Comprehensive classification and perspective for modelling photorespiratory metabolism.

Biological processes involved in photorespiratory and photosynthetic metabolism operate concurrently and affect the interplay between carbon and nitrogen assimilation reflected in plant growth. Experimental evidence has indicated that photorespiratory metabolism has a wide-ranging influence not only on other principal metabolic pathways but also on a multitude of signalling cascades. Therefore, accurate quantitative models of photorespiration can provide a means for predicting and in silico probing of plant behaviour at various levels of the system. We first present a comprehensive classification of current models of photorespiratory metabolism developed within the existing carbon-centric modelling paradigm. We then offer a perspective for modelling photorespiratory metabolism by considering the coupling of carbon and nitrogen metabolism in the context of compartmentalised, genome-scale metabolic models of C3 plants. In addition, we outline the challenges stemming from the need to consider plant metabolic and signalling pathways in assessing the still controversial role of photorespiration and to confront the devised models with the ever-increasing amounts of high-throughput data.

Dimensionality reduction of bistable biological systems.

Time hierarchies, arising as a result of interactions between system's components, represent a ubiquitous property of dynamical biological systems. In addition, biological systems have been attributed switch-like properties modulating the response to various stimuli across different organisms and environmental conditions. Therefore, establishing the interplay between these features of system dynamics renders itself a challenging question of practical interest in biology. Existing methods are suitable for systems with one stable steady state employed as a well-defined reference. In such systems, the characterization of the time hierarchies has already been used for determining the components that contribute to the dynamics of biological systems. However, the application of these methods to bistable nonlinear systems is impeded due to their inherent dependence on the reference state, which in this case is no longer unique. Here, we extend the applicability of the reference-state analysis by proposing, analyzing, and applying a novel method, which allows investigation of the time hierarchies in systems exhibiting bistability. The proposed method is in turn used in identifying the components, other than reactions, which determine the systemic dynamical properties. We demonstrate that in biological systems of varying levels of complexity and spanning different biological levels, the method can be effectively employed for model simplification while ensuring preservation of qualitative dynamical properties (i.e., bistability). Finally, by establishing a connection between techniques from nonlinear dynamics and multivariate statistics, the proposed approach provides the basis for extending reference-based analysis to bistable systems.

Perspectives on plant photorespiratory metabolism.

Being intimately intertwined with (C3) photosynthesis, photorespiration is an incredibly high flux-bearing pathway. Traditionally, the photorespiratory cycle was viewed as closed pathway to refill the Calvin-Benson cycle with organic carbon. However, given the network nature of metabolism, it hence follows that photorespiration will interact with many other pathways. In this article, we review current understanding of these interactions and attempt to define key priorities for future research, which will allow us greater fundamental comprehension of general metabolic and developmental consequences of perturbation of this crucial metabolic process.

Inner composition alignment for inferring directed networks from short time series.

Identifying causal links (couplings) is a fundamental problem that facilitates the understanding of emerging structures in complex networks. We propose and analyze inner composition alignment-a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series, currently applied to gene expression. The measure can be used to infer the direction of couplings, detect indirect (superfluous) links, and account for autoregulation. Applications to the gene regulatory network of E. coli are presented.