Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot

Inferring the function of genes from synthetic lethal mutations

Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that uses synthetic lethal mutations for mapping genes of unknown function to enzymes in a known metabolic network. We show how such mappings can be automatically computed by a logical learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL)

Age, sex, adult and larval diet shape starvation resistance in the Mediterranean fruit fly: an ecological and gerontological perspective.

The ability of an animal to withstand periods of food deprivation is a key driver of invasion success (biodiversity), adaptation to new conditions, and a crucial determinant of senescence in populations. Starvation resistance (SR) is a highly plastic trait and varies in relation to environmental and genetic variables. However, beyond Drosophila, SR has been studied poorly. Exploiting an interesting model species in invasion and ageing studies-the Mediterranean fruit fly (Ceratitis capitata)- we investigated how age, food and gender, shape SR in this species. We measured SR in adults feeding in rich and poor dietary conditions, which had been reared either on natural hosts or artificial larval diet, for every single day across their lifespan. We defined which factor is the most significant determinant of SR and we explored potential links between SR and ageing. We found that SR declines with age, and that age-specific patterns are shaped in relation to adult and larval diet. Females exhibited higher SR than males. Age and adult diet were the most significant determinants of SR, followed by gender and the larval diet. Starvation resistance proved to be a weak predictor of functional ageing. Possible underlying mechanisms, ecological and gerontological significance and potential applied benefits are discussed

The silicon trypanosome

African trypanosomes have emerged as promising unicellular model organisms for the next generation of systems biology. They offer unique advantages, due to their relative simplicity, the availability of all standard genomics techniques and a long history of quantitative research. Reproducible cultivation methods exist for morphologically and physiologically distinct life-cycle stages. The genome has been sequenced, and microarrays, RNA-interference and high-accuracy metabolomics are available. Furthermore, the availability of extensive kinetic data on all glycolytic enzymes has led to the early development of a complete, experiment-based dynamic model of an important biochemical pathway. Here we describe the achievements of trypanosome systems biology so far and outline the necessary steps towards the ambitious aim of creating a , a comprehensive, experiment-based, multi-scale mathematical model of trypanosome physiology. We expect that, in the long run, the quantitative modelling enabled by the Silicon Trypanosome will play a key role in selecting the most suitable targets for developing new anti-parasite drugs

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table

Towards a comprehensive modeling framework for studying glucose repression in yeast

The yeast Saccharomyces cerevisiae is an important model organism for human health and for industry applications as a cell factory. For both purposes, it has been an important organism for studying glucose repression. Glucose sensing and signaling is a complex biological system, where the SNF1 pathway is the main pathway responsible for glucose repression. However, it is highly interconnected with the cAMP-PKA, Snf3-Rgt2 and TOR pathways. To handle the complexity, mathematical modeling has successfully aided in elucidating the structure, mechanism, and dynamics of the pathway. In this thesis, I aim to elucidate what the effect of the interconnection of glucose repression with sensory and metabolic pathways in yeast is, specifically, how crosstalk influences the signaling cascade; what the main effects of nutrient signaling on the metabolism are and how those are affected by intrinsic stress, such as damage accumulation. Here, I have addressed these questions by developing new frameworks for mathematical modeling. A vector based method for Boolean representation of complex signaling events is presented. The method reduces the amount of necessary nodes and eases the interpretation of the Boolean states by separating different events that could alter the activity of a protein. This method was used to study how crosstalk influences the signaling cascade.To be able to represent a diverse biological network using methods suitable for respective pathways, we also developed two hybrid models. The first is demonstrating a framework to connect signaling pathways with metabolic networks, enabling the study of long-term signaling effects on the metabolism. The second hybrid model is demonstrating a framework to connect models of signaling and metabolism to growth and damage accumulation, enabling the study of how the long-term signaling effects on the metabolism influence the lifespan. This thesis represents a step towards comprehensive models of glucose repression. In addition, the methods and frameworks in this thesis can be applied and extended to other signaling pathways

Using a logical model to predict the growth of yeast

<p>Abstract</p> <p>Background</p> <p>A logical model of the known metabolic processes in <it>S. cerevisiae </it>was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.</p> <p>Results</p> <p>Compared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings.</p> <p>Conclusion</p> <p>ROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750.</p

Heat Stress and feeding strategies in meat-type chickens

Heat stress can induce hyperthermia in poultry. A reduction in heat load can be achieved by increasing the possibilities for dissipation, decreasing the level of heat production or by changing the thermal production pattern within a day. Strategies to reduce the negative effects of heat stress can be based on a specific feeding strategy, such as restricted feeding. Feed that is offered long enough before a hot period can ameliorate the harmful effects of high temperature. Another strategy may be to use choice feeding from different feed ingredients, rich in protein or in energy. With such self-selection, the chicken may adjust its intake of individual components, allowing the bird to optimise the heat load associated with the metabolism of the ingested nutrients. Additional promising strategies involve offering a choice between feeds with a different feed particle size or structure. A large particle size contributes to the development of the gastro-intestinal tract (GIT), especially the gizzard and the caeca. A large gizzard will maximize the grinding process and potentially ease digestion down the GIT, thereby reducing heat production associated with digestive processing. Also wet feeding may be profitable under heat stress conditions as well. Feeding wet diets may facilitate an increased water intake and larger particle sizes can limit water excretion in droppings, resulting in more water being available for evaporation during panting, hence cooling the bird. In conclusion, these feeding strategies may help to reduce heat production peaks, facilitate evaporative activity and/or decreases the heat load, resulting in beneficial effects on performance and health of the bird kept in more tropical areas worldwide