Incorporating age and delay into models for biophysical systems.

In many biological systems, chemical reactions or changes in a physical state are assumed to occur instantaneously. For describing the dynamics of those systems, Markov models that require exponentially distributed inter-event times have been used widely. However, some biophysical processes such as gene transcription and translation are known to have a significant gap between the initiation and the completion of the processes, which renders the usual assumption of exponential distribution untenable. In this paper, we consider relaxing this assumption by incorporating age-dependent random time delays (distributed according to a given probability distribution) into the system dynamics. We do so by constructing a measure-valued Markov process on a more abstract state space, which allows us to keep track of the 'ages' of molecules participating in a chemical reaction. We study the large-volume limit of such age-structured systems. We show that, when appropriately scaled, the stochastic system can be approximated by a system of partial differential equations (PDEs) in the large-volume limit, as opposed to ordinary differential equations (ODEs) in the classical theory. We show how the limiting PDE system can be used for the purpose of further model reductions and for devising efficient simulation algorithms. In order to describe the ideas, we use a simple transcription process as a running example. We, however, note that the methods developed in this paper apply to a wide class of biophysical systems.

A Comparison Between Emerging and Current Biophysical Methods for the Assessment of Higher-Order Structure of Biopharmaceuticals.

The higher-order structure (HOS) of protein therapeutics is a critical quality attribute directly related to their function. Traditionally, the HOS of protein therapeutics has been characterized by methods with low to medium structural resolution such as Fourier-transform infrared (FTIR), circular dichroism (CD), and intrinsic fluorescence spectroscopy, and differential scanning calorimetry (DSC). Recently, high-resolution nuclear magnetic resonance (NMR) methods have emerged as powerful tools for HOS characterization. NMR is a multi-attribute method with unique capabilities to provide information about all the structural levels of proteins in solution. We have in this study compared 1 D 1H Profile NMR with the established biophysical methods for HOS assessments using a set of blended samples of the monoclonal antibodies belonging to the subclasses IgG1 and IgG2. The study shows that Profile NMR can distinguish between most sample combinations (93%), DSC can differentiate 61% of the sample combinations, and near-ultraviolet CD spectroscopy can differentiate 52% of the sample combinations, whereas no significant distinction could be made between any samples using FTIR or intrinsic fluorescence. Our data therefore show that NMR has superior ability to address differences in HOS, a feature that could be directly applicable in comparability and similarity assessments.

An ultra-sensitive biophysical risk assessment of light effect on skin cells.

The aim of this study was to analyze photo-dynamic and photo-pathology changes of different color light radiations on human adult skin cells. We used a real-time biophysical and biomechanics monitoring system for light-induced cellular changes in an in vitro model to find mechanisms of the initial and continuous degenerative process. Cells were exposed to intermittent, mild and intense (1-180 min) light with On/Off cycles, using blue, green, red and white light. Cellular ultra-structural changes, damages, and ECM impair function were evaluated by up/down-regulation of biophysical, biomechanical and biochemical properties. All cells exposed to different color light radiation showed significant changes in a time-dependent manner. Particularly, cell growth, stiffness, roughness, cytoskeletal integrity and ECM proteins of the human dermal fibroblasts-adult (HDF-a) cells showed highest alteration, followed by human epidermal keratinocytes-adult (HEK-a) cells and human epidermal melanocytes-adult (HEM-a) cells. Such changes might impede the normal cellular functions. Overall, the obtained results identify a new insight that may contribute to premature aging, and causes it to look aged in younger people. Moreover, these results advance our understanding of the different color light-induced degenerative process and help the development of new therapeutic strategies.

Pieter Cullis: an outstanding lipid biophysicist, drug delivery scientist, educator, and entrepreneur.

There are much said about Pieter Cullis in this special volume honoring him. He was the pioneer to study the role of hexagonal HII phase in membrane fusion and the one who applied this concept for the design of lipid nanoparticles. He was also the first to utilize remote loading techniques for the delivery of amphipathic bases. At the same time, he is a tremendous entrepreneur and an excellent mentor. He is, without doubt, an exceptional scientist and set us an excellent model to follow.

