Finding the "Dark Matter'' in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter'' of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Biología de Sistemas... ¿qué biología de sistemas?

In December 2010, I had the honor and satisfaction of participating as an invited speaker at the international Scientific Meeting ‘Systems Biology: Bridging the Gaps between Disciplines’ organized in Barcelona by the Spanish Network of Systems Biology (REBS). Specifically, I was commissioned to give a lecture following the one given by Dr. Adriano Henney in a session dedicated to Personalized Medicine.En diciembre de 2010 tuve el honor y la satisfacción de participar como ponente invitado en la Reunión Científica internacional Systems Biology: Bridging the Gaps between Disciplines organizada en Barcelona por la Red Española de Biología de Sistemas (REBS). En concreto, se me comisionó impartir una ponencia a continuación de la impartida por el Dr. Adriano Henney dentro de una sesión dedicada a Medicina Personalizada.&nbsp

Uncovering the Molecular Machinery of the Human Spindle—An Integration of Wet and Dry Systems Biology

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called “hidden spindle hub”, proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system

Analysis of Genetic Variation in Humans and Other Species

Recent advances in sequencing technologies have led to the generation of vast amounts genetic variation data for many species, including humans, with advances in our understanding of disease now limited by the speed at which these data can be analysed. This thesis focuses on the analysis of genetic variation at multiple levels. First, in the human disease cystinuria, an inherited form of kidney stones. Genetic variants previously associated with cystinuria were characterised using a series of computational methods, identifying key functional features of these mutations. Predictions of disease severity for a cohort of 74 cystinuria patients were then made based on the genotypes of each individual. When compared to clinical outcomes, these predictions demonstrate the potential for computational methods in delivering precision medicine to cystinuria patients. Second, a genome-wide analysis of variant combinations in individual human genomes identified combinations of variants protein-wide, within close spatial proximity in the 3-dimensional structures of proteins, and in protein-protein interface sites. The vast majority of computational methods for analysing genetic variation consider only one variant at a time. This work highlights the importance of analysing the combined effects of variants, which will be a key challenge in the future of computational biology and precision medicine. Finally, two different analyses of ebolaviruses were performed. The first study focused on human pathogenicity of ebolaviruses, a critical challenge in epidemiology. This study identified a set of key variants that differentiate human pathogenic and non-pathogenic ebolaviruses. The second study focused on the evolution of the Ebola virus genome, the most common causative species of human ebolavirus outbreaks. Ebola virus genome evolution was analysed over time since its identification in 1976, and over the course of the 2013-2016 West Africa outbreak. A strong bias for transition mutations was identified, with suspected mutational pressure from host APOBEC and ADAR enzymes