Telluride Workshop on

The Complexity of Dynamics and Kinetics from Single Molecules to Cells in June 14-18, 2015

Let the system speak for itself

S. Berry, "Enrichment of Network Diagrams for Potential Surfaces"

Traditional network diagrams connect nodes with simple lines. When one uses them to help interpret complex potential surfaces, that information, the links between minima, is not sufficient to give insights into the dynamics that can occur on those surfaces. Here, we introduce a simple means to introduce more information into the network diagram. Specifically, since each node represents a local potential minimum, the line that links two nodes can be considered a representation of the minimum-energy path between those minima. Then one can add a point along the link, for example, the midpoint, so the link then consists of two segments, one from each minimum to the saddle point of the lowest-energy path connecting them. That makes it possible to represent the energy, at least in terms of energy bands, by coloring each segment from minimum to saddle. We here show examples of such color-coded network diagrams for two illustrative atomic clusters.


J. Green, "Information in a rate coefficient"

Traditional predictions of chemical kinetics can breakdown when molecular transformations are sensitive to the microscopic structure and energetics of the surroundings. Examples of this phenomenon include fluorescence decay, hydrogen bond making and breaking in water, electron or proton transfer in viscous solvents, and the forced dissociation of single biomolecules. Predictions accounting for the complex role of the environment often use fluctuating rate coefficients, but it is not clear a priori when a single rate coefficient will suffice. In this talk, I will describe our theoretical framework for this class of "disordered" rate processes, which answers the question: when can kinetics be accurately described by a fluctuating rate coefficient, and when is the rate coefficient unique? The framework includes a measure of fluctuations that could translate into a general method for extracting optimal estimates of rate coefficients from complex kinetics.

[1] S. W. Flynn, H. C. Zhao and J. R. Green, J. Chem. Phys., 2014, 141, 104107.
[2] J. W. Nichols, S. W. Flynn and J. R. Green, J. Chem. Phys., 2015, 142, 064113.

C. Landes, "Hit ‘em where they ain’t: super-resolution imaging of porous nanomaterials"

Porous materials such as the cellular cytosol, hydrogels, and block copolymers have nanoscale features that control their macroscale function. Despite the importance of these materials, a detailed understanding of the relationship between the heterogeneous structure and the functional capabilities is lacking due to insufficient characterization techniques. Super-resolution imaging generally requires chemical labeling, which alters the surface chemistry and limits utility. An optimized analytical method would image in situ and relate the heterogeneous nanoscale structure to a functional property such as intra-material transport. We introduce a super-resolution optical imaging technique that relies on probing the porous space within nanomaterials. The theoretical framework for stochastic diffusion to produce super-resolution information is provided and the technique is demonstrated by simulation. Our results are compared to diffraction-limited imaging and localization-based single particle tracking (SPT) to show correlation provides an objective, sensitive analysis, especially under challenging experimental conditions with low signal or high density of emitters. The method provides sub-diffraction-limited structural information about the material as well as transport dynamics. Pore sizes and diffusion coefficients are better understood compared to diffraction-limited imaging and particle tracking.

[1]L. Kisley, J. Chen, A. P. Mansur, B. Shuang, K. Kourentzi, M. V. Poongavanam, W. H. Chen, S. Dhamane, R. C. Willson and C. F. Landes , PNAS, 2014, 111, 6, 2075–2080.
[2]L. Kisley and C. F. Landes, Anal. Chem., 2015, 87, 1, 83-98.

