back as input and the system is either accelerated or regulated (positive and negative feedback respectively). In other words, feedback mechanisms control the rate of a reaction network without external influences. Feedback mechanisms are well-studied in a variety of reaction networks (e.g. bio-chemical, atmospheric); however, in fluid-rock interaction systems they are not researched as such. Still, indirect evidence, theoretical considerations and direct observations attest to their existence [e.g. 1, 2, 3]. It remains unknown how mass and energy transport between distinct reaction sites affect the overall reaction rate and outcome through feedback mechanisms. We propose that feedback mechanisms are a missing critical ingredient to understand reaction progress and timescales of fluid-rock interactions. We apply the serpentinization of ultramafic silicates as a relatively simple reaction network to investigate feedback mechanisms during fluid-rock interactions. Recent studies show that theoretical timescale-predictions appear inconsistent with natural observations [e.g. 4, 5]. The ultramafic silicate system is ideal for investigating feedback mechanisms as it is relevant to natural processes, is reactive on timescales that can be explored in the laboratory, and natural peridotite typically consists of less than four phases. Our preliminary observations indicate a feedback between pyroxene dissolution and olivine serpentinization. Olivine serpentinization appears to proceed faster in the presence of pyroxene. Furthermore, the bulk system reaction rate increases with increasing fluid salinity, which is opposite to the salinity effect on the monomineralic olivine system. Dunite (>90% olivine) is rare, which is why it is crucial to explore the more common pyroxene-bearing systems. The salinity effect is important to investigate due to the inevitable increase in fluid salinity from the boiling-induced phase separation and OH-uptake in the formation of serpentine. Here we present preliminary textural and chemical observations, which will subsequently be used for kinetic modelling of feedback.</p><p>[1] Ortoleva P., Merino, E., Moore, C. & Chadam, J. (1987). American Journal of Science <strong>287</strong>, 997-1007.</p><p>[2] Centrella, S., Austrheim, H., & Putnis, A. (2015). Lithos <strong>236–237</strong>, 245–255.</p><p>[3] Nakatani, T. & Nakamura, M. (2016). Geochemistry, Geophysics, Geosystems <strong>17</strong>, 3393-3419.</p><p>[4] Ingebritsen, S. E. & Manning, C. E. (2010). Geofluids <strong>10</strong>, 193-205.</p><p>[5] Beinlich, A., John, T., Vrijmoed, J.C., Tominaga, M., Magna, T. & Podladchikov, Y.Y. (2020). Nature Geoscience <strong>13</strong>, 307–311.</p>

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

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.

Optimization of regulatory architectures in metabolic reaction networks

1996 ◽  
Vol 52 (4) ◽  
pp. 485-500 ◽  
Author(s):  
Vassily Hatzimanikatis ◽  
Christodoulos A. Floudas ◽  
James E. Bailey
Keyword(s):  
Reaction Networks ◽  
Metabolic Reaction

scholarly journals A compositional framework for reaction networks

2017 ◽  
Vol 29 (09) ◽  
pp. 1750028 ◽  
Author(s):  
John C. Baez ◽  
Blake S. Pollard
Keyword(s):  
Dynamical System ◽  
Steady State ◽  
General Framework ◽  
Nonlinear Dynamical System ◽  
Reaction Network ◽  
Reaction Networks ◽  
Nonlinear Dynamical ◽  
Input And Output ◽  
Open Dynamical System ◽  
Steady State Solutions

Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned ‘rate constants’, any reaction network gives rise to a nonlinear dynamical system called its ‘rate equation’. Here we generalize these ideas to ‘open’ reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding ‘open dynamical system’. This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a ‘black-boxing’ functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.

scholarly journals A Chemically Consistent Graph Architecture for Massive Reaction Networks Applied to Solid-Electrolyte Interphase Formation

2020 ◽  
Author(s):  
Samuel Blau ◽  
Hetal Patel ◽  
Evan Spotte-Smith ◽  
Xiaowei Xie ◽  
Shyam Dwaraknath ◽  
...  
Keyword(s):  
Solid Electrolyte ◽  
First Principles ◽  
Low Cost ◽  
Solid Electrolyte Interphase ◽  
Negative Electrode ◽  
Reaction Network ◽  
Reaction Networks ◽  
Graph Representations ◽  
Li Ion ◽  
Shortest Path Algorithms

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B reacts to C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6,000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.

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