http://people.physik.hu-berlin.de/~lindner/PDF/physreport.pdf

Effects of noise in excitable systems

Complex bodily rhythms are ubiquitous in living organisms. These rhythms arise from stochastic, nonlinear biological mechanisms interacting with a fluctuating environment. Disease often leads to alterations from normal to pathological rhythm. Fundamental questions concerning the dynamics of these rhythmic processes abound. For example, what is the origin of physiological rhythms? How do the rhythms interact with each other and the external environment? Can we decode the fluctuations in physiological rhythms to better diagnose human disease? And can we develop better methods to control pathological rhythms? Mathematical and physical techniques combined with physiological and medical studies are addressing these questions and are transforming our understanding of the rhythms of life.

Synchronization and rhythmic processes in physiology

Nanofluidics has emerged recently in the footsteps of microfluidics, following the quest for scale reduction inherent to nanotechnologies. By definition, nanofluidics explores transport phenomena of fluids at nanometer scales. Why is the nanometer scale specific? What fluid properties are probed at nanometric scales? In other words, why does ‘nanofluidics’ deserve its own brand name? In this critical review, we will explore the vast manifold of length scales emerging for fluid behavior at the nanoscale, as well as the associated mechanisms and corresponding applications. We will in particular explore the interplay between bulk and interface phenomena. The limit of validity of the continuum approaches will be discussed, as well as the numerous surface induced effects occurring at these scales, from hydrodynamic slippage to the various electro-kinetic phenomena originating from the couplings between hydrodynamics and electrostatics. An enlightening analogy between ion transport in nanochannels and transport in doped semi-conductors will be discussed (156 references).

http://ilm-perso.univ-lyon1.fr/~lbocquet/ChemSocRev-Bocquet-2010.pdf

Nanofluidics, from bulk to interfaces

The integrate-and-fire neuron model is one of the most widely used models for analyzing the behavior of neural systems. It describes the membrane potential of a neuron in terms of the synaptic inputs and the injected current that it receives. An action potential (spike) is generated when the membrane potential reaches a threshold, but the actual changes associated with the membrane voltage and conductances driving the action potential do not form part of the model. The synaptic inputs to the neuron are considered to be stochastic and are described as a temporally homogeneous Poisson process. Methods and results for both current synapses and conductance synapses are examined in the diffusion approximation, where the individual contributions to the postsynaptic potential are small. The focus of this review is upon the mathematical techniques that give the time distribution of output spikes, namely stochastic differential equations and the Fokker–Planck equation. The integrate-and-fire neuron model has become established as a canonical model for the description of spiking neurons because it is capable of being analyzed mathematically while at the same time being sufficiently complex to capture many of the essential features of neural processing. A number of variations of the model are discussed, together with the relationship with the Hodgkin–Huxley neuron model and the comparison with electrophysiological data. A brief overview is given of two issues in neural information processing that the integrate-and-fire neuron model has contributed to – the irregular nature of spiking in cortical neurons and neural gain modulation.

https://link.springer.com/content/pdf/10.1007%2Fs00422-006-0068-6.pdf

A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Seizure prediction: the long and winding road.

Handbook of Biological Physics

Publisher Summary This chapter discusses the cell biological mechanisms through which prokaryotes develop multidrug resistance. The majority of bacterial multidrug transporters characterized thus far, operates via a secondary transport mechanism. A number of genes encoding TEXANs and Mini TEXANs are cloned and sequenced. Analysis of these primary sequences reveals a striking similarity in the overall structure, suggesting that the proteins may function via a similar mechanism. Transport studies in membrane vesicles of Lactococcus lactis ( L. lactis )and Escherichia coli ( E. coli )are demonstrated the drug/proton antiport mechanism of the TEXAN LmrP. Smr and EmrE are purified and characterized upon reconstitution into proteoliposomes. These studies exhibit that a single multidrug resistance protein is able to function as a drug pump, and show the Δp-dependence of drug transport via the Mini TEXANs. The discovery of the ABC-type multidrug transporter LmrA in L. lactis shows overlap with that of the human multidrug resistance P-glycoprotein, the TEXANs LmrP and Bmr, and Mini TEXANs Smr, EmrE and QacC. On the other hand, the substrate specificity of these multidrug transporters is very different from that of the TetA(B) and other transporters dedicated to the efflux of a specific drug or class of drugs.

Multidrug resistance in prokaryotes: molecular mechanisms of drug efflux

The present invention relates to protein and genes encoding said protein as well as cells harbouring said genes or protein. The protein is a prokaryotic homolog of the human P-glycoprotein which is encoded by the MDR-1 gene. The MDR-1 gene is a gene related to the occurrence of multidrug resistance.

A prokaryotic protein having functional and structural homology with the human p-glycoprotein encoded by the mdr-1 gene, nucleic acids encoding and cells expressing said protein

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.

Hendrik Bolhuis

Papers

Handbook of Biological Physics

Multidrug resistance in prokaryotes: molecular mechanisms of drug efflux

A prokaryotic protein having functional and structural homology with the human p-glycoprotein encoded by the mdr-1 gene, nucleic acids encoding and cells expressing said protein

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.