Computing chemical organizations in biological networks

Centler, Florian; Kaleta, Christoph; Fenizio, Pietro Speroni di; Dittrich, Peter

doi:10.1093/bioinformatics/btn228

Cited by 36 publications

(38 citation statements)

References 16 publications

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“…Several advances can be made applying lattice theoretical notions [54] to the hierarchy of organizations [14], and more profound studies in the topology of reaction networks could provide a more rich structure that the one presented in this paper. Such a richer structure could be used not only to better understand how systems emerge and combine, but also for improving algorithms regarding the computation of organizations [41]. In this vein, we present some advances in this issue [36].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, in [39], Theorem 1 is extended to other stable asymptotic behaviors such as periodic orbits and limit cycles. In addition to these results, necessary conditions for the existence of adequate flux vectors are explored in [40], and algorithmic studies concerning the computation of the organizations of a reaction network are presented in [41][42][43].…”

Section: Connecting the Description Levels: Chemical Organization Theorymentioning

confidence: 99%

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Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Veloz

Razeto‐Barry

2017

Systems

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Abstract:The basic processes that bring about living systems are conventionally represented in the framework of chemical reaction networks. Recently, it has been proposed that this framework can be exploited for studying various other phenomena. Reaction networks are specially suited for representing situations where different types of entities interact in contextual ways leading to the emergence of meta-structures. At an abstract level, a reaction network represents a universe whose evolution corresponds to the transformation of collections of entities into other collections of entities. Hence, we propose that systems correspond to the sub-networks that are stable enough to be observed. In this article, we discuss how to use reaction networks for representing systems. Namely, we introduce the different representational levels available (relational, stoichiometric, and kinetic), we show how to identify observable systems in the reaction network, discuss some relevant systemic notions such as context, emergence, and meta-system, and present some examples.

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Section: Discussionmentioning

confidence: 99%

Section: Connecting the Description Levels: Chemical Organization Theorymentioning

confidence: 99%

Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Veloz

Razeto‐Barry

2017

Systems

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“…[76,77]), but not mathematically. Methods to address the kind of structures that appear in biological and biochemical systems, and artificial systems with similar characteristics, include those of Dittrich and co-workers [78][79][80][81][82][83], who introduced a general theory of chemical organization, while Paun and colleagues [84][85][86]; developed a theory of membrane computing; in addition, there are attempts to apply Milner's π -calculus [87] to systems biology [88][89][90]. Other useful methods in biological and adaptive complex systems include Petri net modelling [1,40,41] with hierarchical extensions [5], multi-valued logical modelling (generalizing Kauffman's Boolean network approach [4]) combined with constraint analysis [1,3,6,91,92].…”

Section: Interaction Machines (A) Dynamically Growing and Changing Aumentioning

confidence: 99%

Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions

Nehaniv

Rhodes

Egri-Nagy

et al. 2015

Phil. Trans. R. Soc. A.

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Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.In the 21st century, algebra will be as important for biology as chemistry was in the 20th century.-Günter P. Wagner

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“…Related models are based on a wide variety of different computational paradigms from strings and matrices to Turing machines and graphs [8][9][10][11][12][13][14], see also the reviews [15,16]. The abstract computational models are very useful for understanding algebraic properties of reaction systems; the notion of a selfmaintaining set and the development of a theory of Chemical Organizations [17] emphasizes the success of such approaches.…”

Section: The Chemical Model Universementioning

confidence: 99%

Evolution of metabolic networks: a computational frame-work

et al. 2010

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Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can be selected for. Results: We describe here a simulation environment that implements all these components in a simplified ways so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.

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Computing chemical organizations in biological networks

Abstract: All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

Cited by 36 publications

References 16 publications

Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions

Evolution of metabolic networks: a computational frame-work

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