Monte carlo simulations of enzyme reactions in two dimensions: fractal kinetics and spatial segregation

Fractal-like kinetics of intracellular enzymatic reactions: a chemical framework of endotoxin tolerance and a possible non-specific contribution of macromolecular crowding to cross-tolerance

BACKGROUND

The response to endotoxin (LPS), and subsequent signal transduction lead to the production of cytokines such as tumor necrosis factor-α (TNF-α) by innate immune cells. Cells or organisms pretreated with endotoxin enter into a transient state of hyporesponsiveness, referred to as endotoxin tolerance (ET) which represents a particular case of negative preconditioning. Despite recent progress in understanding the molecular basis of ET, there is no consensus yet on the primary mechanism responsible for ET and for the more complex cases of cross tolerance. In this study, we examined the consequences of the macromolecular crowding (MMC) and of fractal-like kinetics (FLK) of intracellular enzymatic reactions on the LPS signaling machinery. We hypothesized that this particular type of enzyme kinetics may explain the development of ET phenomenon.

METHOD

Our aim in the present study was to characterize the chemical kinetics framework in ET and determine whether fractal-like kinetics explains, at least in part, ET. We developed an ordinary differential equations (ODE) mathematical model that took into account the links between the MMC and the LPS signaling machinery leading to ET. We proposed that the intracellular fractal environment (MMC) contributes to ET and developed two mathematical models of enzyme kinetics: one based on Kopelman's fractal-like kinetics framework and the other based on Savageau's power law model.

RESULTS

Kopelman's model provides a good image of the potential influence of a fractal intracellular environment (MMC) on ET. The Savageau power law model also partially explains ET. The computer simulations supported the hypothesis that MMC and FLK may play a role in ET.

CONCLUSION

The model highlights the links between the organization of the intracellular environment, MMC and the LPS signaling machinery leading to ET. Our FLK-based model does not minimize the role of the numerous negative regulatory factors. It simply draws attention to the fact that macromolecular crowding can contribute significantly to the induction of ET by imposing geometric constrains and a particular chemical kinetic for the intracellular reactions.

A physiologically-based flow network model for hepatic drug elimination II: variable lattice lobule models

We extend a physiologically-based lattice model for the transport and metabolism of drugs in the liver lobule (liver functional unit) to consider structural and spatial variability. We compare predicted drug concentration levels observed exiting the lobule with their detailed distribution inside the lobule, and indicate the role that structural variation has on these results. Liver zonation and its role on drug metabolism represent another aspect of structural inhomogeneity that we consider here. Since various liver diseases can be thought to produce such structural variations, our analysis gives insight into the role of disease on liver function and performance. These conclusions are based on the dominant role of convection in well-vascularized tissue with a given structure.

Systems-level modeling with molecular resolution elucidates the rate-limiting mechanisms of cellulose decomposition by cellobiohydrolases

Interprotein and enzyme-substrate couplings in interfacial biocatalysis induce spatial correlations beyond the capabilities of classical mass-action principles in modeling reaction kinetics. To understand the impact of spatial constraints on enzyme kinetics, we developed a computational scheme to simulate the reaction network of enzymes with the structures of individual proteins and substrate molecules explicitly resolved in the three-dimensional space. This methodology was applied to elucidate the rate-limiting mechanisms of crystalline cellulose decomposition by cellobiohydrolases. We illustrate that the primary bottlenecks are slow complexation of glucan chains into the enzyme active site and excessive enzyme jamming along the crowded substrate. Jamming could be alleviated by increasing the decomplexation rate constant but at the expense of reduced processivity. We demonstrate that enhancing the apparent reaction rate required a subtle balance between accelerating the complexation driving force and simultaneously avoiding enzyme jamming. Via a spatiotemporal systems analysis, we developed a unified mechanistic framework that delineates the experimental conditions under which different sets of rate-limiting behaviors emerge. We found that optimization of the complexation-exchange kinetics is critical for overcoming the barriers imposed by interfacial confinement and accelerating the apparent rate of enzymatic cellulose decomposition.

