Deterministic Scale-Free Networks

Scale-free networks are abundant in nature and society, describing such diverse systems as the world wide web, the web of human sexual contacts, or the chemical network of a cell. All models used to generate a scale-free topology are stochastic, that is they create networks in which the nodes appear to be randomly connected to each other. Here we propose a simple model that generates scale-free networks in a deterministic fashion. We solve exactly the model, showing that the tail of the degree distribution follows a power law

Spatial stochastic resonance in 1D Ising systems

The 1D Ising model is analytically studied in a spatially periodic and oscillatory external magnetic field using the transfer-matrix method. For low enough magnetic field intensities the correlation between the external magnetic field and the response in magnetization presents a maximum for a given temperature. The phenomenon can be interpreted as a resonance phenomenon induced by the stochastic heatbath. This novel "spatial stochastic resonance" has a different origin from the classical stochastic resonance phenomenon.Comment: REVTex, 5 pages, 3 figure

AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry–based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our findings show how prostate tumors undergo a metabolic reprogramming that reflects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.Fil: Priolo, Carmen. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Pyne, Saumyadipta. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Rose, Joshua. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Regan, Erzsébet Ravasz. Harvard Medical School; Estados UnidosFil: Zadra, Giorgia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Photopoulos, Cornelia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Cacciatore, Stefano. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Schultz, Denise. Johns Hopkins University; Estados UnidosFil: Scaglia, Natalia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: McDunn, Jonathan. Metabolon Inc.; Estados UnidosFil: de Marzo, Angelo M.. Johns Hopkins University; Estados UnidosFil: Loda, Massimo. Department of Pathology. Brigham and Women's Hospital; Estados Unidos. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados Unidos. University of Cambridge; Estados Unidos. King's College London. Division of Cancer Studies; Estados Unido

Hierarchical Organization in Complex Networks

Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the World Wide Web, actor network, the Internet at the domain level and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems

Principles of dynamical modularity in biological regulatory networks

Intractable diseases such as cancer are associated with breakdown in multiple individual functions, which conspire to create unhealthy phenotype-combinations. An important challenge is to decipher how these functions are coordinated in health and disease. We approach this by drawing on dynamical systems theory. We posit that distinct phenotype-combinations are generated by interactions among robust regulatory switches, each in control of a discrete set of phenotypic outcomes. First, we demonstrate the advantage of characterizing multi-switch regulatory systems in terms of their constituent switches by building a multiswitch cell cycle model which points to novel, testable interactions critical for early G2/M commitment to division. Second, we define quantitative measures of dynamical modularity, namely that global cell states are discrete combinations of switch-level phenotypes. Finally, we formulate three general principles that govern the way coupled switches coordinate their function

Do endothelial cells dream of eclectic shape?

Endothelial cells (ECs) exhibit dramatic plasticity of form at the single- and collective-cell level during new vessel growth, adult vascular homeostasis, and pathology. Understanding how, when, and why individual ECs coordinate decisions to change shape, in relation to the myriad of dynamic environmental signals, is key to understanding normal and pathological blood vessel behavior. However, this is a complex spatial and temporal problem. In this review we show that the multidisciplinary field of Adaptive Systems offers a refreshing perspective, common biological language, and straightforward toolkit that cell biologists can use to untangle the complexity of dynamic, morphogenetic systems

Boolean model of growth signaling, cell cycle and apoptosis predicts the molecular mechanism of aberrant cell cycle progression driven by hyperactive PI3K.

The PI3K/AKT signaling pathway plays a role in most cellular functions linked to cancer progression, including cell growth, proliferation, cell survival, tissue invasion and angiogenesis. It is generally recognized that hyperactive PI3K/AKT1 are oncogenic due to their boost to cell survival, cell cycle entry and growth-promoting metabolism. That said, the dynamics of PI3K and AKT1 during cell cycle progression are highly nonlinear. In addition to negative feedback that curtails their activity, protein expression of PI3K subunits has been shown to oscillate in dividing cells. The low-PI3K/low-AKT1 phase of these oscillations is required for cytokinesis, indicating that oncogenic PI3K may directly contribute to genome duplication. To explore this, we construct a Boolean model of growth factor signaling that can reproduce PI3K oscillations and link them to cell cycle progression and apoptosis. The resulting modular model reproduces hyperactive PI3K-driven cytokinesis failure and genome duplication and predicts the molecular drivers responsible for these failures by linking hyperactive PI3K to mis-regulation of Polo-like kinase 1 (Plk1) expression late in G2. To do this, our model captures the role of Plk1 in cell cycle progression and accurately reproduces multiple effects of its loss: G2 arrest, mitotic catastrophe, chromosome mis-segregation / aneuploidy due to premature anaphase, and cytokinesis failure leading to genome duplication, depending on the timing of Plk1 inhibition along the cell cycle. Finally, we offer testable predictions on the molecular drivers of PI3K oscillations, the timing of these oscillations with respect to division, and the role of altered Plk1 and FoxO activity in genome-level defects caused by hyperactive PI3K. Our model is an important starting point for the predictive modeling of cell fate decisions that include AKT1-driven senescence, as well as the non-intuitive effects of drugs that interfere with mitosis