Evolution of structure in RNA

Evidence is presented that structures formed by RNA and by RNA in association with protein have evolved from simpler structures by successive unions among them. The progressively more complex molecular structures have conferred selective advantage in evolution by progressively enhancing the specificities of the biochemical reactions. Before each union, the RNAs which joined at the time of union belonged to separate reproducing species. The record of unions in RNA therefore reflects unions among species in the biosphere, tracing the evolution of life from quite simple reproducing molecules up to well developed organisms.     

Kinetic modeling of the recA protein promoted renaturation of complementary DNA strands

Quantitative agarose gel assays reveal that the recA protein promoted renaturation of complementary DNA strands (phi X DNA) proceeds in two stages. The first stage results in the formation of unit-length duplex DNA as well as a distribution of other products ("initial products"). In the second stage, the initial products are converted to complex multipaired DNA structures ("network DNA"). In the presence of ATP, the initial products are formed within 2 min and are then rapidly converted to network DNA. In the absence of ATP, the initial products are formed nearly as fast as with ATP present, but they are converted to network DNA at a much lower rate. The time-dependent formation of initial products and network DNA from complementary single strands for both the ATP-stimulated and ATP-independent reactions can be modeled by using a simple two-step sequential kinetic scheme. This model indicates that the primary effect of ATP in the recA protein promoted renaturation reaction is not on the initial pairing step (which leads to the formation of initial products) but rather is to increase the rate at which subsequent pairing events can occur.     

Mathematical modelling and kinetic study for CD production catalysed by Toruzyme® and CGTase from Bacillus firmus strain 37

A new mathematical model was developed for the kinetics of α-, β- and γ-cyclodextrin production, expanding an existing model that only included the production of β- and γ-cyclodextrins, because a detailed kinetic modelling of the reactions involved allows the manipulation of the process yields. The kinetic behaviour of the commercial enzyme Toruzyme^® was studied with maltodextrin as substrate at different concentrations and for CGTase from Bacillus firmus strain 37 at a concentration of 100 g L⁻¹. The mathematical model showed a proper fit to the experimental data, within the 24-h period studied, confirming that the considered hypotheses represent the kinetic behaviour of the enzymes in the reaction medium. The kinetic parameters generated by the model allowed reproducing previous observed qualitative tendencies as it can be seen that changing experimental conditions in the reaction process such as enzyme and substrate concentrations results in large changes in the enzyme kinetics and using high substrate concentrations does not guarantee the highest conversion rates due to enzyme inhibition and reverse reactions. In addition, this new mathematical model complements previous qualitative observations enabling the manipulation of the direct and reverse reactions catalysed by the enzyme by adjusting the reaction conditions, to target quantitative results of increased productivity and better efficiency in the production of a desired cyclodextrin.

     

Kinetic constraints for formation of steady states in biochemical networks

The constraint-based analysis has emerged as a useful tool for analysis of biochemical networks. This work introduces the concept of kinetic constraints. It is shown that maximal reaction rates are appropriate constraints only for isolated enzymatic reactions. For biochemical networks, it is revealed that constraints for formation of a steady state require specific relationships between maximal reaction rates of all enzymes. The constraints for a branched network are significantly different from those for a cyclic network. Moreover, the constraints do not require Michaelis-Menten constants for most enzymes, and they only require the constants for the enzymes at the branching or cyclic point. Reversibility of reactions at system boundary or branching point may significantly impact on kinetic constraints. When enzymes are regulated, regulations may impose severe kinetic constraints for the formation of steady states. As the complexity of a network increases, kinetic constraints become more severe. In addition, it is demonstrated that kinetic constraints for networks with co-regulation can be analyzed using the approach. In general, co-regulation enhances the constraints and therefore larger fluctuations in fluxes can be accommodated in the networks with co-regulation. As a first example of the application, we derive the kinetic constraints for an actual network that describes sucrose accumulation in the sugar cane culm, and confirm their validity using numerical simulations.     

Selection advantage of metabolic over non-metabolic replicators: A kinetic analysis

A kinetic analysis and simulation of the replication reactions of two competing replicators—one non-metabolic (thermodynamic), the other metabolic, are presented. Our analysis indicates that in a rich resource environment the non-metabolic replicator is likely to be kinetically selected for over the metabolic replicator. However, in the more typical resource-poor environment it will be the metabolic replicator that is the kinetically more stable entity, and the one that will be kinetically selected for. Accordingly, a causal relationship between the emergence of a simple replicator and the emergence of a metabolic system is indicated. The results lend further support for the “replication first” school of thought in the origin of life problem by providing a mechanistic basis for the emergence of a metabolism, once a simple non-metabolic replicating system has itself been established. The study reaffirms our view that the roots of Darwinian theory may be found within standard chemical kinetic theory.     

