Pathway databases and tools for their exploitation: benefits, current limitations and challenges

In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

JNK controls the onset of mitosis of planarian stem cells and triggers apoptotic cell death required for regeneration and remodeling

Regeneration of lost tissues depends on the precise interpretation of molecular signals that control and coordinate the onset of proliferation, cellular differentiation and cell death. However, the nature of those molecular signals and the mechanisms that integrate the cellular responses remain largely unknown. The planarian flatworm is a unique model in which regeneration and tissue renewal can be comprehensively studied in vivo. The presence of a population of adult pluripotent stem cells combined with the ability to decode signaling after wounding enable planarians to regenerate a complete, correctly proportioned animal within a few days after any kind of amputation, and to adapt their size to nutritional changes without compromising functionality. Here, we demonstrate that the stress-activated c-jun-NH2-kinase (JNK) links wound-induced apoptosis to the stem cell response during planarian regeneration. We show that JNK modulates the expression of wound-related genes, triggers apoptosis and attenuates the onset of mitosis in stem cells specifically after tissue loss. Furthermore, in pre-existing body regions, JNK activity is required to establish a positive balance between cell death and stem cell proliferation to enable tissue renewal, remodeling and the maintenance of proportionality. During homeostatic degrowth, JNK RNAi blocks apoptosis, resulting in impaired organ remodeling and rescaling. Our findings indicate that JNK-dependent apoptotic cell death is crucial to coordinate tissue renewal and remodeling required to regenerate and to maintain a correctly proportioned animal. Hence, JNK might act as a hub, translating wound signals into apoptotic cell death, controlled stem cell proliferation and differentiation, all of which are required to coordinate regeneration and tissue renewal.