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Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

Studying the gene regulatory networks (GRNs) that govern how cells change into specific cell types with unique roles throughout development is an active area of experimental research. The fate specification process can be viewed as a biological program prescribing the system dynamics, governed by a network of genetic interactions. To investigate the possibility that GRNs are not fixed but rather change their topology, for example as cells progress through commitment, we introduce the concept of Switching Gene Regulatory Networks (SGRNs) to enable the modelling and analysis of network reconfiguration. We define the synthesis problem of constructing SGRNs that are guaranteed to satisfy a set of constraints representing experimental observations of cell behaviour. We propose a solution to this problem that employs methods based upon Satisfiability Modulo Theories (SMT) solvers, and evaluate the feasibility and scalability of our approach by considering a set of synthetic benchmarks exhibiting possible biological behaviour of cell development. We outline how our approach is applied to a more realistic biological system, by considering a simplified network involved in the processes of neuron maturation and fate specification in the mammalian cortex., (Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)

In previous studies it was shown that Pichia guilliermondii strain Z1, isolated from healthy Moroccan citrus Valencia-Late oranges, was effective against Penicillium italicum. Here the effectiveness of strain Z1 was assessed against Penicillium digitatum , the causal agent of green mould, under different temperature (5-25°C) and relative humidity (RH) (45-100%) regimes for its reliable and large-scale application in packinghouse. All main effects and interactions were significant (P<0.0001). In the pathogen control, the largest lesion diameter was at an RH range between 98 and 100%, regardless of the incubation temperature. The efficacy of strain Z1 was not dependent on the environment and reduced disease incidence by >80%. Its applications as a formulated product significantly reduced the incidence of infected fruit (55%) and the percentage of infected wounds (47%) compared to the only pathogen control treatment. However, disease control with formulated product was significantly less than that obtained with thiabendazole (30%) or strain Z1 culturable cells (35%). These results highlight that strain Z1 is an effective biological control agent for control of green mould under varying environmental conditions, and control may be optimized by combining its use with other environmentally-safe post-harvest treatments or improved formulation.