J. Cao, "Conformational Fluctuations in DNAs and Enzymes"

Conformational Fluctuations in DNAs and Enzymes Jianshu Cao, MIT, Department of Chemistry In collaboration with experimental studies, we unveiled a correlation between local deformations in DNAs and identified this correlation as the underlying mechanism for the observed allosteric DNA-protein binding.[1] With an explicit consideration of the length scale over which local deformations are correlated, our improved Correlated Worm-Like Chain (C-WLC) model predicts a length dependent flexibility. While our model reduces to the traditional WLC model in the long chain limit, it predicts that DNA becomes much more flexible at shorter scales that are of biological importance, in good agreement with the loop formation measurements of short DNA fragments around 100 base pairs. [2] Theoretical results from the C-WLC model at weakly bending limits are also derived, providing key insight that will motivate new experiments. Motivated by single molecule kinetic experiments on enzymes and proteins, we derived a generalized Michaelis-Menten equation for an arbitrary enzymatic network.[3,4] The generalized MM equation reduces to the MM form, when detailed balance is obeyed, which explains why most enzymatic reactions follow the MM functional form under experimental conditions. More importantly, the new expression predicts a relation between non-MM correction terms and unbalanced conformational currents. If time allows, I will present a general theoretical tool to analyze network kinetics, which focuses on the topology of networks instead of fundamental rate steps.[5,6]

[1] Modeling Spatial Correlation of DNA Deformation: DNA Allostery in Protein Binding. X. L. Xu, H. Ge, C. Gu, Y. Q. Gao, S. S. Wang, B. J. R. Thio, J. T. Hynes, X. S. Xie, and J. Cao, J. Phys. Chem. B, 117, p13378 (2013)
[2] Correlated Local Bending of a DNA Double Helix and Its Effect on DNA Flexibility in the Sub-Persistence-Length Regime. X. L. Xu, B. J. R. Thio, and J. Cao, J. Phys. Chem. Lett., 5, 2868 (2014)
[3] Michaelis-Menten Equation and Detailed Balance in Enzymatic Networks. J. Cao, J. Phys. Chem. B, 115, p5493 (2011)
[4] Generalized Michaelis-Menten Equation for Conformation-Modulated Monomeric Enzymes. J. Wu and J. Cao, Adv. Chem. Phys., 146, p329 (2011)
[5] Generic Schemes for Single-Molecule Kinetics. 1: Self-Consistent Pathway Solutions for Renewal Processes. J. Cao and R. J. Silbey, JPC B, 112, p12876 (2008)
[6] Universality of Poisson Indicator and Fano Factor of Transport Event Statistics in Ion Channels and Enzyme Kinetics. S. Chaudhury, J. Cao, and N. A. Sinitsyn, J. Phys. Chem. B, 117, p503 (2013) 13:45-14:30

C.-B. Li, "The Roles of ATP Hydrolysis Revealed by Single Molecule Time Series Analysis of Rotary Fluctuations in F1-ATPase"

F1-ATPase is a rotary motor protein that can efficiently convert chemical energy to mechanical work of rotation via fine coordination of its conformational motions and reaction sequences. In compared with reactant binding and product release, the ATP hydrolysis has relatively little contributions to the torque and chemical energy generation. To scrutinize possible roles of ATP hydrolysis, we extract the detailed statistics of the catalytic dwells in terms of change point and clustering analysis from high-speed single wild-type F1 observations, and detect a small rotation during the catalytic dwell triggered by the ATP hydrolysis that is indiscernible in previous studies. Moreover, we found in freely rotating F1 that ATP hydrolysis is followed by the release of inorganic phosphate (Pi) with low synthesis rates. We then propose functional roles of the ATP hydrolysis as a key to kinetically unlock the subsequent Pi-release and promote the correct reaction order.