Antipsicóticos para la esquizofrenia: paradigma de los medicamentos psiquiátricos / Antipsychotics for schizophrenia: the paradigm of psychiatric drugs

Los antipsicóticos no parecen revertir las causas de la esquizofrenia y, aunque son fármacos que pueden aliviar los síntomas a corto y mediano plazo, a largo plazo pueden no ser beneficiosos e incluso ser contraproducentes. Su empleo debería limitarse a situaciones agudas con agitación y tensión incapacitante. Presentan considerables efectos adversos y, ante la negativa de una persona a seguir tomándolos, adoptar una estrategia de reducción de daños apoyando y supervisando la retirada puede ser preferible a la coerción. Existen alternativas a los neurolépticos. Los prescriptores deberían estar más atentos y considerar las valoraciones que los usuarios hacen de sus efectos. El apego a las guías de tratamiento es escaso, seguramente por basarse en ensayos clinicos de calidad deficente, que deben mejorar y prolongarse en el tiempo. La raíz del problema probablemente se encuentra en la tautología sobre la etiología y naturaleza biológica de lo que llaman esquizofrenia, que realmente no parece ser más que un constructo ideológico-comercial.

Antipsychotic drugs do not appear to reverse the causes of schizophrenia, and although they can relieve symptoms in the short to medium term, in the long term they may not be beneficial and could even be counterproductive. Their use should be limited to acute situations in which agitation and tension is disabling. The drugs have significant adverse effects, and given the refusal of a person to continue taking them, a harm reduction strategy to support and monitor the withdrawal may be preferable to coercion. There are alternatives to neuroleptics. Prescribers should be more vigilant and consider the assessments of users regarding the drugs' effects. Adherence to treatment guidelines is low, probably because the guidelines are based on clinical trials of deficient quality which consequently should be improved and extended over a greater period of time. The root of the problem is likely the tautology on the etiology and biological nature of what is known as schizophrenia, which in fact does not seem to be more than a commercial and ideological construct.

Combined biophysical and cell-based approaches for the assessment of ligand binding to PPARγ.

Transcription factors of the peroxisome proliferator-activated receptor (PPAR) family are ligand-activated receptors that play key roles in lipid metabolism and inflammation. The γ isoform (PPARγ) is involved in adipocyte differentiation, insulin sensitization, and vascular pathophysiology, including inflammation and atherosclerosis, for which it is considered an important drug target. PPARγ ligands display varied structures and include fatty acids, electrophilic lipids, and certain drugs. These agonists promote conformational changes allowing interaction of PPARγ with coactivators and hence transcriptional regulation. Here we present a panoply of methods to study PPARγ interactions with ligands and activation in vitro and in cells. The first method is based on the competition of the fluorescent dye 1-anilinonaphthalene-8-sulfonic acid (ANS) with PPARγ ligands for the ligand binding pocket, allowing detection and quantification of ligand binding to PPARγ. This method is specific for PPARγ while ANS displays negligible interaction with other nuclear receptors such as PPARα and retinoid X receptor α (RXRα). The ANS competition assay has been validated through comparison of the affinities determined for well-known PPARγ ligands by this method with those reported in the literature. We also describe here gel-based competition assays that show limited performance with non-covalently bound ligands. In addition, we present a fluorescence anisotropy assay to analyze PPARγ activation by ligands in vitro through their capacity of eliciting PPARγ interaction with a fluorescently labeled peptide derived from one of its coactivators (SRC-1). Finally, we show cell-based assays to investigate PPARγ activation by interaction with its ligands. We believe that combined approaches using ANS, fluorescent coactivator peptides, and in-cell assays to monitor PPARγ binding and interactions may provide valuable strategies for the identification and characterization of PPARγ ligands.

Transition paths, diffusive processes, and preequilibria of protein folding.

Fundamental relationships between the thermodynamics and kinetics of protein folding were investigated using chain models of natural proteins with diverse folding rates by extensive comparisons between the distribution of conformations in thermodynamic equilibrium and the distribution of conformations sampled along folding trajectories. Consistent with theory and single-molecule experiment, duration of the folding transition paths exhibits only a weak correlation with overall folding time. Conformational distributions of folding trajectories near the overall thermodynamic folding/unfolding barrier show significant deviations from preequilibrium. These deviations, the distribution of transition path times, and the variation of mean transition path time for different proteins can all be rationalized by a diffusive process that we modeled using simple Monte Carlo algorithms with an effective coordinate-independent diffusion coefficient. Conformations in the initial stages of transition paths tend to form more nonlocal contacts than typical conformations with the same number of native contacts. This statistical bias, which is indicative of preferred folding pathways, should be amenable to future single-molecule measurements. We found that the preexponential factor defined in the transition state theory of folding varies from protein to protein and that this variation can be rationalized by our Monte Carlo diffusion model. Thus, protein folding physics is different in certain fundamental respects from the physics envisioned by a simple transition-state picture. Nonetheless, transition state theory can be a useful approximate predictor of cooperative folding speed, because the height of the overall folding barrier is apparently a proxy for related rate-determining physical properties.

Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model.

Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.

Weighted Kolmogorov-Smirnov test: accounting for the tails.

Accurate goodness-of-fit tests for the extreme tails of empirical distributions is a very important issue, relevant in many contexts, including geophysics, insurance, and finance. We have derived exact asymptotic results for a generalization of the large-sample Kolmogorov-Smirnov test, well suited to testing these extreme tails. In passing, we have rederived and made more precise the approximate limit solutions found originally in unrelated fields, first in [L. Turban, J. Phys. A 25, 127 (1992)] and later in [P. L. Krapivsky and S. Redner, Am. J. Phys. 64, 546 (1996)].

First-passage and escape problems in the Feller process.

The Feller process is an one-dimensional diffusion process with linear drift and state-dependent diffusion coefficient vanishing at the origin. The process is positive definite and it is this property along with its linear character that have made Feller process a convenient candidate for the modeling of a number of phenomena ranging from single-neuron firing to volatility of financial assets. While general properties of the process have long been well known, less known are properties related to level crossing such as the first-passage and the escape problems. In this work we thoroughly address these questions.

Critical behavior of a tumor growth model: directed percolation with a mean-field flavor.

We examine the critical behavior of a lattice model of tumor growth where supplied nutrients are correlated with the distribution of tumor cells. Our results support the previous report [Ferreira et al., Phys. Rev. E 85, 010901(R) (2012)], which suggested that the critical behavior of the model differs from the expected directed percolation (DP) universality class. Surprisingly, only some of the critical exponents (ß, α, ν([perpendicular]), and z) take non-DP values while some others (ß', ν(||), and spreading-dynamics exponents Θ, δ, z') remain very close to their DP counterparts. The obtained exponents satisfy the scaling relations ß=αν(||), ß'=δν(||), and the generalized hyperscaling relation Θ+α+δ=d/z, where the dynamical exponent z is, however, used instead of the spreading exponent z'. Both in d=1 and d=2 versions of our model, the exponent ß most likely takes the mean-field value ß=1, and we speculate that it might be due to the roulette-wheel selection, which is used to choose the site to supply a nutrient.

Poissonian steady states: from stationary densities to stationary intensities.

Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.

Orientational correlations in confined DNA.

We study how the orientational correlations of DNA confined to nanochannels depend on the channel diameter D by means of Monte Carlo simulations and a mean-field theory. This theory describes DNA conformations in the experimentally relevant regime where the Flory-de Gennes theory does not apply. We show how local correlations determine the dependence of the end-to-end distance of the DNA molecule upon D. Tapered nanochannels provide the necessary resolution in D to study experimentally how the extension of confined DNA molecules depends upon D. Our experimental and theoretical results are in qualitative agreement.

Density functional theory for inhomogeneous ring polymeric fluids.

The modeling of ring polymers remains a challenge in classical density functional theory (DFT) due to the difficulty in solving the direct bond connectivity of the ring architecture without free ends. By considering the feature that all of the segments in a ring are equivalent, we give an algorithm to solve the integral of direct bond connectivity for ideal ring polymers, and therefore propose a DFT for inhomogeneous ring polymers, where the excess free energy functional is extended from an equation of state (EOS). This EOS exhibits better agreement than other EOSs for the compressibility factors, compared to Monte Carlo data. Importantly, the DFT satisfactorily reproduces the data of the configurational-bias Monte Carlo (CBMC) simulations for ring polymers. The local density profiles from the DFT show that the bead density of inhomogeneous ring fluids is independent of ring size, which is also confirmed by the CBMC simulations. Interestingly, the behavior of solvation force for ring polymers is quite similar to that of the polymers with infinite chain length.

Framework to study dynamic dependencies in networks of interacting processes.

