Macrophage inhibitory cytokine-1 (MIC-1/GDF15) slows cancer development but increases metastases in TRAMP prostate cancer prone mice

Macrophage inhibitory cytokine-1 (MIC-1/GDF15), a divergent member of the TGF-β superfamily, is over-expressed by many common cancers including those of the prostate (PCa) and its expression is linked to cancer outcome. We have evaluated the effect of MIC-1/GDF15 overexpression on PCa development and spread in the TRAMP transgenic model of spontaneous prostate cancer. TRAMP mice were crossed with MIC-1/GDF15 overexpressing mice (MIC-1fms) to produce syngeneic TRAMPfmsmic-1 mice. Survival rate, prostate tumor size, histopathological grades and extent of distant organ metastases were compared. Metastasis of TC1-T5, an androgen independent TRAMP cell line that lacks MIC-1/GDF15 expression, was compared by injecting intravenously into MIC-1fms and syngeneic C57BL/6 mice. Whilst TRAMPfmsmic-1 survived on average 7.4 weeks longer, had significantly smaller genitourinary (GU) tumors and lower PCa histopathological grades than TRAMP mice, more of these mice developed distant organ metastases. Additionally, a higher number of TC1-T5 lung tumor colonies were observed in MIC-1fms mice than syngeneic WT C57BL/6 mice. Our studies strongly suggest that MIC-1/GDF15 has complex actions on tumor behavior: it limits local tumor growth but may with advancing disease, promote metastases. As MIC-1/GDF15 is induced by all cancer treatments and metastasis is the major cause of cancer treatment failure and cancer deaths, these results, if applicable to humans, may have a direct impact on patient care.

Paradoxical roles of tumour necrosis factor-alpha in prostate cancer biology

Tumour necrosis factor (TNF) is a pleiotropic cytokine with dual roles in cancer biology including prostate cancer (PCa). On the one hand, there is evidence that it stimulates tumour angiogenesis, is involved in the initiation of PCa from an androgen-dependent to a castrate resistant state, plays a role in epithelial to mesenchymal plasiticity, and may contribute to the aberrant regulation of eicosanoid pathways. On the other hand, TNF has also been reported to inhibit neovascularisation, induce apoptosis of PCa cells, and stimulate anti-tumour immunity. Much of the confusion surrounding its seemingly paradoxical roles in cancer biology stems from the dependence of its effects on the biological model within which TNF is investigated. This review will address some of these issues, and also discuss on the therapeutic implications.

Lineage-specific biomarkers predict response to FGFR inhibition

In this issue of Cancer Discovery, Guagnano and colleagues use a large and diverse annotated collection of cancer cell lines, the Cancer Cell Line Encyclopedia, to correlate whole-genome expression and genomic alteration datasets with cell line sensitivity data to the novel pan-fibroblast growth factor receptor (FGFR) inhibitor NVP-BGJ398. Their findings underscore not only the preclinical use of such cell line panels in identifying predictive biomarkers, but also the emergence of the FGFRs as valid therapeutic targets, across an increasingly broad range of malignancies.

Intercultural competence in engineering education : who are we teaching?

BACKGROUND There is little doubt that our engineering graduates’ ability to identify cultural differences and their potential to impact on engineering projects, and to work effectively with these differences is of key importance in the modern engineering practice. Within engineering degree programs themselves there is also a significant need to recognise the impact of changing student and staff profiles on what happens in the classroom. The research described in this paper forms part of a larger project exploring issues of intercultural competence in engineering. PURPOSE This paper presents an observational and survey study of undergraduate and postgraduate engineering students from four institutions working in groups on tasks with a purely technical focus, or with a cultural and humanitarian element. The study sought to explore how students rate their own intercultural competence and team process and whether any differences exist depending on the nature of the task they are working on. We also investigated whether any differences were evident between groups of first year, second year and postgraduate students. DESIGN/METHOD The study used the miniCQS instrument (Ang & Van Dyne, 2008) and a Bales Interaction Process Analysis based scale (Bales, 1950; Carney, 1976) to collect students self ratings of group process, task management, and cultural experience and behaviour. The Bales IPA was also used for coding video observations of students working in groups. Survey data were used to form descriptive variables to compare outcomes across the different tasks and contexts. Observations analysed in Nvivo were used to provide commentary and additional detail on the quantitative data. RESULTS The results of the survey indicated consistent mean scores on each survey item for each group of students, despite vastly different tasks, student backgrounds and educational contexts. Some small, statistically significant mean differences existed, offering some basic insights into how task and student group composition could affect self ratings. Overall though, the results suggest minimal shift in how students view group function and their intercultural experience, irrespective of differing educational experience. CONCLUSIONS The survey results, contrasted with group observations, indicate that either students are not translating their experience (in the group tasks) into critical self assessment of their cultural competence and teamwork, or that they become more critical of team performance and cultural competence as their competence in these areas grows, so their ratings remain consistent. Both outcomes indicate that students need more intensive guidance to build their critical self and peer assessment skills in these areas irrespective of their year level of study.

Peripheral compartment innate immune response to Haemophilus influenzae and Streptococcus pneumoniae in chronic obstructive pulmonary disease patients.