Classification, error detection, and reconciliation of process information in complex biochemical systems

Bioprocess identification starts with collection of process information. Usually there is a variety of information available, consisting of actual measurement data, historical data, empirical kinetic and yield correlations, and general knowledge available from literature. A central problem is to find out how the various pieces of information should be integrated. In addition, one should know how to deal with missing, inconsistent, or too inaccurate data. Recently, a general systematic method for dealing with these problems, based on conservation constraints, was published, and application shown to simple black box systems. In this article, the scope is generalized by including metabolic network data and dispersed process information of diverse type and nature, such as multiple sources of the value of one particular quantity, use of kinetic expressions, analytical problems, cometabolism or mixed substrate utilization, and chemical reactions. The alkalophilic bacterium Acinetobacter calcoaceticus is used as a model organism, growing on acetate and converting xylose into xylonolactone. It is shown that all relevant pieces of information can be straightforwardly and systematically treated, by considering them as constaints. In general, it is illustrated how the search for directed process improvements starts with an optimal selection of information sources, followed by an accurate analysis of possible metabolic bottlenecks. In this particular case it is shown that the yield of A. calcoaceticus on acetate at varying xylose/acetate feed ratios can be accurately predicted using dispersed process information. (c) 1996 John Wiley & Sons, Inc.     

Top-down elasticity analysis and its application to energy metabolism in isolated mitochondria and intact cells

This paper reviews top-down elasticity analysis, which is a subset of metabolic control analysis. Top-down elasticity analysis provides a systematic yet simple experimental method to identify all the primary sites of action of an effector in complex systems and to distinguish them from all the secondary, indirect, sites of action. In the top-down approach, the complex system (for example, a mitochondrion, cell, organ or organism) is first conceptually divided into a small number of blocks of reactions interconnected by one or more metabolic intermediates. By changing the concentration of one intermediate when all others are held constant and measuring the fluxes through each block of reactions, the overall kinetic response of each block to each intermediate can be established. The concentrations of intermediates can be changed by adding new branches to the system or by manipulating the activities of blocks of reactions whose kinetics are not under investigation. To determine how much an effector alters the overall kinetics of a block of reactions, the overall kinetic response of the block to the intermediate is remeasured in the presence of the effector. Blocks that contain significant primary sites of action will display altered kinetics; blocks that change rate only because of secondary alterations in the concentrations of other metabolites will not. If desired, this elasticity analysis can be repeated with the primary target blocks subdivided into simpler blocks so that the primary sites of action can be defined with more and more precision until, with sufficient subdivision, they are mapped onto individual kinetic steps. Top-down elasticity analysis has been used to identify the targets of effectors of oxygen consumption in mitochondria, hepatocytes and thymocytes. Effectors include poisons such as cadmium and hormones such as tri-iodothyronine. However, the method is more general than this; in principle it can be applied to any metabolic or other steady-state system.     

Arrhenius curves of hydrogen transfers: tunnel effects, isotope effects and effects of pre-equilibria

In this paper, the Arrhenius curves of selected hydrogen-transfer reactions for which kinetic data are available in a large temperature range are reviewed. The curves are discussed in terms of the one-dimensional Bell-Limbach tunnelling model. The main parameters of this model are the barrier heights of the isotopic reactions, barrier width of the H-reaction, tunnelling masses, pre-exponential factor and minimum energy for tunnelling to occur. The model allows one to compare different reactions in a simple way and prepare the kinetic data for more-dimensional treatments. The first type of reactions is concerned with reactions where the geometries of the reacting molecules are well established and the kinetic data of the isotopic reactions are available in a large temperature range. Here, it is possible to study the relation between kinetic isotope effects (KIEs) and chemical structure. Examples are the tautomerism of porphyrin, the porphyrin anion and related compounds exhibiting intramolecular hydrogen bonds of medium strength. We observe pre-exponential factors of the order of kT/h congruent with 10(13) s-1 corresponding to vanishing activation entropies in terms of transition state theory. This result is important for the second type of reactions discussed in this paper, referring mostly to liquid solutions. Here, the reacting molecular configurations may be involved in equilibria with non- or less-reactive forms. Several cases are discussed, where the less-reactive forms dominate at low or at high temperature, leading to unusual Arrhenius curves. These cases include examples from small molecule solution chemistry like the base-catalysed intramolecular H-transfer in diaryltriazene, 2-(2'-hydroxyphenyl)-benzoxazole, 2-hydroxy-phenoxyl radicals, as well as in the case of an enzymatic system, thermophilic alcohol dehydrogenase. In the latter case, temperature-dependent KIEs are interpreted in terms of a transition between two regimes with different temperature-independent KIEs.     