The analysis of dynamic dependencies in complex systems such as the brain helps to understand how emerging properties arise from interactions. Here we propose an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy. We define multi-information measures that allow us to study the global statistical structure of the system as a whole, the total dependence between subsystems, and the temporal statistical structure of each subsystem. We develop a stationary and a nonstationary formulation of the framework. We then examine different decompositions of these multi-information measures. The transfer entropy naturally appears as a term in some of these decompositions. This allows us to examine its properties not as an isolated measure of interdependence but in the context of the complete framework. More generally we use causal graphs to study the specificity and sensitivity of all the measures appearing in these decompositions to different sources of statistical dependence arising from the causal connections between the subsystems. We illustrate that there is no straightforward relation between the strength of specific connections and specific terms in the decompositions. Furthermore, causal and noncausal statistical dependencies are not separable. In particular, the transfer entropy can be nonmonotonic in dependence on the connectivity strength between subsystems and is also sensitive to internal changes of the subsystems, so it should not be interpreted as a measure of connectivity strength. Altogether, in comparison to an analysis based on single isolated measures of interdependence, this framework is more powerful to analyze emergent properties in multivariate systems and to characterize functionally relevant changes in the dynamics.

Structure of a micropipette-aspirated vesicle determined from the bending-energy model.

The structure of the system consisting of an aspirating pipette and an aspirated vesicle is investigated with fixed total vesicle volume, total vesicle surface area, and aspirated volume fraction, based on the bending-energy model. Through an energetic consideration, the usage of an aspirated volume fraction can be converted to the aspirating pressure for the determination of a phase diagram; the procedure identifies a first-order transition, between a weakly aspirated state and the strongly aspirated state, as the pressure increases. The physical properties of the system are obtained from minimization of the bending energy by an implementation of the simulated annealing Monte Carlo procedure, which searches for a minimum in a multivariable space. An analysis of the hysteresis effects indicates that the experimentally observed aspirating and releasing critical pressures are related to the location of the spinodal points.

Hysteresis and nonequilibrium work theorem for DNA unzipping.

We study by using Monte Carlo simulations the hysteresis in unzipping and rezipping of a double stranded DNA (dsDNA) by pulling its strands in opposite directions in the fixed force ensemble. The force is increased at a constant rate from an initial value g(0) to some maximum value g(m) that lies above the phase boundary and then decreased back again to g(0). We observed hysteresis during a complete cycle of unzipping and rezipping. We obtained probability distributions of work performed over a cycle of unzipping and rezipping for various pulling rates. The mean of the distribution is found to be close (the difference being within 10%, except for very fast pulling) to the area of the hysteresis loop. We extract the equilibrium force versus separation isotherm by using the work theorem on repeated nonequilibrium force measurements. Our method is capable of reproducing the equilibrium and the nonequilibrium force-separation isotherms for the spontaneous rezipping of dsDNA.

Molecular simulations of the fluctuating conformational dynamics of intrinsically disordered proteins.

Intrinsically disordered proteins (IDPs) do not possess well-defined three-dimensional structures in solution under physiological conditions. We develop all-atom, united-atom, and coarse-grained Langevin dynamics simulations for the IDP α-synuclein that include geometric, attractive hydrophobic, and screened electrostatic interactions and are calibrated to the inter-residue separations measured in recent single-molecule fluorescence energy transfer (smFRET) experiments. We find that α-synuclein is disordered, with conformational statistics that are intermediate between random walk and collapsed globule behavior. An advantage of calibrated molecular simulations over constraint methods is that physical forces act on all residues, not only on residue pairs that are monitored experimentally, and these simulations can be used to study oligomerization and aggregation of multiple α-synuclein proteins that may precede amyloid formation.

Alzheimer random walk model: two previously overlooked diffusion regimes.

A non-Markovian one-dimensional random walk model is studied with emphasis on the phase-diagram, showing all the diffusion regimes, along with the exactly determined critical lines. The model, known as the Alzheimer walk, is endowed with memory-controlled diffusion, responsible for the model's long-range correlations, and is characterized by a rich variety of diffusive regimes. The importance of this model is that superdiffusion arises due not to memory per se, but rather also due to loss of memory. The recently reported numerically and analytically estimated values for the Hurst exponent are hereby reviewed. We report the finding of two, previously overlooked, phases, namely, evanescent log-periodic diffusion and log-periodic diffusion with escape, both with Hurst exponent H=1/2. In the former, the log-periodicity gets damped, whereas in the latter the first moment diverges. These phases further enrich the already intricate phase diagram. The results are discussed in the context of phase transitions, aging phenomena, and symmetry breaking.