The hallmarks of cancer.

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

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Initial sequencing and analysis of the human genome.

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.

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Statistical mechanics of complex networks

A genetic model for colorectal tumorigenesis

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

Complex networks: Structure and dynamics

p53 is a cellular-encoded phosphoprotein first identified in protein complexes with the large tumor (T) antigen of simian virus 40 (SV40) (Linzer and Levine, 1979; Lane and Crawford, 1979). High levels of p53 protein have been detected in both embryonal carcinoma cells and chemically induced transformed cells using antisera from animals with SV40-induced tumors or immunized with these tumorigenic lines (Linzer and Levine, 1979; DeLeo et al., 1979). In addition, humans with cancer have been shown to have circulating anti-p53 antibodies (Crawford et al., 1982; Caron de Fromental et al., 1987). Thus, p53 was termed a tumor antigen.

A Comparison of the Properties of Human P53 Mutant Alleles

The immense amount of sequence data available from expressed sequence tag (EST) databases (1,2), together with the development of technologies for the highly parallel analysis of gene expression (3–5) have created the opportunity to interrogate biochemical pathways and gene function on an unprecedented scale. We describe here a set of high-density DNA arrays containing oligonucleotides complementary to more than 6,500 human EST’s. These arrays were used to generate normal and breast cancer specific gene expression profiles. More than 1,500 expressed genes were detected in both cell types examined with 85% of all gene expression observed in the range of 1–50 copies per cell. Over 300 genes demonstrated significantly different levels of expression between normal and transformed breast cells. Increased mRNA levels were observed for the Her2/neu oncogene and genes involved in its signal transduction pathway, including Grb-7, Ras, Raf, Mek and ERK. In addition, a simple categorization of the expression changes revealed patterns characteristic of loss of wild-type p53 function. Genotyping of the p53 locus using a DNA re-sequencing array revealed inactivating mutations in the p53 DNA binding domain and loss of heterozygosity. These data demonstrate a general array-hybridization-based approach to deciphering biochemical pathways and generating testable hypotheses concerning the mechanisms of cell growth and differentiation.

Deciphering Molecular Circuitry Using High-Density DNA Arrays

The Future of Systems Biology

The relative importance of viral tumor antigen expression and the cellular background in the maintenance of a transformation phenotype was examined in five SV40-transformed teratocarcinoma-derived cell lines. These cell lines show qualitative differences in growth characteristics associated with transformation, and vary in their state of differentiation. Viral T antigen expression was evaluated by two criteria: 1) the amount of immunoprecipitated antigen in growing cells, and 2) the amount and rate of antigen synthesis in density-inhibited cells. There was no direct correlation found between retention, or rate of synthesis, of the viral T antigen and the degree of transformation. These findings imply that the cellular environment has a more important influence on the growth properties of a stably transformed cell than the quantitative levels of viral T antigen expression.

Expression of SV40 tumor antigen in SV40 transformed teratocarcinoma-derived cell lines

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Arnold J. Levine

Papers

A Comparison of the Properties of Human P53 Mutant Alleles

Deciphering Molecular Circuitry Using High-Density DNA Arrays

The Future of Systems Biology

Expression of SV40 tumor antigen in SV40 transformed teratocarcinoma-derived cell lines

Normal and Neoplastic Growth and Development: AACR Special Conference in Cancer Research