Modeling subunit cooperativity in opening of tetrameric ion channels

Most potassium channels are tetramers of four homologous polypeptides (subunits). During channel gating, each subunit undergoes several conformational changes independent of the state of other subunits before reaching a permissive state, from which the channel can open. However, transition from the permissive states to the open state involves a concerted movement of all subunits. This cooperative transition must be included in Markov models of channel gating. Previously, it was implemented by considering all possible combinations of four subunit states in a much larger expanded model of channel states (e.g., 27,405 channel states versus 64 subunit states), which complicates modeling and is computationally intense, especially when accurate modeling requires a large number of subunit states. To overcome these complexities and retain the tetrameric molecular structure, a modeling approach was developed to incorporate the cooperative transition directly from the subunit models. In this approach, the open state is separated from the subunit models and represented by the net flux between the open state and the permissive states. Dynamic variations of the probability of state residencies computed using this direct approach and the expanded model were identical. Implementation of the direct approach is simple and its computational time is orders-of-magnitude shorter than the equivalent expanded model.     

Computing the transition state populations in simple protein models

We describe the master equation method for computing the kinetics of protein folding. We illustrate the method using a simple Go model. Presently most models of two-state fast-folding protein folding kinetics invoke the classical idea of a transition state to explain why there is a single exponential decay in time. However, if proteins fold via funnel-shaped energy landscapes, as predicted by many theoretical studies, then it raises the question of what is the transition state. Is it a specific structure, or a small ensemble of structures, as is expected from classical transition state theory? Or is it more like the denatured states of proteins, a very broad ensemble? The answer that is usually obtained depends on the assumptions made about the transition state. The present method is a rigorous way to find transition states, without assumptions or approximations, even for very nonclassical shapes of energy landscapes. We illustrate the method here, showing how the transition states in two-state protein folding can be very broad ensembles. © 2002 Wiley Periodicals, Inc. Biopolymers 68: 35–46, 2003     

Testing hypotheses about ecological specialization using phylogenetic trees

It is often assumed that ecological specialization represents an evolutionary "dead-end" that limits further evolution. Maximum-likelihood (ML) analyses on phylogenies for 15 groups of phytophagous insects revealed that high transition rates both to and from specialization occurred, but that the mean ratio of rates was significantly biased toward a higher rate to specialization. Here we explore the consequences of the fact that transition rates inferred by ML are affected not only by the distribution, but also by the frequency, of character states. Higher rates to the more common state were inferred in the analyses of Nosil (2002), in similar studies published since 2002, and in a small set of simulations. Thus, the ratio of the rate toward versus away from specialization was strongly, positively correlated with the proportion of specialist species at the tips of the phylogeny and whether transitions away from specialization occur remains unclear. Here we reexamine these data using methods that do not rely on directly comparing transition rates. Maximum-likelihood analyses show that models with no transitions in one direction (e.g., irreversible evolution as predicted by the "specialist as dead end" framework) are usually strongly rejected, independent of the proportion of specialists at the tips. Ancestral state reconstruction revealed two instances where generalists were unambiguously derived from specialists. Transition rates would need to biased 100-fold and 5000-fold toward specialization to reconstruct a history where these shifts from specialization toward generalization do not occur. The general conclusions of Nosil (2002) appear to hold; transitions in either direction likely occur such that specialization does not always limit further evolution. Most generally, inferences regarding character evolution can be strengthened by comparing models of character change and examining ancestor states, rather than only comparing parameter values.     

From complex spatial dynamics to simple Markov chain models: do predators and prey leave footprints?