Kinetics of diffusion-mediated DNA hybridization in lipid monolayer films determined by single-molecule fluorescence spectroscopy

We use single-molecule fluorescence microscopy to monitor individual hybridization reactions between membrane-anchored DNA strands, occurring in nanofluidic lipid monolayer films deposited on Teflon AF substrates. The DNA molecules are labeled with different fluorescent dyes, which make it possible to simultaneously monitor the movements of two different molecular species, thus enabling tracking of both reactants and products. We employ lattice diffusion simulations to determine reaction probabilities upon interaction. The observed hybridization rate of the 40-mer DNA was more than 2-fold higher than that of the 20-mer DNA. Since the lateral diffusion coefficient of the two different constructs is nearly identical, the effective molecule radius determines the overall kinetics. This implies that when two DNA molecules approach each other, hydrogen bonding takes place distal from the place where the DNA is anchored to the surface. Strand closure then propagates bidirectionally through a zipper-like mechanism, eventually bringing the lipid anchors together. Comparison with hybridization rates for corresponding DNA sequences in solution reveals that hybridization rates are lower for the lipid-anchored strands and that the dependence on strand length is stronger.

Anomalous diffusion and multifractional Brownian motion: simulating molecular crowding and physical obstacles in systems biology

There have been many recent studies from both experimental and simulation perspectives in order to understand the effects of spatial crowding in molecular biology. These effects manifest themselves in protein organisation on the plasma membrane, on chemical signalling within the cell and in gene regulation. Simulations are usually done with lattice- or meshless-based random walks but insights can also be gained through the computation of the underlying probability density functions of these stochastic processes. Until recently much of the focus had been on continuous time random walks, but some very recent work has suggested that fractional Brownian motion may be a good descriptor of spatial crowding effects in some cases. The study compares both fractional Brownian motion and continuous time random walks and highlights how well they can represent different types of spatial crowding and physical obstacles. Simulated spatial data, mimicking experimental data, was first generated by using the package Smoldyn. We then attempted to characterise this data through continuous time anomalously diffusing random walks and multifractional Brownian motion (MFBM) by obtaining MFBM paths that match the statistical properties of our sample data. Although diffusion around immovable obstacles can be reasonably characterised by a single Hurst exponent, we find that diffusion in a crowded environment seems to exhibit multifractional properties in the form of a different short- and long-time behaviour.

Receptor clustering affects signal transduction at the membrane level in the reaction-limited regime

Many types of membrane receptors are found to be organized as clusters on the cell surface. We investigate the potential effect of such receptor clustering on the intracellular signal transduction stage. We consider a canonical pathway with a membrane receptor (R) activating a membrane-bound intracellular relay protein (G). We use Monte Carlo simulations to recreate biochemical reactions using different receptor spatial distributions and explore the dynamics of the signal transduction. Results show that activation of G by R is severely impaired by R clustering, leading to an apparent blunted biological effect compared to control. Paradoxically, this clustering decreases the half maximal effective dose (ED50) of the transduction stage, increasing the apparent affinity. We study an example of inter-receptor interaction in order to account for possible compensatory effects of clustering and observe the parameter range in which such interactions slightly counterbalance the loss of activation of G. The membrane receptors' spatial distribution affects the internal stages of signal amplification, suggesting a functional role for membrane domains and receptor clustering independently of proximity-induced receptor-receptor interactions.

Ras GTPase activating (RasGAP) activity of the dual specificity GAP protein Rasal requires colocalization and C2 domain binding to lipid membranes

Rasal, belonging to the GAP1 subfamily of Ras GTPase-activating proteins (RasGAPs) with dual RasGAP/RapGAP specificity, is epigenetically silenced in several tumor types. Surprisingly, the isolated protein has GAP activity on Rap but not on Ras. Its membrane recruitment is regulated by interaction with calcium and lipids, which simultaneously induces its RasGAP activity through a yet unknown mechanism. Here we show that the interaction of Rasal with membranes induces Rasal RasGAP activity by spatial and conformational regulation, although it does not have any effect on its RapGAP activity. Not only is colocalization of Rasal and Ras in the membrane essential for RasGAP activation, but direct and Ca-dependent interaction between the tandem C2 domains of Rasal and lipids of the membrane is also required. Whereas the C2A domain binds specifically phosphatidylserine, the C2B domain interacts with several phosphoinositol lipids. Finally we show, that similar to the C2 domains of synaptotagmins, the Rasal tandem C2 domains are able to sense and induce membrane curvature by the insertion of hydrophobic loops into the membrane.