Translating biochemical network models between different kinetic formats

Mechanistic biochemical network models describe the dynamics of intracellular metabolite pools in terms of substance concentrations, stoichiometry and reaction kinetics. Data from stimulus response experiments are currently the most informative source for in-vivo parameter estimation in such models. However, only a part of the parameters of classical enzyme kinetic models can usually be estimated from typical stimulus response data. For this reason, several alternative kinetic formats using different “languages” (e.g. linear, power laws, linlog, generic and convenience) have been proposed to reduce the model complexity. The present contribution takes a rigorous “multi-lingual” approach to data evaluation by translating biochemical network models from one kinetic format into another. For this purpose, a new high-performance algorithm has been developed and tested. Starting with a given model, it replaces as many kinetic terms as possible by alternative expressions while still reproducing the experimental data. Application of the algorithm to a published model for Escherichia coli's sugar metabolism demonstrates the power of the new method. It is shown that model translation is a powerful tool to investigate the information content of stimulus response data and the predictive power of models. Moreover, the local and global approximation capabilities of the models are elucidated and some pitfalls of traditional single model approaches to data evaluation are revealed.     

Short peptides are not reliable models of thermodynamic and kinetic properties of the N-terminal metal binding site in serum albumin.

A comparative study of thermodynamic and kinetic aspects of Cu(II) and Ni(II) binding at the N-terminal binding site of human and bovine serum albumins (HSA and BSA, respectively) and short peptide analogues was performed using potentiometry and spectroscopic techniques. It was found that while qualitative aspects of interaction (spectra and structures of complexes, order of reactions) could be reproduced, the quantitative parameters (stability and rate constants) could not. The N-terminal site in HSA is much more similar to BSA than to short peptides reproducing the HSA sequence. A very strong influence of phosphate ions on the kinetics of Ni(II) interaction was found. This study demonstrates the limitations of short peptide modelling of Cu(II) and Ni(II) transport by albumins.     

Question 9: Minority Control and Genetic Takeover

As an issue of constructive biology, we study how molecules carrying heredity appear in a reproduction system that consists of mutually catalytic reactions. Molecules that are minority in number are shown to be preserved over generations, and control the behavior of the system. Life-critical information is then expected to be packed into such molecules and transferred over generations, leading to kinetic origin of genetic information. Relevance of this minority control to genetic takeover from loose reproduction is discussed, as a general consequence of any reproducing system with evolvability. Appropriate cell size to achieve both for reproduction and evolvability is also estimated based on this minority control mechanism.     

Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution

A new method for the mathematical analysis of large metabolic networks is presented. Based on the fact that the occurrence of a metabolic reaction generally requires the existence of other reactions providing its substrates, series of metabolic networks are constructed. In each step of the corresponding expansion process those reactions are incorporated whose substrates are made available by the networks of the previous generations. The method is applied to the set of all metabolic reactions included in the KEGG database. Starting with one or more seed compounds, the expansion results in a final network whose compounds define the scope of the seed. Scopes of all metabolic compounds are calculated and it is shown that large parts of cellular metabolism can be considered as the combined scope of simple building blocks. Analyses of various expansion processes reveal crucial metabolites whose incorporation allows for the increase in network complexity. Among these metabolites are common cofactors such as NAD⁺, ATP, and coenzyme A. We demonstrate that the outcome of network expansion is in general very robust against elimination of single or few reactions. There exist, however, crucial reactions whose elimination results in a dramatic reduction of scope sizes. It is hypothesized that the expansion process displays characteristics of the evolution of metabolism such as the temporal order of the emergence of metabolic pathways. [Reviewing Editor : Dr. David Pollock]     

