Redox processes in biological systems

General principles of organization and distinctive features of the redox processes of biological systems are discussed. We paid special attention to the most examined parts of redox biology. As one of the approaches to the generalization of accumulated knowledge about redox processes, the so-called redox hypothesis of oxidative stress was examined. Extrapolation of this hypothesis on the processes taking place in plant cells, formulated on the basis of thiol-disulfide metabolism of animal cells, may help to systematize the available knowledge about redox processes in plants.     

Autonomously oscillating biochemical systems: parametric sensitivity of extrema and period

This work addresses sensitivity analysis of autonomously oscillating biochemical systems. Building on results from the engineering literature, a general analysis is presented which addresses key features of oscillatory trajectories, namely the period and local maximum or minimum values of species concentrations and reaction rates. A discussion of sensitivity invariants generalises results from steady-state sensitivity analysis to this context. The results are illustrated by the application to a model of a circadian oscillator.     

Theoretical effects of monotonically changing and fluctuating temperature on oscillating biological systems

A phenomenological model of non-isothermal evolution of naturally oscillating biological systems was constructed on the assumption that the momentary growth or decline rate is the isothermal rate at the momentary temperature, at the time that corresponds to the system's momentary state. Simulations using this model show that monotonic temperature rise or fall only affects the oscillations amplitude and frequency. In contrast, fluctuating temperature can induce irregular periodicity, aperiodic outbursts of varying magnitude and duration and/or extinction, depending on the temperature fluctuations’ complexity, and frequency relative to that of the biological system's own. This suggests that coupling of the regular oscillations of a population or biological process, with temperature or other fluctuations in its environment could be a cause of irregular and apparently chaotic patterns, at least in principle. For lack of suitable data, the described model is yet to be validated. However, it is a testable model that could be confirmed experimentally by future research.     

The frequency of cyclic processes in biological multistate systems

Summary Cyclic processes in stochastic models of macromolecular biological systems are considered. The diagram solution of the model equations (master equation) gives rise to special functions of the rate constants, called the circuit (or one-way cycle) fluxes. As Hill has shown, these functions are the fundamental theoretical components of the operational fluxes, i.e., of the rates of reaction, of transport, of energy conversion, etc. Evidence recently has been found by Monte Carlo simulations that the circuit fluxes can be interpreted as the frequencies of circuit completions. Making use of the theory of graphs, we prove that this physical interpretation of the circuit fluxes is generally valid.     

GOTax: investigating biological processes and biochemical activities along the taxonomic tree

We describe GOTax, a comparative genomics platform that integrates protein annotation with protein family classification and taxonomy. User-defined sets of proteins, protein families, annotation terms or taxonomic groups can be selected and compared, allowing for the analysis of distribution of biological processes and molecular activities over different taxonomic groups. In particular, a measure of functional similarity is available for comparing proteins and protein families, establishing functional relationships independent of evolution.     

Initiation and inhibition of free-radical processes in biochemical peroxide systems: A review

The role of complexes containing oxygen or peroxide in monooxygenase systems and models thereof, as well as in peroxidase-and quasi-peroxidase-catalyzed processes, has been reviewed. Pathways of conversion of these intermediate complexes involving single-electron (radical) and two-electron (heterolytic) mechanisms are dealt with. Peroxidase-catalyzed co-oxidation of aromatic amines and phenols is analyzed; inhibition and activation of peroxidase-catalyzed reactions are characterized quantitatively. Oxidation of chromogenic substrates (ABTS, OPD, and TMB) in the presence of phenolic inhibitors or polydisulfides of substituted phenols is characterized by inhibition constants (K _i, μmol). Activation of peroxidase-catalyzed oxidation of the same substrates is characterized by the degree (coefficient) of activation (α, M^?1), which was determined for 2-aminothiazole, melamine, tetrazole, and its 5-substituted derivatives. Examples of applied use of peroxidase-catalyzed enzyme and model systems are given (oxidation of organic compounds, chemical analysis, enzyme immunoassay, tests for antioxidant activity of biological fluids).     

Initiation and inhibition of free-radical processes in biochemical peroxidase systems: a review

The role of complexes containing oxygen or peroxide in monooxygenase systems and models thereof, as well as in peroxidase- and quasi-peroxidase-catalyzed processes, has been reviewed. Pathways of conversion of these intermediate complexes involving single-electron (radical) and two-electron (heterolytic) mechanisms are dealt with. Coupled peroxidase-catalyzed oxidation of aromatic amines and phenols is analyzed; inhibition and activation of peroxidase-catalyzed reactions are characterized quantitatively. Oxidation of chromogenic substrates (ABTS, OPD, and TMB) in the presence of phenolic inhibitors or polydisulfides of substituted phenols is characterized by inhibition constants (Ki, micromol). Activation of peroxidase-catalyzed oxidation of the same substrates is characterized by the degree (coefficient) of activation (alpha, M(-1)), which was determined for 2-aminothiazole, melamine, tetrazole, and its 5-substituted derivatives. Examples of applied use of peroxidase-catalyzed enzyme and model systems are given (oxidation of organic compounds, chemical analysis, enzyme immunoassay, tests for antioxidant activity of biological fluids).     