Membrane microdomains emergence through non-homogeneous diffusion

Background

In the classical view, cell membrane proteins undergo isotropic random motion, that is a 2D Brownian diffusion that should result in an homogeneous distribution of concentration. It is, however, far from the reality: Membrane proteins can assemble into so-called microdomains (sometimes called lipid rafts) which also display a specific lipid composition. We propose a simple mechanism that is able to explain the colocalization of protein and lipid rafts.

Results

Using very simple mathematical models and particle simulations, we show that a variation of membrane viscosity directly leads to variation of the local concentration of diffusive particles. Since specific lipid phases in the membrane can account for diffusion variation, we show that, in such a situation, the freely diffusing proteins (or any other component) still undergo a Brownian motion but concentrate in areas of lower diffusion. The amount of this so-called overconcentration at equilibrium issimply related to the ratio of diffusion coefficients between zones of high and low diffusion. Expanding the model to include particle interaction, we show that inhomogeneous diffusion can impact particles clusterization as well. The clusters of particles were more numerous and appear for a lower value of interaction strength in the zones of low diffusion compared to zones of high diffusion.

Conclusion

Provided we assume stable viscosity heterogeneity in the membrane, our model propose a simple mechanism to explain particle concentration heterogeneity. It has also a non-trivial impact on density of particles when interaction is added. This could potentially have an impact on membrane chemical reactions and oligomerization.

Multi-Scale Continuum Modeling of Biological Processes: From Molecular Electro-Diffusion to Sub-Cellular Signaling Transduction

This article provides a brief review of multi-scale modeling at the molecular to cellular scale, with new results for heart muscle cells. A finite element-based simulation package (SMOL) was used to investigate the signaling transduction at molecular and sub-cellular scales (http://mccammon.ucsd.edu/smol/, http://FETK.org) by numerical solution of time-dependent Smoluchowski equations and a reaction-diffusion system. At the molecular scale, SMOL has yielded experimentally-validated estimates of the diffusion-limited association rates for the binding of acetylcholine to mouse acetylcholinesterase using crystallographic structural data. The predicted rate constants exhibit increasingly delayed steady-state times with increasing ionic strength and demonstrate the role of an enzyme's electrostatic potential in influencing ligand binding. At the sub-cellular scale, an extension of SMOL solves a non-linear, reaction-diffusion system describing Ca²⁺ ligand buffering and diffusion in experimentally-derived rodent ventricular myocyte geometries. Results reveal the important role for mobile and stationary Ca²⁺ buffers, including Ca²⁺ indicator dye. We found that the alterations in Ca²⁺-binding and dissociation rates of troponin C (TnC) and total TnC concentration modulate subcellular Ca²⁺ signals. Model predicts that reduced off-rate in whole troponin complex (TnC, TnI, TnT) versus reconstructed thin filaments (Tn, Tm, actin) alters cytosolic Ca²⁺ dynamics under control conditions or in disease-linked TnC mutations. The ultimate goal of these studies is to develop scalable methods and theories for integration of molecular-scale information into simulations of cellular-scale systems.

Barriers to diffusion in dendrites and estimation of calcium spread following synaptic inputs

The motion of ions, molecules or proteins in dendrites is restricted by cytoplasmic obstacles such as organelles, microtubules and actin network. To account for molecular crowding, we study the effect of diffusion barriers on local calcium spread in a dendrite. We first present a model based on a dimension reduction approach to approximate a three dimensional diffusion in a cylindrical dendrite by a one-dimensional effective diffusion process. By comparing uncaging experiments of an inert dye in a spiny dendrite and in a thin glass tube, we quantify the change in diffusion constants due to molecular crowding as D(cyto)/D(water) = 1/20. We validate our approach by reconstructing the uncaging experiments using Brownian simulations in a realistic 3D model dendrite. Finally, we construct a reduced reaction-diffusion equation to model calcium spread in a dendrite under the presence of additional buffers, pumps and synaptic input. We find that for moderate crowding, calcium dynamics is mainly regulated by the buffer concentration, but not by the cytoplasmic crowding, dendritic spines or synaptic inputs. Following high frequency stimulations, we predict that calcium spread in dendrites is limited to small microdomains of the order of a few microns (<5 μm).