In this paper we present a concept for using presence–absence data to recover information on the population dynamics of predator–prey systems. We use a highly complex and spatially explicit simulation model of a predator–prey mite system to generate simple presence–absence data: the number of patches with both prey and predators, with prey only, with predators only, and with neither species, along with the number of patches that change from one state to another in each time step. The average number of patches in the four states, as well as the average transition probabilities from one state to another, are then depicted in a state transition diagram, constituting the "footprints" of the underlying population dynamics. We investigate to what extent changes in the population processes modeled in the complex simulation (i.e. the predator's functional response and the dispersal rates of both species) are reflected by different footprints
The transition probabilities can be used to forecast the expected fate of a system given its current state. However, the transition probabilities in the modeled system depend on the number of patches in each state. We develop a model for the dependence of transition probabilities on state variables, and combine this information in a Markov chain transition matrix model. Finally, we use this extended model to predict the long-term dynamics of the system and to reveal its asymptotic steady state properties.     

Calculation of mutational free energy changes in transition states for protein folding

Recent advances in experimental and computational methods have made it possible to determine with considerable accuracy the structures whose formation is rate limiting for the folding of some small proteins-the transition state ensemble, or TSE. We present a method to analyze and validate all-atom models of such structures. The method is based on the comparison of experimental data with the computation of the change in free energy of the TSE resulting from specific mutations. Each mutation is modeled individually in all members of an ensemble of transition state structures using a method originally developed to predict mutational changes in the stability of native proteins. We first apply this method to six proteins for which we have determined the TSEs with a technique that uses experimental mutational data (Phi-values) as restraints in the structure determination and find a highly significant correlation between the calculated free energy changes and those derived from experimental kinetic data. We then use the procedure to analyze transition state structures determined by molecular dynamics simulations of unfolding, again finding a high correlation. Finally, we use the method to estimate changes in folding rates of several hydrophobic core mutants of Fyn SH3. Taken together, these results show that the procedure developed here is a tool of general validity for analyzing, assessing, and improving the quality of the structures of transition states for protein folding.     

On phylogenetic tests of irreversible evolution

"Dollo's law" states that, following loss, a complex trait cannot reevolve in an identical manner. Although the law has previously fallen into disrepute, it has only recently been challenged with statistical phylogenetic methods. We employ simulation studies of an irreversible binary character to show that rejections of Dollo's law based on likelihood-ratio tests of transition rate constraints or on reconstructions of ancestral states are frequently incorrect. We identify two major causes of errors: incorrect assignment of root state frequencies, and neglect of the effect of the character state on rates of speciation and extinction. Our findings do not necessarily overturn the conclusions of phylogenetic studies claiming reversals, but we demonstrate devastating flaws in the methods that are the foundation of all such studies. Furthermore, we show that false rejections of Dollo's law can be reduced by the use of appropriate existing models and model selection procedures. More powerful tests of irreversibility require data beyond phylogenies and character states of extant taxa, and we highlight empirical work that incorporates additional information.     

Exploring the role of large conformational changes in the fidelity of DNA polymerase beta

The relationships between the conformational landscape, nucleotide insertion catalysis and fidelity of DNA polymerase beta are explored by means of computational simulations. The simulations indicate that the transition states for incorporation of right (R) and wrong (W) nucleotides reside in substantially different protein conformations. The protein conformational changes that reproduce the experimentally observed fidelity are significantly larger than the small rearrangements that usually accompany motions from the reactant state to the transition state in common enzymatic reactions. Once substrate binding has occurred, different constraints imposed on the transition states for insertion of R and W nucleotides render it highly unlikely that both transition states can occur in the same closed structure, because the predicted fidelity would then be many orders of magnitude too large. Since the conformational changes reduce the transition state energy of W incorporation drastically they decrease fidelity rather than increase it. Overall, a better agreement with experimental data is attained when the R is incorporated through a transition state in a closed conformation and W is incorporated through a transition state in one or perhaps several partially open conformations. The generation of free energy surfaces for R and W also allow us to analyze proposals about the relationship between induced fit and fidelity.     

A new method for inferring hidden markov models from noisy time sequences

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.     

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in “continuous time”. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.     

