Model Reduction on the Wnt Pathway Leads to Biological Adaptation

Complex systems are an unavoidable problem in the field of biology. One of the ways that scientists have tried to overcome this problem is by building mathematical models—manageable representations designed to look at specific physical phenomena. The Wnt Signaling Pathway is a complex system known to regulate cell-to-cell interactions, play a crucial role in Embryonic Development, and has been implicated in the study of cancer. Typically, the Wnt signal is observed through the behavior of a protein called beta-Catenin (β-Catenin). In 2003, Lee et al. built a model of the Wnt pathway which caused β-Catenin to increase over time. However, in 2010, Jensen et al. built a different model of the Wnt pathway which caused β-Catenin to oscillate over time. This project called for model reduction on the Jensen et al. model to identify the phenomenological parameter combinations that determined features of the Wnt oscillations. The method used to reduce the model is called the Manifold Boundary Approximation Method, which is a geometric, parameter-independent method of reducing the model one parameter at a time. Reduction of the model showed that there were 5 variables and 8 parameters which drove the oscillating behavior of the system. After comparing our results to the Lee et al. reduced model of the Wnt pathway done by student Dane Bjork, a minimal model was constructed which predicted a novel class behavior of the Wnt system: biological adaptation

A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators

We develop an algorithm for computing the solution of a large system of linear ordinary differential equations (ODEs) with polynomial inhomogeneity. This is equivalent to computing the action of a certain matrix function on the vector representing the initial condition. The matrix function is a linear combination of the matrix exponential and other functions related to the exponential (the so-called phi-functions). Such computations are the major computational burden in the implementation of exponential integrators, which can solve general ODEs. Our approach is to compute the action of the matrix function by constructing a Krylov subspace using Arnoldi or Lanczos iteration and projecting the function on this subspace. This is combined with time-stepping to prevent the Krylov subspace from growing too large. The algorithm is fully adaptive: it varies both the size of the time steps and the dimension of the Krylov subspace to reach the required accuracy. We implement this algorithm in the Matlab function phipm and we give instructions on how to obtain and use this function. Various numerical experiments show that the phipm function is often significantly more efficient than the state-of-the-art.Comment: 20 pages, 3 colour figures, code available from http://www.maths.leeds.ac.uk/~jitse/software.html . v2: Various changes to improve presentation as suggested by the refere

Batch solution of small PDEs with the OPS DSL

In this paper we discuss the challenges and optimisations opportunities when solving a large number of small, equally sized discretised PDEs on regular grids. We present an extension of the OPS (Oxford Parallel library for Structured meshes) embedded Domain Specific Language, and show how support can be added for solving multiple systems, and how OPS makes it easy to deploy a variety of transformations and optimisations. The new capabilities in OPS allow to automatically apply data structure transformations, as well as execution schedule transformations to deliver high performance on a variety of hardware platforms. We evaluate our work on an industrially representative finance simulation on Intel CPUs, as well as NVIDIA GPUs

Feature Selection of Post-Graduation Income of College Students in the United States

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure

A Virtual ‘experiential expert’ communities of practice in sharing evidence based prevention of novel psychoactive substance (NPS) use: The Portuguese experience

We present findings from a unique virtual community of practice piloted to support a programme of prevention evidence and knowledge sharing among professional prevention practitioners as ‘experiential experts’ around tackling novel psychoactive substances (NPS) use in Portugal. A mixed-methods approach that combined quantitative analysis of interactions and qualitative content analysis of debates about NPS, NPS users, patterns of use and best practices in prevention of this type of drug use was conducted. Results show low and irregular interactions between members of this virtual community, but very rich discussions around sharing of experiences and problematizing practices. We discuss the layers of interaction between members, and the shared learning around policy and practice implications. Such virtual and collaborative work practices are not yet integrated within the drug prevention field where instead individualistic approaches tend to prevail and preclude the sharing of alternative solutions that shape different experiences. Our virtual community of NPS prevention experts provides a flagship for ongoing collaboration between research, generation of evidence informing policy and practice, professional training, support and shared learning. It underscores the need for an innovative and multi-disciplinary approach to sharing perspectives in tackling emerging and harmful drug trends.info:eu-repo/semantics/acceptedVersio