[1] C.-B. Li et al., “ATP hydrolysis assists Phosphate release and promotes reaction order in F1-ATPase”, to be submitted, 2015 (preprint available on request)
[2] C.-B. Li et al., “Handling Noisy Data from Single Molecule Experiments”, Biophysics, 54, 257 (2014)
[3] J.N. Taylor et al., “Error-based Extraction of States and Energy Landscapes from Experimental Single-Molecule Time-Series”, Sci. Rep., 5, 9174 (2015)

T. Komatsuzaki, "Energy Landscapes learned from Single-Molecule Time-Series: Dimensionality and Disconnectivity Graph"

Energy landscapes are among the most pervasive concepts in understanding macromolecular functions such as folding and molecular recognition. Mapping experimental single-molecule trajectories to energy landscapes has been one of the most intriguing subjects, that is, how many number of microstates exists, how these microstates group together with each other, especially under a given timescale, the constraint of finite sample data points and experimental noise inherent to the measurement.
We present a method to extract the underlying effective free energy landscape from experimental single-molecule time-series taking into account empirical error arising from photon statistics and the finite sampling of the time-series. The core of the method is the application of rate-distortion theory from information theory, allowing the individual data points to be assigned to multiple states simultaneously.
With presenting its application to experimental fluorescence resonance energy transfer trajectories obtained from isolated agonist binding domains (ABD) of the AMPA receptor, I would like to address what the open problems are to be resolved in the next.

[1] J. N. Taylor, C.-B. Li, D. Cooper, C. F. Landes, T. Komatsuzaki Error-based Extraction of States and Energy Landscapes from Experimental Single-Molecule Time-Series Scientific Reports 5, pp.9174-1-9174-9 (2015)
[2] Akinori Baba, Tamiki Komatsuzaki Extracting the Underlying Effective Free Energy Landscape From Single- Molecule Time Series --- Local Equilibrium States and their Network Physical Chemistry Chemical Physics 13(4), pp.1395-1406 (2011)
[3] Akinori Baba, Tamiki Komatsuzaki Construction of effective free energy landscape from single molecule time series Proceedings of National Academy of Sciences USA 104(49), pp.19297-19302 (2007)


D. Makarov, "Multidimensional effects in single-molecule force spectroscopy"

In single-molecule force spectroscopy studies, molecular transitions (e.g. protein folding/unfolding, stepping of a molecular motor but, more recently, also scission of a covalent bond) are induced and/or monitored using an instrument that holds an individual molecule under controllable tension and measures the molecular extension as a function of time. The dynamics of the extension is most commonly modeled as a one-dimensional diffusive process and the force dependence of the transition rates is interpreted in terms of one-dimensional Kramers-type theory. Despite the high dimensionality of the underlying molecular processes, this one-dimensional model has been viewed as overwhelmingly successful in the past two decades. In my talk I would like to discuss the following questions:
1. Why is the one-dimensional model so successful?
2. What qualitative effects are missing in the 1D model?
3. Are there any examples of mechanical processes that, fundamentally, cannot be modeled by 1D models?
4. What are the experimental manifestations of high dimensionality in single-molecule pulling studies?
An additional question I would like to address is this:
5. Does single-molecule force spectroscopy probe intrinsic molecular properties or do instrumental effects (such as the hydrodynamic drag on the force probe) dominate experimental signals?

F. Noe, "Markov models from molecular simulation data at multiple thermodynamic states "

I will present novel methods based on Markov modeling for extracting statistical information (thermodynamics and kinetics) from molecular simulation data that has been generated at multiple thermodynamic states. Such data may be obtained from enhanced sampling protocols, such as umbrella sampling or replica-exchange dynamics, and by mixing one of these protocols with direct molecular dynamics data. Here I will propose ways to optimally extract information from such data, including the reconstruction of the kinetics of rare events that are not directly sampled in the data. An application of our approach is the estimation of rare unbinding kinetics of protein-ligand complexes when only the more frequent binding process can be sampled in direct MD simulations.