Open capture-recapture models with heterogeneity: I. Cormack-Jolly-Seber model

In open population capture-recapture studies, it is usually assumed that similar animals (e.g., of the same sex and age group) have similar survival rates and capture probabilities. These assumptions are generally perceived to be an oversimplification, and they can lead to incorrect model selection and biased parameter estimates. Allowing for individual variability in survival and capture probabilities among apparently similar animals is now becoming possible, due to advances in closed population models and improved computing power. This article presents a flexible framework of likelihood-based models which allow for individual heterogeneity in survival and capture rates. Heterogeneity is modeled using finite mixtures, which have enough flexibility of distribution shape to accommodate a wide variety of different patterns of individual variation. The models condition on the first capture of each animal, and include as a special case the Cormack-Jolly-Seber model. Model selection is done either using Akaike's information criterion or by likelihood ratio tests, making available checks of different influences on survival rates. Bias in parameter estimates is reduced by including individual heterogeneity. Model selection and bias reduction are important in population studies and for making informed management decisions.     

Modelling the transition of asexual blood stages of Plasmodium falciparum to gametocytes

In this paper, we investigate the transition of asexual blood stages of P. falciparum to gametocytes. The study is based on daily data, collected from 262 individual courses of parasitaemia. We propose several mathematical models that follow biological reasoning. The models are fitted with maximum likelihood and are compared with each other. The models differ in the assumptions made about the mortality of circulating gametocytes and about the transition rate of the asexual parasites. Gametocyte mortality is modelled as being (i) constant over time, (ii) linearly increasing over time, (iii) linearly increasing over gametocyte age, and (iv) exponentially increasing over gametocyte age, respectively. The transition rate is either kept constant per patient or piecewise constant within intervals that correspond to waves of asexual parasitaemia which are assumed to be caused by different Pf(emp1)-variants. According to likelihood ratio tests, the models with age-dependent mortality rate and wave-dependent transition rates are superior to the models with constant transition rate and/or constant or time-dependent mortality rate. The best fits are reached for models with exponentially increasing (Gompertz-type) mortality. Furthermore, an impact of high asexual parasite densities on the survival of gametocytes, interpreted as a cytokine-mediated effect, is evident in some cases.     

Assessing the robustness of randomization tests: examples from behavioural studies

Behavioural studies are commonly plagued with data that violate the assumptions of parametric statistics. Consequently, classic nonparametric methods (e.g. rank tests) and novel distribution-free methods (e.g. randomization tests) have been used to a great extent by behaviourists. However, the robustness of such methods in terms of statistical power and type I error have seldom been evaluated. This probably reflects the fact that empirical methods, such as Monte Carlo approaches, are required to assess these concerns. In this study we show that analytical methods cannot always be used to evaluate the robustness of statistical tests, but rather Monte Carlo approaches must be employed. We detail empirical protocols for estimating power and type I error rates for parametric, nonparametric and randomization methods, and demonstrate their application for an analysis of variance and a regression/correlation analysis design. Together, this study provides a framework from which behaviourists can compare the reliability of different methods for data analysis, serving as a basis for selecting the most appropriate statistical test given the characteristics of data at hand. Copyright 2001 The Association for the Study of Animal Behaviour.     

Modeling animals' behavioral response by Markov chain models for capture-recapture experiments

A bivariate Markov chain approach that includes both enduring (long-term) and ephemeral (short-term) behavioral effects in models for capture-recapture experiments is proposed. The capture history of each animal is modeled as a Markov chain with a bivariate state space with states determined by the capture status (capture/noncapture) and marking status (marked/unmarked). In this framework, a conditional-likelihood method is used to estimate the population size and the transition probabilities. The classical behavioral model that assumes only an enduring behavioral effect is included as a special case of the bivariate Markovian model. Another special case that assumes only an ephemeral behavioral effect reduces to a univariate Markov chain based on capture/noncapture status. The model with the ephemeral behavioral effect is extended to incorporate time effects; in this model, in contrast to extensions of the classical behavioral model, all parameters are identifiable. A data set is analyzed to illustrate the use of the Markovian models in interpreting animals' behavioral response. Simulation results are reported to examine the performance of the estimators.     