Physiological environment induces quick response - slow exhaustion reactions

In vivo environments are highly crowded and inhomogeneous, which may affect reaction processes in cells. In this study we examined the effects of intracellular crowding and an inhomogeneity on the behavior of in vivo reactions by calculating the spectral dimension (d(s)), which can be translated into the reaction rate function. We compared estimates of anomaly parameters obtained from fluorescence correlation spectroscopy (FCS) data with fractal dimensions derived from transmission electron microscopy (TEM) image analysis. FCS analysis indicated that the anomalous property was linked to physiological structure. Subsequent TEM analysis provided an in vivo illustration; soluble molecules likely percolate between intracellular clusters, which are constructed in a self-organizing manner. We estimated a cytoplasmic spectral dimension d(s) to be 1.39 ± 0.084. This result suggests that in vivo reactions initially run faster than the same reactions in a homogeneous space; this conclusion is consistent with the anomalous character indicated by FCS analysis. We further showed that these results were compatible with our Monte-Carlo simulation in which the anomalous behavior of mobile molecules correlates with the intracellular environment, leading to description as a percolation cluster, as demonstrated using TEM analysis. We confirmed by the simulation that the above-mentioned in vivo like properties are different from those of homogeneously concentrated environments. Additionally, simulation results indicated that crowding level of an environment might affect diffusion rate of reactant. Such knowledge of the spatial information enables us to construct realistic models for in vivo diffusion and reaction systems.

Fractal symmetry of protein interior: what have we learned?

The application of fractal dimension-based constructs to probe the protein interior dates back to the development of the concept of fractal dimension itself. Numerous approaches have been tried and tested over a course of (almost) 30 years with the aim of elucidating the various facets of symmetry of self-similarity prevalent in the protein interior. In the last 5 years especially, there has been a startling upsurge of research that innovatively stretches the limits of fractal-based studies to present an array of unexpected results on the biophysical properties of protein interior. In this article, we introduce readers to the fundamentals of fractals, reviewing the commonality (and the lack of it) between these approaches before exploring the patterns in the results that they produced. Clustering the approaches in major schools of protein self-similarity studies, we describe the evolution of fractal dimension-based methodologies. The genealogy of approaches (and results) presented here portrays a clear picture of the contemporary state of fractal-based studies in the context of the protein interior. To underline the utility of fractal dimension-based measures further, we have performed a correlation dimension analysis on all of the available non-redundant protein structures, both at the level of an individual protein and at the level of structural domains. In this investigation, we were able to separately quantify the self-similar symmetries in spatial correlation patterns amongst peptide-dipole units, charged amino acids, residues with the π-electron cloud and hydrophobic amino acids. The results revealed that electrostatic environments in the interiors of proteins belonging to 'α/α toroid' (all-α class) and 'PLP-dependent transferase-like' domains (α/β class) are highly conducive. In contrast, the interiors of 'zinc finger design' ('designed proteins') and 'knottins' ('small proteins') were identified as folds with the least conducive electrostatic environments. The fold 'conotoxins' (peptides) could be unambiguously identified as one type with the least stability. The same analyses revealed that peptide-dipoles in the α/β class of proteins, in general, are more correlated to each other than are the peptide-dipoles in proteins belonging to the all-α class. Highly favorable electrostatic milieu in the interiors of TIM-barrel, α/β-hydrolase structures could explain their remarkably conserved (evolutionary) stability from a new light. Finally, we point out certain inherent limitations of fractal constructs before attempting to identify the areas and problems where the implementation of fractal dimension-based constructs can be of paramount help to unearth latent information on protein structural properties.