On the Emergence of Biological Complexity: Life as a Kinetic State of Matter

A kinetic model that attempts to further clarify the nature of biological complexification is presented. Its essence: reactions of replicating systems and those of regular chemical systems follow different selection rules leading to different patterns of chemical behavior. For regular chemical systems selection is fundamentally thermodynamic, whereas for replicating chemical systems selection is effectively kinetic. Building on an extension of the kinetic stability concept it is shown that complex replicators tend to be kinetically more stable than simple ones, leading to an on-going process of kinetically-directed complexification. The high kinetic stability of simple replicating assemblies such as phages, compared to the low kinetic stability of the assembly components, illustrates the complexification principle. The analysis suggests that living systems constitute a kinetic state of matter, as opposed to the traditional thermodynamic states that dominate the inanimate world, and reaffirms our view that life is a particular manifestation of replicative chemistry.     

A simple computer program with statistical tests for the analysis of enzyme kinetics.

A simple computer program that calculates the kinetic parameters of enzyme reactions is described. Parameters are determined by nonlinear, least-squares regression using either Marquardt-Levenberg or Gauss-Newton algorithms to find the minimum sum of squares. Three types of enzyme reactions can be analyzed: single substrate reactions (Michaelis-Menten and sigmoidal kinetics), enzyme activation at a fixed substrate value or enzyme inhibition at a fixed substrate value. The user can monitor goodness of fit through nonparametric statistical tests (performed automatically by the computer) and through visual examination of the pattern of residuals. The program is unique in providing equations for activator and inhibition analysis as well as in enabling the user to fix some of the parameters before regression analysis. The simplicity of the program makes it extremely useful for quickly determining kinetic parameters during the data-gathering process.     

Kinetics of biomass catalytic pyrolysis

The Coats–Redfern method was used to analyze the kinetic characteristics of biomass catalytic pyrolysis, indicating that it can be described by multi-step reactions, rather than as a simple first-order reaction. Friedman model-free calculations were used to describe the starting reaction types and the corresponding initial kinetic parameters. Finally, nonlinear regression of biomass catalytic pyrolysis showed that the reaction mechanism of the whole process could be kinetically characterized by three successive reactions: a one-dimensional diffusion reaction, followed by an apparent first-order reaction, and then by a two-dimensional diffusion reaction. The kinetic parameters and equations were also calculated.     

Punctualism, non-adaptationism, neutralism and evolution

In its further development the theory of evolution will incorporate molecular biology, synergetics and the theory of information. Using a simple model it is shown that speciation can be similar to phase transition. This is a thermodynamical statement which does not say anything concerning the sharpness and kinetic features of transition. Hence there is no contradiction between punctuated equilibrium and phyletic gradualism. The notion of punctualism can be used in the sense of phase transition. Evolution is directional because of constraints of natural selection due to the structure of organisms already existing and to the possible pathways of development. Correspondingly many characters are non-adaptative. Not only are the structures of proteins important for speciation but also the exact answers to the questions: "how much", "where" and "when"? These answers can be obtained as the results of regulation of genes, particularly of homeiotic regulation. The basis features of the structure of proteins are considered and the sense of the neutral theory is discussed in connection with degeneracy of correlation between the primary structure of a protein, its spatial structure and biological function. Informational aspects of evolution are discussed. Punctualism, non-adaptationism and neutralism form the triad of internally connected features of evolution. The Darwinian theory preserves its fundamental significance.     

The origin of the RNA world: co-evolution of genes and metabolism

Discoveries demonstrating that RNA can serve genetic, catalytic, structural, and regulatory roles have provided strong support for the existence of an RNA World that preceded the origin of life as we know it. Despite the appeal of this idea, it has been difficult to explain how macromolecular RNAs emerged from small molecules available on the early Earth. We propose here a mechanism by which mutual catalysis in a pre-biotic network initiated a progression of stages characterized by ever larger and more effective catalysts supporting a proto-metabolic network, and the emergence of RNA as the dominant macromolecule due to its ability to both catalyze chemical reactions and to be copied in a template-directed manner. This model suggests that many features of modern life, including the biosynthetic pathways leading to simple metabolites, the structures of organic and metal ion cofactors, homochirality, and template-directed replication of nucleic acids, arose long before the RNA World and were retained as pre-biotic systems became more sophisticated.     

Hierarchical Self-Organization of Non-Cooperating Individuals

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.