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Understanding the basis for intracellular motion is critical as the field moves toward a deeper understanding of the relation between Brownian forces, molecular crowding, and anisotropic (or isotropic) energetic forcing. Effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study discusses limitations in currently popular analysis methods (e.g., mean square displacement-based analyses) and how new techniques can be used to systematically analyze Single Particle Tracking (SPT) data experiencing abrupt state changes in time or space. The approach is to track GFP tagged chromatids in metaphase in live yeast cells and quantitatively probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes can be induced by various sources including: microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, and DNA-based molecular machines including polymerases and protein exchange complexes such as chaperones and chromatin remodeling complexes. Simulations aiming to show the relevance of the approach to more general SPT data analyses are also studied. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for “ground-truth” and/or subjective information.     

Non-linear rate-equilibrium free energy relationships and Hammond behavior in protein folding

Non-linear rate-equilibrium relationships upon mutation or changes in solvent conditions are frequently observed in protein folding reactions and are usually interpreted in terms of Hammond behavior. Here we first give a general overview over the concept of transition state movements in chemical reactions and discuss its application to protein folding. We then show examples for genuine Hammond behavior and for apparent transition state movements caused by other effects like changes in the rate-limiting step of the folding reaction or ground state effects, i.e. structural changes in either the native state or the unfolded state. These examples show that apparent transition state movements can easily be mistaken for Hammond behavior. We describe experimental tests using self- and cross-interaction parameters to distinguish between structural changes in a single transition state following Hammond behavior and apparent transition state movements caused by other effects.     

Demographic estimation methods for plants with unobservable life-states

Demographic estimation of vital parameters in plants with an unobservable dormant state is complicated, because time of death is not known. Conventional methods assume that death occurs at a particular time after a plant has last been seen aboveground but the consequences of assuming a particular duration of dormancy have never been tested. Capture–recapture methods do not make assumptions about time of death; however, problems with parameter estimability have not yet been resolved. To date, a critical comparative assessment of these methods is lacking. We analysed data from a 10 year study of Cleistes bifaria, a terrestrial orchid with frequent dormancy, and compared demographic estimates obtained by five varieties of the conventional methods, and two capture–recapture methods. All conventional methods produced spurious unity survival estimates for some years or for some states, and estimates of demographic rates sensitive to the time of death assumption. In contrast, capture–recapture methods are more parsimonious in terms of assumptions, are based on well founded theory and did not produce spurious estimates. In Cleistes, dormant episodes lasted for 1–4 years (mean 1.4, SD 0.74). The capture–recapture models estimated ramet survival rate at 0.86 (SE～0.01), ranging from 0.77–0.94 (SEs≤0.1) in any one year. The average fraction dormant was estimated at 30% (SE 1.5), ranging 16–47% (SEs≤5.1) in any one year. Multistate capture–recapture models showed that survival rates were positively related to precipitation in the current year, but transition rates were more strongly related to precipitation in the previous than in the current year, with more ramets going dormant following dry years. Not all capture–recapture models of interest have estimable parameters; for instance, without excavating plants in years when they do not appear aboveground, it is not possible to obtain independent time‐specific survival estimates for dormant plants. We introduce rigorous computer algebra methods to identify the parameters that are estimable in principle. As life‐states are a prominent feature in plant life cycles, multistate capture–recapture models are a natural framework for analysing population dynamics of plants with dormancy.     

Superposition properties of interacting ion channels.