R. Hernandez, "ASMD for Protein Energetics: Advances & Applications"

The behavior and function of proteins necessarily occurs during nonequilibrium conditions such as when a protein unfolds or binds. The need to treat both the dynamics and the high-dimensionality of proteins and their environments presents significant challenges to theoretical or computational methods. The present work attempts to reign in this complexity by way of capturing the dominant energetic pathway in a particular protein motion. In particular, the energetics of an unfolding event can be formally obtained using steered molecular dynamics (SMD) and Jarzynski’s inequality but the cost of the calculation increases dramatically with the length of the pathway. An adaptive algorithm has been introduced that allows for this pathway to be nonlinear and staged while reducing the computational cost. [J. Chem. Theory Comput. 6, 3026-3038 (2010)] It has been verified using decaalanine in water [J. Chem. Phys. 136, 215104 (2012)] and extended to MB-ASMD for cases in which multiple distinct pathways contribute significantly. [J. Chem. Phys. 141, 064101 (2014)] The PMF for stretching decaalanine at 300K in explicit water solvent (using the TIP3P water potential) [J. Chem. Theory Comput. 8, 4837 (2012)] and in implicit water solvent [PLoS ONE 10, e0127034 (2015)] has also been obtained through ASMD. Not surprisingly, the stabilization from the water solvent reduces the overall work required to unfold it. However, the PMF remains structured suggesting that some regions of the energy landscape act partially as doorways. This is also further verified through a study of the hydrogen-bond breaking and formation along the stretching paths of decaalanine in vacuum and solvent. Perhaps surprisingly, the implicit water solvent gives rise to similar pathway even though it does not explicitly account for structure.

J. Das, "Limiting energy dissipation induces glassy kinetics in single cell high precision responses"

Single cells often generate precise responses by involving dissipative out-of-thermodynamic equilibrium processes in signaling networks. The available free energy to fuel these processes could become limited depending on the metabolic state of an individual cell. How does limiting dissipation affect the kinetics of high precision responses in single cells? I address this question in the context of a kinetic proofreading scheme used in a simple model of early time T cell signaling. I show using exact analytical calculations and numerical simulations that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-tocell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and, ergodicity breaking. Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, create a fundamental difficulty in interpreting single cell kinetics from cell population level results found in the literature regarding the connection between high precision responses and dissipation.


S. Sivasankar, "Biological ice-nine: resolving the structural conversion and templated aggregation of prion proteins at the single molecule level"

In Kurt Vonnegut’s Cat’s Cradle, the physicist Felix Hoenikker creates ice-nine, a highly stable form of crystalline water that seeds its own replication and instantly freezes any liquid water it touches. Vonnegut’s fictitious ice-nine has a very real biological counterpart: the prion protein. When a prion protein misfolds, it imposes its structure upon natively folded proteins and templates their aggregation. The consequence of this self-amplifying cycle is an accumulation of toxic prion protein aggregates that destroys neurons and invariably kill the organism. It has been proposed that Cu2+ ions play an important role in prion protein misfolding, however direct proof of the role of Cu2+ in triggering the structural conversion of prion proteins is lacking. Here we resolve for the first time and with single molecule precision, the role of Cu2+ ions in prion protein misfolding and templated oligomerization. Using a single molecule fluorescence assay, we demonstrate that individual prion protein monomers misfold before oligomer assembly; Cu2+ ions are obligatory for this conformational switching. Using single molecule force measurements with an Atomic Force Microscope (AFM), we show that the misfolded conformation has a significantly higher association constant compared to the native conformation; the high affinity of the misfolded conformation promotes its oligomerization. Finally, using a cell-free seeding assay, we demonstrate that misfolded monomers template prion structural conversion and aggregation. Since metal imbalance is one of the clinical features of prion disease, our data suggests that Cu2+ ions promote the structural instability of cellular prion proteins and increases their propensity to oligomerize.

