scholarly journals Reaction Network Modeling of Complex Ecological Interactions: Endosymbiosis and Multilevel Regulation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tomas Veloz ◽  
Daniela Flores
Keyword(s):  
Cellular Proliferation ◽  
Cell Metabolism ◽  
Network Modeling ◽  
Reaction Network ◽  
Reaction Networks ◽  
Ecological Interactions ◽  
Mutual Benefit ◽  
Cell Replication ◽  
Regulation Mechanisms ◽  
Multilevel Regulation

Endosymbiosis is a type of symbiosis where one species of microscopic scale inhabits the cell of another species of a larger scale, such that the exchange of metabolic byproducts produces mutual benefit. These benefits can occur at different biological levels. For example, endosymbiosis promotes efficiency of the cell metabolism, cell replication, and the generation of a macroscopic layer that protects the organism from its predators. Therefore, modeling endosymbiosis requires a complex-systems and multilevel approach. We propose a model of endosymbiosis based on reaction networks, where species of the reaction network represent either ecological species, resources, or conditions for the ecological interactions to happen, and the endosymbiotic interaction mechanisms are represented by different sequences of reactions (processes) in the reaction network. As an example, we develop a toy model of the coral endosymbiotic interaction. The model considers two reaction networks, representing biochemical traffic and cellular proliferation levels, respectively. In addition, the model incorporates top-down and bottom-up regulation mechanisms that stabilizes the endosymbiotic interaction.

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 Complex III Inhibition-Induced Pulmonary Hypertension Affects the Mitochondrial Proteomic Landscape

2020 ◽  
Vol 21 (16) ◽  
pp. 5683
Author(s):  
Joel James ◽  
Mathews Valuparampil Varghese ◽  
Mikhail Vasilyev ◽  
Paul R. Langlais ◽  
Stevan P. Tofovic ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Mitochondrial Function ◽  
Cellular Proliferation ◽  
Protein Import ◽  
Tricarboxylic Acid Cycle ◽  
Cell Metabolism ◽  
Complex Iii ◽  
Acid Oxidation ◽  
Mitochondrial Complex ◽  
Mitochondrial Proteome

The mitochondria play a vital role in controlling cell metabolism and regulating crucial cellular outcomes. We previously demonstrated that chronic inhibition of the mitochondrial complex III in rats by Antimycin A (AA) induced sustained pulmonary vasoconstriction. On the metabolic level, AA-induced mitochondrial dysfunction resulted in a glycolytic shift that was reported as the primary contributor to pulmonary hypertension pathogenesis. However, the regulatory proteins driving this metabolic shift with complex III inhibition are yet to be explored. Therefore, to delineate the mechanisms, we followed changes in the rat lung mitochondrial proteome throughout AA treatment. Rats treated with AA for up to 24 days showed a disturbed mitochondrial proteome with significant changes in 28 proteins (p < 0.05). We observed a time-dependent decrease in the expression of key proteins that regulate fatty acid oxidation, the tricarboxylic acid cycle, the electron transport chain, and amino acid metabolism, indicating a correlation with diminished mitochondrial function. We also found a significant dysregulation in proteins that controls the protein import machinery and the clearance and detoxification of oxidatively damaged peptides via proteolysis and mitophagy. This could potentially lead to the onset of mitochondrial toxicity due to misfolded protein stress. We propose that chronic inhibition of mitochondrial complex III attenuates mitochondrial function by disruption of the global mitochondrial metabolism. This potentially aggravates cellular proliferation by initiating a glycolytic switch and thereby leads to pulmonary hypertension.

scholarly journals Dissecting Germ Cell Metabolism through Network Modeling

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137607 ◽  
Author(s):  
Leanne S. Whitmore ◽  
Ping Ye
Keyword(s):  
Germ Cell ◽  
Cell Metabolism ◽  
Network Modeling

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.

scholarly journals A general technique for the detection of switch-like bistability in chemical reaction networks governed by mass action kinetics with conservation laws

2020 ◽  
Author(s):  
Brandon C Reyes ◽  
Irene Otero-Muras ◽  
Vladislav A Petyuk
Keyword(s):  
Conservation Laws ◽  
Signaling Pathways ◽  
Chemical Reaction ◽  
Reaction Network ◽  
Mass Action ◽  
Numerical Continuation ◽  
Reaction Networks ◽  
General Technique ◽  
Bistable Switch ◽  
Mass Action Kinetics

AbstractBackgroundTheoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws.ResultsIn this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves 1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, 2) minimization of the objective function to search for conditions resulting in a zero eigenvalue 3) computation of a confidence level that describes if the global minimum has been found and 4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability.ConclusionsWe have developed a variation of an optimization-based method for discovery of bistability, which is not limited to the structure of the chemical reaction network. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

scholarly journals Scalable Reaction Network Modeling with Automatic Validation of Consistency in Event-B

2021 ◽  
Author(s):  
Usman Sanwal ◽  
Thai Son Hoang ◽  
Luigia Petre ◽  
Ion Petre
Keyword(s):  
Formal Method ◽  
Network Modeling ◽  
Reaction Network ◽  
System Level ◽  
Evolutionary Pathway ◽  
Trace Back ◽  
Recent Approach ◽  
Erbb Signaling ◽  
Consistency Properties ◽  
Erbb Signaling Pathway

Abstract Constructing a large biological model is a difficult, error-prone process. Small errors in writing a part of the model cascade to the system level and their sources are difficult to trace back. In this paper we extend a recent approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to validate for model consistency step-by-step in an automated way. We demonstrate this approach on a model of the heat shock response and its scalability on a model of the ErbB signaling pathway, a key evolutionary pathway with a significant role in development and in many types of cancer. All consistency properties of the model were proved automatically with computer support.

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