Quantitative analysis of patch clamp data is widely based on stochastic models of single-channel kinetics. Membrane patches often contain more than one active channel of a given type, and it is usually assumed that these behave independently in order to interpret the record and infer individual channel properties. However, recent studies suggest there are significant channel interactions in some systems. We examine a model of dependence in a system of two identical channels, each modeled by a continuous-time Markov chain in which specified transition rates are dependent on the conductance state of the other channel, changing instantaneously when the other channel opens or closes. Each channel then has, e.g., a closed time density that is conditional on the other channel being open or closed, these being identical under independence. We relate the two densities by a convolution function that embodies information about, and serves to quantify, dependence in the closed class. Distributions of observable (superposition) sojourn times are given in terms of these conditional densities. The behavior of two channel systems based on two- and three-state Markov models is examined by simulation. Optimized fitting of simulated data using reasonable parameters values and sample size indicates that both positive and negative cooperativity can be distinguished from independence.     

Estimating transition rates in aggregated Markov models of ion channel gating with loops and with nearly equal dwell times

A typical task in the application of aggregated Markov models to ion channel data is the estimation of the transition rates between the states. Realistic models for ion channel data often have one or more loops. We show that the transition rates of a model with loops are not identifiable if the model has either equal open or closed dwell times. This non-identifiability of the transition rates also has an effect on the estimation of the transition rates for models which are not subject to the constraint of either equal open or closed dwell times. If a model with loops has nearly equal dwell times, the Hessian matrix of its likelihood function will be ill-conditioned and the standard deviations of the estimated transition rates become extraordinarily large for a number of data points which are typically recorded in experiments.     

Ethological validation and the assessment of anxiety-like behaviours: methodological comparison of classical analyses and structural approaches

The research on emotional reactivity usually implies the use of standardised behavioural tests that provide a quick idea of the effect of a treatment on the reactivity of subjects to potentially dangerous situations. Many validity criteria have been considered to evaluate these tests. This validity concept supports the idea that animals' behaviour in these tests model human anxiety. Generally, those criteria repeatedly labelled as "ethological validation" refer to the analogy between animals and human in the meaning of the test situation. Although the content of the ethological validation concept is heterogeneous, it is steadily related to a fixed interpretation of the behavioural items produced in a given experimental setting. The basic assumption of such reasoning is that the behavioural items would always be expressed in the same behavioural context whatever the subject, its gender, strain or species, thoroughly asserting a predefined subjective state. Using multivariate and textual analysis, we found evidence that the "ethological validation" recourse to an a priori interpretation for a given behavioural variable may be deceptive. We defend the idea that the meaning of a behavioural variable should be restricted to the general context where it arose. Theoretical propositions and methodological options are discussed.     

Computerized Analysis of Crustacean Relationships

The strengths and weaknesses of phylogenetic analysis using computers are reviewed from the viewpoint of understanding crustacean evolution. Computerized methods require the explicit presentation of characters and character state homologies. New techniques allow investigators to design evolutionary models into a character data matrix, or to use evolutionary models that make minimal a priori assumptions. The computer analysis relieves the investigator from the highly repetitious testing of trees, allows the concentration on the character state data, and provides objective methods for comparing trees, primarily their length. These are regarded as the strengths of computerized methods. The weaknesses of these methods include the relatively inscrutable nature of the character data matrix compared with the overall ‘gestalt’ of resulting trees, the difficulties of defining discrete homologies within the Crustacea, especially for counts of segmentation, the lack of clear intermediate character states in some multistate segmental characters, and the inability to define evolutionary polarity. These difficulties may be overcome by analysing the data using the minimal assumption models of character evolution, and by a recognition that the trees are a result of the input data, and therefore the data should be criticized, rather than the trees themselves. A ‘consensus’ character data set, including most extant major groups of the Crustacea as well as several key fossils, was assembled and revised by the participants in the workshop. An artificial taxon, ‘ur-crustacean characters’, was introduced to root the tree. Three observations may be made from parsimony analyses using several weighting and tree rooting methods. (1) The currently accepted large scale phylogeny and classification of the Crustacea is not corroborated. (2) The number of supposed plesiomorphic traits possessed by a taxon is not a good index for early derivation in crustacean evolution. (3) The taxon Maxillopoda is not supported by the arrangement of any of the trees.