Generalized Mayer-Vietoris sequences in algebraic K-theory

AbstractLong exact sequences of algebraic K-groups for certain kinds of multiple pullback rings are constructed, with special emphasis on Dedekind-like rings. In the case where excision holds they reduce to the usual Mayer-Vietoris sequences. These sequences are then used to obtain information about the K-groups of integral group rings of abelian groups of square-free order; about certain rings of integers in number fields; and about the coordinate ring of n lines in the plane

A Systems Biology Approach to Iron Metabolism

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying cause

A mathematical model of combined CD8 T cell costimulation by 4-1BB (CD137) and OX40 (CD134) receptors.

Combined agonist stimulation of the TNFR costimulatory receptors 4-1BB (CD137) and OX40(CD134) has been shown to generate supereffector CD8 T cells that clonally expand to greater levels, survive longer, and produce a greater quantity of cytokines compared to T cells stimulated with an agonist of either costimulatory receptor individually. In order to understand the mechanisms for this effect, we have created a mathematical model for the activation of the CD8 T cell intracellular signaling network by mono- or dual-costimulation. We show that supereffector status is generated via downstream interacting pathways that are activated upon engagement of both receptors, and in silico simulations of the model are supported by published experimental results. The model can thus be used to identify critical molecular targets of T cell dual-costimulation in the context of cancer immunotherapy

A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.

Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC

Modeling Stochasticity and Variability in Gene Regulatory Networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This paper contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.Comment: 23 pages, 8 figure