抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Continuous control of chaos by self-controlling feedback

Chaotic time series data are observed routinely in experiments on physical systems and in observations in the field. The authors review developments in the extraction of information of physical importance from such measurements. They discuss methods for (1) separating the signal of physical interest from contamination ("noise reduction"), (2) constructing an appropriate state space or phase space for the data in which the full structure of the strange attractor associated with the chaotic observations is unfolded, (3) evaluating invariant properties of the dynamics such as dimensions, Lyapunov exponents, and topological characteristics, and (4) model making, local and global, for prediction and other goals. They briefly touch on the effects of linearly filtering data before analyzing it as a chaotic time series. Controlling chaotic physical systems and using them to synchronize and possibly communicate between source and receiver is considered. Finally, chaos in space-time systems, that is, the dynamics of fields, is briefly considered. While much is now known about the analysis of observed temporal chaos, spatio-temporal chaotic systems pose new challenges. The emphasis throughout the review is on the tools one now has for the realistic study of measured data in laboratory and field settings. It is the goal of this review to bring these tools into general use among physicists who study classical and semiclassical systems. Much of the progress in studying chaotic systems has rested on computational tools with some underlying rigorous mathematics. Heuristic and intuitive analysis tools guided by this mathematics and realizable on existing computers constitute the core of this review.

The analysis of observed chaotic data in physical systems

Nonlinear prediction of chaotic time series

“Understanding complex systems” has become the overarching theme of a new approach to science. Promoting research that encourages a holistic perspective[1] to “understand complex systems” is a long term investment priority in the strategic plan of the National Science Foundation of the United States for the next five years [2]. In 2005 Springer Verlag started a book series entitled “Understanding Complex Systems” [3] and since 2001 the annual “Understanding Complex Systems” symposium is held at the University of Illinois at Urbana-Champaign [4]. The phrase “understanding complex systems” is often used, but its meaning is rarely defined. The phrase is a sequence of three words, where each word describes an abstract concept: understanding, complex, and system.

Understanding complex systems: Defining an abstract concept

Generalized dimensions, entropies, Lyapounov exponents [1], and approximations of the flow vector field [2, 3] are used to describe the periodic and chaotic dynamics of nonlinear experimental systems. In addition to the passive observation of a nonlinear oscillator and the description of the measured data using statistical quantities, it is possible to characterize a nonlinear oscillator by an active method, namely by determining its response to specific driving forces [4]. Active methods, like resonance spectroscopy, are superior to passive methods, especially when the experimental system is a set of identical, weakly coupled oscillators, behaving incoherently. I f one gets without driving force a compound signal of all oscillators, small and complex due to interference, a strong response emerges at resonance. At resonance every single oscillator is forced to coherent oscillations, sychronized by the driving force. For example, when a damped nonlinear mechanical pendulum is perturbed by a sinusoidal force, the response is comparatively small in amplitude [5] and does not fulfill any well-defined resonance condition [6], even when the frequency of the driving force coincides with a peak(resonance) in the power spectrum of the dynamics of the unperturbed system [7]. Outside the region of entrainment the response is complicated, in many cases chaotic [8]. In order to obtain a large response, the frequency of the driving force has to be varied in such a way, that it coincides at all amplitudes with a characteristic frequency of the oscillator. The characteristic frequencies of a nonlinear oscillator depend on the amplitude. For example, a weekly damped, mechanical oscillator with an anharmonic potential

Resonant stimulation and control of nonlinear oscillators

A new algorithm to determine the number of degrees of freedom of dynamic systems is presented. To obtain a concise description of an observed chaotic time sequence, an approximation of the flow in a state space representation by series is shown to be useful.

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Construction of Differential Equations from Experimental Data

We have developed a method for reconstructing equations of motion for systems where all the necessary variables have not been observed. This technique can be applied to systems with one or several such hidden variables, and can be used to reconstruct maps or differential equations. The effects of experimental noise are discussed through specific examples. The control of nonlinear systems containing hidden variables is also discussed.

Reconstructing equations of motion from experimental data with unobserved variables

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k, from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

/pdf/inferring-markov-chains-bayesian-estimation-model-comparison-1ms55v4ooe.pdf

Alfred Hubler

Papers

Understanding complex systems: Defining an abstract concept

Resonant stimulation and control of nonlinear oscillators

Construction of Differential Equations from Experimental Data

Reconstructing equations of motion from experimental data with unobserved variables

Inferring Markov chains : Bayesian estimation, model comparison, entropy rate, and out-of-class modeling