S. Iyer-Biswas, "Universality in stochastic single-cell dynamics"

There has been a longstanding quest to uncover the quantitative laws governing the stochastic growth and division of individual cells. While great strides have been made in unravelling and modeling the details of the gene regulatory networks which dictate growth and division for different organisms, there is a regrettable paucity of quantitative physical laws derived from the complementary “top down” perspective. Introducing the unique combination of technologies that facilitated probing these stochastic cellular dynamics with unprecedented precision, I will first summarize my previous findings, namely, the "scaling laws" that govern fluctuations in growth and division of individual cells under steady-state growth conditions. Taking a minimalist perspective, I will argue for how these scaling laws reveal an elegant physical principle governing these complex biological processes: a single cellular unit of time, which scales with external conditions, governs all aspects of stochastic cell growth and division at a given condition. I will then focus on applications of the technology to probe more complex growth conditions, the corresponding generalizations of the physical principle, and the implications for the underlying biological systems design.

[1] S. Iyer-Biswas et-al, PNAS 111, 15912 (2014)
[2] S. Iyer-Biswas et-al, PRL 113, 028101 (2014)

S. Pressé, "Enzymes Stepping on Landmines"

Enzymes are proteins that are responsible for catalyzing reactions in living systems. Some enzymes perform reactions so exothermic that their own self-generated heat would be sufficient to unfold over a million small proteins per second. How do enzymes then cope with this heat? Here we will discuss recent work where we have shown that enzymes rapidly dissipate heat by accelerating their center of mass.
I will also discuss a recent method for addressing the 'single molecule counting problem'. Here is the problem: there is currently no routine way to determine how many proteins of type X, say, are in a given complex in living cells. A quantitative characterization of protein-protein interactions is an essential prerequisite for developing a mechanistic understanding of cell biology and the disease states associated with defective protein complexes. Nonetheless, characterizing protein assemblies -- as they occur in their native cellular environment -- is a major challenge since these assemblies can involve up to many tens of proteins within approximately a 10nm range. Here we will show a method we’ve developed that shows promise in characterizing protein complexes in living cells by using state-of-the-art (superresolution) data already available.

[1] C. Riedel, R. Gabizon, C. A. M.Wilson, K. Hamadani, K. Tsekouras, S. Marqusee, S. Pressé & C. Bustamante, Nature, 2014, 517, 227-230.
[2] G. C. Rollins, J. Y. Shin, C. Bustamante and S. Pressé, PNAS, 2014, 112, 2, E110–E118.

K.Ghosh, "Biophysical Proteome: limits, evolution and universality"

Protein sequence encodes complex network of interactions and it is difficult to decipher simple rules in protein science. In spite of this challenge, semi-empirical, approximate rules can be found to describe biophysical properties of different proteins. Using principles of polymer physics and statistical mechanics, our goal is to unravel such approximate rules to quantitate physicochemical properties of proteins based on sequence and/or structure information. Our next goal is to extend these transferrable laws in a high throughput manner -- due to analytical nature of the approach -- to model the entire collection of proteins inside an organism, called the proteome. The application at the proteome level allows us to bridge the gap between molecular biophysics and cellular phenotype. With this approach we will try to address some questions of broad interest: i) Why are cells so sensitive to temperature ? ii) How do thermophilic proteins -- derived from organisms that thrive at high temperature -- withstand high temperatures compared to their mesophilic counterparts ? iii) What is the evolutionary implication of distribution of different rate processes in the proteome ?

[1]Proteome folding kinetics is limited by protein halflife. T. Zou, N. Williams, S.B. Ozkan and K. Ghosh PLOS ONE: DOI: 10.1371/journal.pone.0112701 (2014).
[2]Why and how does native topology dictate the folding speed of a protein? M. Rustad and K. Ghosh J. Chem. Phys. 137:205104 (2012).
[3]Physical limits of cells and proteomes. K. A. Dill, K. Ghosh, J. Schmit Proc. Natl. Acad. Sci. 108: 17876 (2011).
[4]How do thermophilic proteins and proteomes withstand high temperature ? L. Sawle, K. Ghosh Biophys J 101, 217 (2011).

