抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

/pdf/statistical-mechanics-of-complex-networks-1zfjpvxipu.pdf

Statistical mechanics of complex networks

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

/pdf/the-structure-and-function-of-complex-networks-34deompc9l.pdf

The Structure and Function of Complex Networks

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

http://brainimaging.waisman.wisc.edu/~chung/neuro.processing/literature/rubinov.2010.NI.network.pdf

Complex network measures of brain connectivity: uses and interpretations.

Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time-consuming. As a consequence of manual data processing, the results are often not analysed in full depth, so potential novel findings might get lost. To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose, it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to the number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny application that can be run online in a web browser on 
https://mpietzke.shinyapps.io/autoplotter

. We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis. With the help of Metabolite AutoPlotter, it is now possible to simplify data processing and visualisation for a wide audience. High-quality plots from complex data can be generated in a short time by pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further, this eliminates potential copy-and-paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

/pdf/metabolite-autoplotter-an-application-to-process-and-tfc9tf8mbe.pdf

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web browser.

Cancer cells exhibit high rates of aerobic glycolysis and glutaminolysis. Aerobic glycolysis can provide energy and glutaminolysis can provide carbon for anaplerosis and reductive carboxylation to citrate. However, all these metabolic requirements could be in principle satisfied from glucose. Energy can be generated from oxidative phosphorylation (OxPhos) of glucose, anaplerosis can be accomplished using pyruvate carboxylate and citrate can be derived from glucose. Here we investigate why cancer cells do not satisfy their metabolic demands using aerobic biosynthesis from glucose. Based on the typical composition of a mammalian cell we quantify the energy demand and the OxPhos burden of cell biosynthesis from glucose. Our calculation demonstrates that aerobic growth from glucose is feasible up to a minimum doubling time that is proportional to the OxPhos burden and inversely proportional to the mitochondria OxPhos capacity. To grow faster cancer cells must activate aerobic glycolysis for energy generation and uncouple NADH generation from biosynthesis. To uncouple biosynthesis from NADH generation cancer cells can synthesize lipids from carbon sources that do not produce NADH in their catabolism, including acetate and the amino acids glutamate, glutamine, phenylalanine and tyrosine. Finally, we show that cancer cell lines commonly used in cancer research have an OxPhos capacity that is insufficient to support aerobic biosynthesis from glucose. We conclude that selection for high rate of biosynthesis implies a selection for aerobic glycolysis and uncoupling biosynthesis from NADH generation. Any defect or perturbation reducing the OxPhos capacity will exacerbate this selection.

/pdf/limits-of-aerobic-metabolism-in-cancer-cells-rlmej36lex.pdf

Limits of aerobic metabolism in cancer cells

Aldehyde dehydrogenase class 3, encoded by ADH5 in humans, catalyzes the glutathione dependent detoxification of formaldehyde. Here we show that ADH5 deficient cells turn over formaldehyde using alternative pathways starting from the reaction of formaldehyde with free amino acids. When mammalian cells are exposed to formaldehyde, the levels of the reaction products of formaldehyde with the amino acids cysteine and histidine - timonacic and spinacine - are increased. These reactions take place spontaneously and the formation of timonacic is reversible. The levels of timonacic are higher in the plasma of Adh5−/− mice relative to controls and they are further increased upon administration of methanol. We conclude that mammals possess pathways of cysteine and histidine dependent formaldehyde metabolism and that timonacic is a formaldehyde reservoir. Formaldehyde is known to react with nucleophilic amino acids in solution but its in vivo nonenzymatic reactivity is poorly understood. Here LC/MS and isotopic labeling studies suggest nonenzymatic formaldehyde metabolism may occur in living mice through the direct reactivity of formaldehyde with amino acids, including a possible role of timonacic, a product of formaldehyde and cysteine, as a reservoir.

/pdf/amino-acid-dependent-formaldehyde-metabolism-in-mammals-53xjg1l0ba.pdf

Amino acid dependent formaldehyde metabolism in mammals

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Recent empirical evidence indicates that the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. In this work we analyze how this bursty dynamics impacts the dynamics of spreading processes in computer and social systems. We demonstrate that the non-Poisson nature of the contact dynamics results in prevalence decay times significantly larger than predicted by the standard Poisson process based models. Thanks to this slow dynamics the spreading entity, namely a virus, rumor, etc., can persist in the system for long times.

Spreading Dynamics Following Bursty Activity Patterns

Mapping the Internet generally consists in sampling the network from a limited set of sources by using traceroute-like probes. This methodology has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. Here we explore these biases and provide a statistical analysis of their origin. We derive a mean-field analytical approximation for the probability of edge and vertex detection that allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular we show that shortest path routed sampling allows a clear characterization of underlying graphs with scale-free topology. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in different network models.

Alexei Vazquez

Papers

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web browser.

Limits of aerobic metabolism in cancer cells

Amino acid dependent formaldehyde metabolism in mammals

Spreading Dynamics Following Bursty Activity Patterns

Traceroute-like exploration of unknown networks: a statistical analysis