Replicators in Fine-grained Environment: Adaptation and Polymorphism

Selection in a time-periodic environment is modeled via the two-player replicator dynamics. For sufficiently fast environmental changes, this is reduced to a multi-player replicator dynamics in a constant environment. The two-player terms correspond to the time-averaged payoffs, while the three and four-player terms arise from the adaptation of the morphs to their varying environment. Such multi-player (adaptive) terms can induce a stable polymorphism. The establishment of the polymorphism in partnership games [genetic selection] is accompanied by decreasing mean fitness of the population.Comment: 4 pages, 2 figure

Evaluating predictive pharmacogenetic signatures of adverse events in colorectal cancer patients treated with fluoropyrimidines

The potential clinical utility of genetic markers associated with response to fluoropyrimidine treatment in colorectal cancer patients remains controversial despite extensive study. Our aim was to test the clinical validity of both novel and previously identified markers of adverse events in a broad clinical setting. We have conducted an observational pharmacogenetic study of early adverse events in a cohort study of 254 colorectal cancer patients treated with 5-fluorouracil or capecitabine. Sixteen variants of nine key folate (pharmacodynamic) and drug metabolising (pharmacokinetic) enzymes have been analysed as individual markers and/or signatures of markers. We found a significant association between TYMP S471L (rs11479) and early dose modifications and/or severe adverse events (adjusted OR = 2.02 [1.03; 4.00], p = 0.042, adjusted OR = 2.70 [1.23; 5.92], p = 0.01 respectively). There was also a significant association between these phenotypes and a signature of DPYD mutations (Adjusted OR = 3.96 [1.17; 13.33], p = 0.03, adjusted OR = 6.76 [1.99; 22.96], p = 0.002 respectively). We did not identify any significant associations between the individual candidate pharmacodynamic markers and toxicity. If a predictive test for early adverse events analysed the TYMP and DPYD variants as a signature, the sensitivity would be 45.5 %, with a positive predictive value of just 33.9 % and thus poor clinical validity. Most studies to date have been under-powered to consider multiple pharmacokinetic and pharmacodynamic variants simultaneously but this and similar individualised data sets could be pooled in meta-analyses to resolve uncertainties about the potential clinical utility of these markers

Immunological basis of differences in disease resistance in the chicken

Genetic resistance to diseases is a multigenic trait governed mainly by the immune system and its interactions with many physiologic and environmental factors. In the adaptive immunity, T cell and B cell responses, the specific recognition of antigens and interactions between antigen presenting cells, T cells and B cells are crucial. It occurs through a network of mediator proteins such as the molecules of the major histocompatibility complex (MHC), T cell receptors, immunoglobulins and secreted proteins such as the cytokines and antibodies. The diversity of these proteins that mainly is due to an intrinsic polymorphism of the genes causes phenotypic variation in disease resistance. The well-known linkage of MHC polymorphism and Marek's disease resistance difference represents a classic model revealing immunological factors in resistance differences and diversity of mediator molecules. The molecular bases in any resistance variation to infectious pathogens are vaguely understood. This paper presents a review of the major immune mediators involved in resistance and susceptibility to infectious diseases and their functional mechanisms in the chicken. The genetic interaction of disease resistance with production traits and the environment is mentioned

Study on Evolvement Complexity in an Artificial Stock Market

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is L\'{e}vy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.Comment: 4 pages and 4 figure

Simulating Evolutionary Games: A Python-Based Introduction

This paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.Agent-Based Simulation, Python, Prisoner's Dilemma

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Biochemical characterization of a multi-drug resistant HIV-1 subtype AG reverse transcriptase: antagonism of AZT discrimination and excision pathways and sensitivity to RNase H inhibitors

We analyzed a multi-drug resistant (MR) HIV-1 re- verse transcriptase (RT), subcloned from a patient- derived subtype CRF02 AG, harboring 45 amino acid exchanges, amongst them four thymidine analog mutations (TAMs) relevant for high-level AZT (azi- dothymidine) resistance by AZTMP excision (M41L, D67N, T215Y, K219E) as well as four substitutions of the AZTTP discrimination pathway (A62V, V75I, F116Y and Q151M). In addition, K65R, known to an- tagonize AZTMP excision in HIV-1 subtype B was present. Although MR-RT harbored the most signif- icant amino acid exchanges T215Y and Q151M of each pathway, it exclusively used AZTTP discrimi- nation, indicating that the two mechanisms are mu- tually exclusive and that the Q151M pathway is ob- viously preferred since it confers resistance to most nucleoside inhibitors. A derivative was created, ad- ditionally harboring the TAM K70R and the rever- sions M151Q as well as R65K since K65R antago- nizes excision. MR-R65K-K70R-M151Q was compe- tent of AZTMP excision, whereas other combinations thereof with only one or two exchanges still pro- moted discrimination. To tackle the multi-drug resis- tance problem, we tested if the MR-RTs could still be inhibited by RNase H inhibitors. All MR-RTs exhibited similar sensitivity toward RNase H inhibitors be- longing to different inhibitor classes, indicating the importance of developing RNase H inhibitors further as anti-HIV drugs