A. Prasad, "Complexity and Modularity in Biology"

It has long been believed that the non-trivial properties of biological organisms are emergent properties of complex networks. This is true of the simplest living object, the cell, whose life activities are controlled by an intricate set of protein-protein interactions. As a consequence the protein-protein interaction network has been studied extensively for its properties, in the hope that these would help discover fundamentals principles. One such property is believed to be modularity, which suggests that the complex protein network in cells can be decomposed into semi-independent functional modules, often specialized for a particular function. In this talk we examine modularity from two angles. In the first, inspired by some previous work, we look at the consequences of downstream connections on the dynamics of an important non-linear module, the biological switch. We show that the dynamics of the switch are in fact very strongly affected by the mere act of connecting it to a downstream network, and speculate on how biology would deal with this problem. In the second part of my talk, I use information theory to study a common network motif found in signal transduction to suggest that signal transduction may be intrinsically less modular than what is commonly believed. Both cases represent some limits of modularity in protein networks, that appear to be significant for biological function and its evolution.

[1] Lyons, S. M., Xu, W., Medford, J., & Prasad, A. (2014). Loads bias genetic and signaling switches in synthetic and natural systems. PLOS Computational Biology, 10(3), e1003533.
[2] Lyons, S. M., & Prasad, A. (2012). Cross-talk and information transfer in mammalian and bacterial signaling. Plos One, 7(4), e34488.

K. Tsekouras, "Novel methoods of inference from FCS and photobleaching experiments"

We report on two new methods intended to extract information from experiments probing protein diffusion in the cell nucleus via FCS, and from photobleaching experiments.
For the first, we develop a method based on MaxEnt that can be applied to FCS data from fluorophore-tagged proteins diffusing in the cell’s complex environment. Although the FCS curves are often fit to anomalous diffusion models, we propose a biologically motivated alternative to explain how apparent anomalous diffusion arises in the cell. From our method we extract diffusion coefficient distributions which in turn let us determine how molecular crowding, fluorophore artifacts and affinity site binding contribute to the apparent anomalous behavior. We validate our method using actual experimental data from red fluorescent protein-tagged BZip transcription factor protein domains as they diffuse within different cellular environments.
Next, photobleaching event counting is widely used to determine stoichiometry of protein complexes that are critical to cell function. However, despite its importance, currently photobleaching event counting is limited to 30 events at most, though typically around 5. Here we present a novel method based on the Bayesian Information Criterion to reliably and efficiently count up to hundreds of photobleaching events. We benchmark our method on synthetic data, showing that it can also account for overlapping and fluorophore reactivation events. We also apply our method to real experimental data from a variety of sources. We discuss the robustness of our method even for low signal-to-noise ratios.

[1] K. Tsekouras, A. P. Siegel, R. N. Day and S. Press´e, Biophys. J., 2015.

N. Scherer, "Insights into the Subordinated Random Walk of Insulin Granules in Beta Cells"

A few years ago we discovered that a new type of statistic, a Subordinated Random Walk, described the dynamics (transport) of single insulin granules in analogs of pancreatic beta cells. [Tabi etal., PNAS, 110, 4911, 2013] This statistic consists of and combines a space-oriented mechanism, known as fractional Brownian motion (fBM), with a time-oriented mechansim, Continuous Time Random Walk (CTRW). That is, when tested with suitable order parameters or observations both statistics were shown to be operative. This talk will take some first steps in unraveling the underlying causes (molecular mechanisms) that can give rise to the aforementioned statistics. Some ongoing and future challenges will also be touched as time permits.

Tabi etal., PNAS, 110, 4911, 2013
Burov etal., PNAS, 110, 49, 19689, 2013
Johanna L. Miller, Physics Today, 67(2), 17, 2014

A. Tokmakoff, "Visualizing protein conformational dynamics with 2D IR spectroscopy"

How we describe protein conformation and dynamics is colored by the methods we use to study them. Traditional structural and kinetic tools often lead to impressions of simple deterministic conformation changes between well-defined structures. The conformational fluctuations and disorder of proteins is more difficult to quantify. This presentation will describe an approach toward characterizing and quantifying structure and disorder and dynamics in proteins and peptides, using 2D IR spectroscopy of amide I vibrations, isotope labeling strategies, and computational modeling based on molecular dynamics simulations and Markov state models.