Energy and biological evolution—I. The equilibrium states of biochemical processes

The Thom gradient model of morphogenesis poses the followinga posteriori problem: “From the observed morphology of a given natural process (effect) determine the dynamics of the process (cause)”. In this paper we consider the classicala priori problem: “Given the cause (dynamics) determine the effect (resultant morphology)”. We find that in biochemical processes the mechanisms for energy activation, energy-matter interaction and energy dissipation determine the dynamics. Furthermore there exists basic energy mechanisms which drive the equilibrium states through the elementary catastrophes of Thom. A comparison with current theories shows that our models describe open ecological food chains and their dynamical systems generalize the equations of organisation posed by M. Eigen. Work supported by a Research Associateship of the International Centre for Theoretical Physics, P.O.B. 586, Miramare, 34100 Trieste, Italy.     

Stochastic P systems and the simulation of biochemical processes with dynamic compartments

We introduce a sequential rewriting strategy for P systems based on Gillespie's stochastic simulation algorithm, and show that the resulting formalism of stochastic P systems makes it possible to simulate biochemical processes in dynamically changing, nested compartments. Stochastic P systems have been implemented using the spatially explicit programming language MGS. Implementation examples include models of the Lotka-Volterra auto-catalytic system, and the life cycle of the Semliki Forest virus.     

Hydrophobic hydrophilic phenomena in biochemical processes

The evolution of concepts developed in the study of the hydrophobic affect is surveyed, within the more general context of solvent-induced effects. A systematic analysis of the solvent-induced contribution to the driving force for the process of protein folding has led to two important modifications in our understanding of these effects. First, the conventional concepts of hydrophobic solvation and hydrophobic interactions had to be replaced by their respective conditional effects. Second, each of the hydrophobic effects has also a corresponding hydrophilic counterpart. Some of the latter effects could contribute significantly to the total driving force for the process of protein folding, and perhaps even dominate the driving force for biochemical processes.     

On-line monitoring and control of substrate concentrations in biological processes by flow injection analysis systems

Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process ofSaccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.     

Biological probability: cognitive processes of generating probabilities of events in biological systems.

This paper analyses relationships between probabilities of events happening in biological systems (or probabilistic disposition of systems) and cognitive properties of biological entities comprising such systems. Two kinds of cognitive properties are identified as relevant to the current problem: the ability to respond differently against different configurations of the environment (discriminability of cognition), and the ability to make an appropriate response to maintain a particular relation with the environment (selectivity of cognition). A basic framework bridging the two features of living systems, probabilistic disposition and the cognitive properties, is presented towards a general theory explaining the process generating probabilities of biological events. In this framework, a deterministic model of a system of entities is developed, in which objects are described as subjects that cognize events (i.e. entities as cognizers). Cognition is used in a wider sense, including not only biotic but also abiotic, and cognizers are conceptually distinguished from the meta-observer who describes the system externally. Based on this perspective, this paper seeks to explicate how events can occur in an uncertain, probabilistic manner, if observed from a cognizer viewpoint, even under a deterministic system. Each cognizer is identified with both the set of states that are actually taken, and its motion function which maps its state uniquely to a successor state depending on the current states of itself and of the rest of cognizers constituting the system. The model analysis reveals that the cognitive properties, discriminability and selectivity, of a cognizer can contribute to determining the probability of an event encountered by the cognizer itself-in particular, discrimination reducing the uncertainty in events occurrence for the cognizer. Biological implication of this result is discussed focusing on the concept of the probability of survival and reproduction.     

Induction versus modulation in phytochrome-regulated biochemical processes

The time course of appearance of competence towards phytochrome (P_fr) was studied in cotyledons of mustard (Sinapis alba L.) with regard to the light-mediated formation of anthocyanin (aglycone cyanidin) and NADP-dependent plastidal glyceraldehyde-3-phosphate dehydrogenase (GPD, EC 1.2.1.13). The experiments were performed to answer the following question: Does phytochrome act to turn responses on (induction), or — as an alternative — does phytochrome cause an amplification of processes already occurring in absolute darkness albeit at low rates once competence is reached (modulation)? The data show that in the case of GPD, phytochrome causes an amplification of the rate of synthesis once the competence point is reached at approximately 36 h after sowing at 25° C. In the case of anthocyanin, it was found that two distinct points of competence exist (26 h and 39 h after sowing, 25° C). In the case of ‘early anthocyanin’ (competence point at 26 h), synthesis does not occur in darkness without P_fr, while in the case of ‘late anthocyanin’ (competence point at 39 h), phytochrome causes an amplification of a process occurring in complete darkness albeit at a very low rate. It is concluded that in phytochrome-mediated photomorphogenesis, modulation as well as induction of biosynthetic processes plays a role.     

Optimization in integrated biochemical systems

As of yet, steady-state optimization in biochemical systems has been limited to a few studies in which networks of fluxes were optimized. These networks of fluxes are represented by linear (stoichiometric) equations that are used as constraints in a linear program, and a flux or a sum of weighted fluxes is used as the objective function. In contrast to networks of fluxes, systems of metabolic processes have not been optimized in a comparable manner. The primary reason is that these types of integrated biochemical systems are full of synergisms, antagonisms, and regulatory mechanisms that can only be captured appropriately with nonlinear models. These models are mathematically complex and difficult to analyze. In most cases it is not even possible to compute, let alone optimize, steady-state solutions analytically. Rare exceptions are S-system representations. These are nonlinear and able to represent virtually all types of dynamic behaviors, but their steady states are characterized by linear equations that can be evaluated both analytically and numerically. The steady-state equations are expressed in terms of the logarithms of the original variables, and because a function and its logarithms of the original variables, and because a function and its logarithm assume their maxima for the same argument, yields or fluxes can be optimized with linear programs expressed in terms of the logarithms of the original variables. (c) 1992 John Wiley & Sons, Inc.     

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.