Alterations in innate immunity that predispose to chronic obstructive pulmonary disease (COPD) exacerbations are poorly understood. We examined innate immunity gene expression in peripheral blood polymorphonuclear leukocytes (PMN) and monocytes stimulated by Haemophilus influenzae and Streptococcus pneumoniae. Thirty COPD patients (15 rapid and 15 non-rapid lung function decliners) and 15 smokers without COPD were studied. Protein expression of IL-8, IL-6, TNF-α and IFN-γ (especially monocytes) increased with bacterial challenge. In monocytes stimulated with S. pneumoniae, TNF-α protein expression was higher in COPD (non-rapid decliners) than in smokers. In co-cultures of monocytes and PMN, mRNA expression of TGF-β1 and MYD88 was up-regulated, and CD14, TLR2 and IFN-γ down-regulated with H. influenzae challenge. TNF-α mRNA expression was increased with H. influenzae challenge in COPD. Cytokine responses were similar between rapid and non-rapid decliners. TNF-α expression was up-regulated in non-rapid decliners in response to H. influenzae (monocytes) and S. pneumoniae (co-culture of monocytes and PMN). Exposure to bacterial pathogens causes characteristic innate immune responses in peripheral blood monocytes and PMN in COPD. Bacterial exposure significantly alters the expression of TNF-α in COPD patients, although not consistently. There did not appear to be major differences in innate immune responses between rapid and non-rapid decliners.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge-Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E coli, and conclude with a discussion on the significance of this work. (C) 2004 Elsevier Ltd. All rights reserved.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations

The pioneering work of Runge and Kutta a hundred years ago has ultimately led to suites of sophisticated numerical methods suitable for solving complex systems of deterministic ordinary differential equations. However, in many modelling situations, the appropriate representation is a stochastic differential equation and here numerical methods are much less sophisticated. In this paper a very general class of stochastic Runge-Kutta methods is presented and much more efficient classes of explicit methods than previous extant methods are constructed. In particular, a method of strong order 2 with a deterministic component based on the classical Runge-Kutta method is constructed and some numerical results are presented to demonstrate the efficacy of this approach.

Order conditions of stochastic Runge-Kutta methods by B-series

In this paper, general order conditions and a global convergence proof are given for stochastic Runge Kutta methods applied to stochastic ordinary differential equations ( SODEs) of Stratonovich type. This work generalizes the ideas of B-series as applied to deterministic ordinary differential equations (ODEs) to the stochastic case and allows a completely general formalism for constructing high order stochastic methods, either explicit or implicit. Some numerical results will be given to illustrate this theory.

Numerical solutions of stochastic differential equations – implementation and stability issues

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively.

A variable stepsize implementation for stochastic differential equations

Stochastic differential equations (SDEs) arise from physical systems where the parameters describing the system can only be estimated or are subject to noise. Much work has been done recently on developing higher order Runge-Kutta methods for solving SDEs numerically. Fixed stepsize implementations of numerical methods have limitations when, for example, the SDE being solved is stiff as this forces the stepsize to be very small. This paper presents a completely general variable stepsize implementation of an embedded Runge Kutta pair for solving SDEs numerically; in this implementation, there is no restriction on the value used for the stepsize, and it is demonstrated that the integration remains on the correct Brownian path.

Adams-Type Methods for the Numerical Solution of Stochastic Ordinary Differential Equations

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively. (C) 2000 Elsevier Science B.V. All rights reserved.

High strong order methods for non-commutative stochastic ordinary differential equation systems and the Magnus formula

In recent years considerable attention has been paid to the numerical solution of stochastic ordinary differential equations (SODEs), as SODEs are often more appropriate than their deterministic counterparts in many modelling situations. However, unlike the deterministic case numerical methods for SODEs are considerably less sophisticated due to the difficulty in representing the (possibly large number of) random variable approximations to the stochastic integrals. Although Burrage and Burrage [High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations, Applied Numerical Mathematics 22 (1996) 81-101] were able to construct strong local order 1.5 stochastic Runge-Kutta methods for certain cases, it is known that all extant stochastic Runge-Kutta methods suffer an order reduction down to strong order 0.5 if there is non-commutativity between the functions associated with the multiple Wiener processes. This order reduction down to that of the Euler-Maruyama method imposes severe difficulties in obtaining meaningful solutions in a reasonable time frame and this paper attempts to circumvent these difficulties by some new techniques. An additional difficulty in solving SODEs arises even in the Linear case since it is not possible to write the solution analytically in terms of matrix exponentials unless there is a commutativity property between the functions associated with the multiple Wiener processes. Thus in this present paper first the work of Magnus [On the exponential solution of differential equations for a linear operator, Communications on Pure and Applied Mathematics 7 (1954) 649-673] (applied to deterministic non-commutative Linear problems) will be applied to non-commutative linear SODEs and methods of strong order 1.5 for arbitrary, linear, non-commutative SODE systems will be constructed - hence giving an accurate approximation to the general linear problem. Secondly, for general nonlinear non-commutative systems with an arbitrary number (d) of Wiener processes it is shown that strong local order I Runge-Kutta methods with d + 1 stages can be constructed by evaluated a set of Lie brackets as well as the standard function evaluations. A method is then constructed which can be efficiently implemented in a parallel environment for this arbitrary number of Wiener processes. Finally some numerical results are presented which illustrate the efficacy of these approaches. (C) 1999 Elsevier Science B.V. All rights reserved.