B.Munsky, "Predicting Spatiotemporal Fluctuations of Signal-Activated Gene Expression"

Spatial, temporal and stochastic fluctuations cause genetically identical cells to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, and makes predictive understanding all but impossible. However, if we examine cellular fluctuations more closely and match them to discrete stochastic analyses, we discover an untapped, yet powerful information resource [1]. In this talk, I will present our collaborative endeavors to integrate single-cell experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for Mitogen Activated Protein Kinase (MAPK) signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal (1-minute) and spatial (1-molecule) resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding, and how we design novel experiments to rapidly and systematically improve this understanding. I will illustrate the effectiveness of our integrated approach with the identification of predictive models for MAPK induction of transcription in yeast [2] and mammalian [3] systems.

[1] B. Munsky, G. Neuert and A. van Oudenaarden, Science, 2012, 336, 6078, 183--187.
[2] G. Neuert, B. Munsky, et al, Science, 2013, 339, 6119, 584-587.
[3] A. Senecal, B. Munsky, et al, Cell Reports, 2014, 8,1, 75-83.

M. Linden, "Model-based analysis and inverse crimes in live cell single particle tracking data"

Single particle tracking (SPT) in live cells is emerging as a quantitative and non-invasive tool for systems biology. A particularly promising direction is the possibility to monitor chemical reactions in vivo by exploiting the fact that small molecules diffuse slower than large ones, so that a fluorescently tagged ligand will change diffusion constant as it associates and dissociates from its binding partners. We have developed an analysis suite, vbspt.sourceforce.net (1), that uses a Bayesian treatment of hidden Markov models to learn the number of diffusive states and their interconversion rates from position trajectories of diffusing particles with random jumps in diffusion constant.
However, limitations in microscopy and fluorescence labeling techniques influence the resolution of the method, and live cells are more complex than the model assumptions used by vbSPT. The obvious approach to improved analysis is to make the underlying analysis model more realistic. It is then important to keep in mind that even complex models are only approximate, and that testing statistical methods on data that satisfy all assumptions of the analysis model, a practice known as "inverse crimes", are likely to give overly optimistic results that do not reflect the performance on real data.
To avoid this problem, and learn more about the limits of SPT as a tool for studying complex cellular pathways, we are currently developing computational tools to simulate live cell microscopy, using a combination of reaction-diffusion kinetics and photophysics models with experimentally accessible parameters. The simulations include more physical realism than the models on which vbSPT are based, and are thus suitable for benchmarking, optimizing experimental conditions, and improving the analysis methods.
I will give a brief introduction to single particle tracking, describe some of the physics of camera-based tracking that are relevant for the next generation of analysis tools, and show some preliminary results and lessons from our analysis of simulated data.

[1]Persson F*, Lindén M*, Unoson C, Elf J (2013) Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat Methods 10:265–269. * Equal contributions.

M. Toda, "Time-frequency approach to molecular dynamics simulation of proteins"

I will present our reseach toward time series data of molecular dynamics simulation of proteins using the wavelet transformation. After applying the wavelet transformation, we will introduce indeces to chacaterize collective motions. Then, we will see how multiple secondary structures exhibit correlation movement and how often secondary structures become cracked.

[1] M. Kamada, M. Toda, M. Sekijima, M. Takata, K. Joe, Chem. Phys. Lett., 2011, 502, 241-247.
[2] Y. Matsunaga, A. Baba, C. B. Li, J. E. Straub, M. Toda, T. Komatsuzaki and R. S. Berry, J. Chem. Phys., 2013, 139, 215101, 1-13.