Enzyme Catalysis and the Outcome of Biochemical Reactions

Milanowski, Piotr; Carter, Thomas J.; Weber, Georg F.

doi:10.4172/jpb.1000271

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Nevertheless, the traditional, reductionist description of nature has typically categorized observations and created opposites that are purportedly mutually independent, thus generating partial entities of the world that have been treated as detached from one another. Such categorizations had the benefit of achieving tractability through a quasi-linearization of descriptions [26]. The construct of a dichotomy between necessary events and chance events enabled us-especially in the eras preceding computers and complexity research-to make meaningful observations about parts of the world.…”

Section: Discussionmentioning

confidence: 99%

Information Dynamics in Complex Systems Negates a Dichotomy between Chance and Necessity

Weber

2020

Information

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Entropy increases in the execution of linear physical processes. At equilibrium, all uncertainty about the future is removed and information about the past is lost. Complex systems, on the other hand, can lead to the emergence of order, sustain uncertainty about the future, and generate new information to replace all old information about the system in finite time. The Kolmogorov–Sinai entropy for events and the Kolmogorov–Chaitin complexity for strings of numbers both approximate Shannon’s entropy (an indicator for the removal of uncertainty), indicating that information production is equivalent to the degree of complexity of an event. Thus, in the execution of non-linear processes, information entropy is inseparably tied to thermodynamic entropy. Therein, the critical decision points (bifurcations), which can exert lasting impact on the evolution of the future (the “butterfly effect”), defy the definition of being either born from randomness or from determination. Nevertheless, their information evolution and degree of complexity are amenable to measurement and can meaningfully replace the dichotomy of chance versus necessity. Common anthropomorphic perceptions do not accurately account for the transient durability of information, the potential for major consequences by small actions, or the absence of a discernible opposition between coincidence and inevitability.

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Section: Discussionmentioning

confidence: 99%

Information Dynamics in Complex Systems Negates a Dichotomy between Chance and Necessity

Weber

2020

Information

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“…that are seemingly unconnected, thus generating sub-entities of the world that have been treated as mutually detached. Practically, such reductionist categorizations have achieved a quasi-linearization of descriptions [21], thus gaining tractability at the expense of excessively simplifying our perceptions of nature. This thinking has divided the physical world into basic entities (such as space, time, matter), the separate identities of which have been considered to be absolute.…”

Section: Implications For Epistemologymentioning

confidence: 99%

Information Gain in Event Space Reflects Chance and Necessity Components of an Event

Weber

2019

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Information flow for occurrences in phase space can be assessed through the application of the Lyapunov characteristic exponent (multiplicative ergodic theorem), which is positive for non-linear systems that act as information sources and is negative for events that constitute information sinks. Attempts to unify the reversible descriptions of dynamics with the irreversible descriptions of thermodynamics have replaced phase space models with event space models. The introduction of operators for time and entropy in lieu of traditional trajectories has consequently limited—to eigenvectors and eigenvalues—the extent of knowable details about systems governed by such depictions. In this setting, a modified Lyapunov characteristic exponent for vector spaces can be used as a descriptor for the evolution of information, which is reflective of the associated extent of undetermined features. This novel application of the multiplicative ergodic theorem leads directly to the formulation of a dimension that is a measure for the information gain attributable to the occurrence. Thus, it provides a readout for the magnitudes of chance and necessity that contribute to an event. Related algorithms express a unification of information content, degree of randomness, and complexity (fractal dimension) in event space.

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“…The study of enzyme kinetics and modeling is experimenting a revival with both the access to web databases and the increasing computer power [1]- [4]. Complex biological problems can now be investigated with experimental and theoretical approaches, both presenting their own limitations: experimental data are sometimes difficult, costly and lengthy to be implemented; theoretical alternatives offer many survey possibilities but must be in harmony with the experimental findings.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Evaluation of Both Unknown Substrate Concentrations and Enzyme Kinetic Constants of Metabolic Cycles

Sillero

García-Herrero²

2015

JBiSE

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A formula has been deduced, and named as 2ESKV, relating the concentration of two consecutive substrates of a metabolic cycle with the kinetic constants of the two enzymes involved in their synthesis and degradation. After application of formula 2ESKV to consecutive pairs of substrates and enzymes, a system of interrelated equations was obtained allowing a great variety of theoretical postulates to calculate, back and forth: bunches of unknown enzyme kinetic constants and substrate concentrations, from complementary sets of known data. This vision of a metabolic cycle is of partial application to irreversible pathways and can be useful for modeling and understanding of metabolomics data. To our knowledge, the formula 2ESKV is here described for the first time.

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Enzyme Catalysis and the Outcome of Biochemical Reactions

Cited by 5 publications

References 30 publications

Information Dynamics in Complex Systems Negates a Dichotomy between Chance and Necessity

Information Dynamics in Complex Systems Negates a Dichotomy between Chance and Necessity

Information Gain in Event Space Reflects Chance and Necessity Components of an Event

Theoretical Evaluation of Both Unknown Substrate Concentrations and Enzyme Kinetic Constants of Metabolic Cycles

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