Challenges in determining anomalous diffusion in crowded fluids

Anomalous diffusion in crowded fluids, e.g. in the cytoplasm of living cells, is a frequent phenomenon. Despite manifold observations of anomalous diffusion with several experimental techniques, a thorough understanding of the underlying microscopic causes is still lacking. Here, we have quantitatively compared two popular techniques with which anomalous diffusion is typically assessed. Using extensive computer simulations of two prototypical random walks with stationary increments, i.e. fractional brownian motion and obstructed diffusion, we find that single particle tracking (SPT) yields results for the diffusion anomaly that are equivalent to those obtained by fluorescence correlation spectroscopy (FCS). We also show that positional uncertainties, inherent to SPT experiments, lead to a systematic underestimation of the diffusion anomaly, regardless of the underlying random walk and measurement technique. This effect becomes particularly relevant when the position uncertainty is larger than the average positional displacement between two successive frames.

Subdiffusion of a sticky particle on a surface

Conventional diffusion (ΔR2(t))=2Dt gives way to subdiffusion (ΔR2(t))∼t(μ), 0<μ<1 when the waiting time distribution φ(τ) is nonintegrable. We have studied a model system, colloidal particles functionalized with DNA "sticky ends" diffusing on a complementary coated surface. We observe a crossover from subdiffusive to conventional behavior for (ΔR2(t)) and φ(τ) as temperature is increased near the particle-surface melting temperature consistent with a simple Gaussian distribution of sticky ends. Our results suggest that any system with randomness in its binding energy should exhibit subdiffusive behavior as it unbinds. This will strongly affect the kinetics of self-assembly.

Dynamics of enzymatic digestion of elastic fibers and networks under tension

We study the enzymatic degradation of an elastic fiber under tension using an anisotropic random-walk model coupled with binding-unbinding reactions that weaken the fiber. The fiber is represented by a chain of elastic springs in series along which enzyme molecules can diffuse. Numerical simulations show that the fiber stiffness decreases exponentially with two distinct regimes. The time constant of the first regime decreases with increasing tension. Using a mean field calculation, we partition the time constant into geometrical, chemical and externally controllable factors, which is corroborated by the simulations. We incorporate the fiber model into a multiscale network model of the extracellular matrix and find that network effects do not mask the exponential decay of stiffness at the fiber level. To test these predictions, we measure the force relaxation of elastin sheets stretched to 20% uniaxial strain in the presence of elastase. The decay of force is exponential and the time constant is proportional to the inverse of enzyme concentration in agreement with model predictions. Furthermore, the fragment mass released into the bath during digestion is linearly related to enzyme concentration that is also borne out in the model. We conclude that in the complex extracellular matrix, feedback between the local rate of fiber digestion and the force the fiber carries acts to attenuate any spatial heterogeneity of digestion such that molecular processes manifest directly at the macroscale. Our findings can help better understand remodeling processes during development or in disease in which enzyme concentrations and/or mechanical forces become abnormal.

Validation of fractal-like kinetic models by time-resolved binding kinetics of dansylamide and carbonic anhydrase in crowded media

Kinetic studies of biochemical reactions are typically carried out in a dilute solution that rarely contains anything more than reactants, products, and buffers. In such studies, mass-action-based kinetic models are used to analyze the progress curves. However, intracellular compartments are crowded by macromolecules. Therefore, we investigated the adequacy of the proposed generalizations of the mass-action model, which are meant to describe reactions in crowded media. To validate these models, we measured time-resolved kinetics for dansylamide binding to carbonic anhydrase in solutions crowded with polyethylene glycol and Ficoll. The measured progress curves clearly show the effects of crowding. The fractal-like model proposed by Savageau was used to fit these curves. In this model, the association rate coefficient k(a) allometrically depends on concentrations of reactants. We also considered the fractal kinetic model proposed by Schnell and Turner, in which k(a) depends on time according to a Zipf-Mandelbrot distribution, and some generalizations of these models. We found that the generalization of the mass-action model, in which association and dissociation rate coefficients are concentration-dependent, represents the preferred model. Other models based on time-dependent rate coefficients were inadequate or not preferred by model selection criteria.

Impact of receptor clustering on ligand binding

Background

Cellular response to changes in the concentration of different chemical species in the extracellular medium is induced by ligand binding to dedicated transmembrane receptors. Receptor density, distribution, and clustering may be key spatial features that influence effective and proper physical and biochemical cellular responses to many regulatory signals. Classical equations describing this kind of binding kinetics assume the distributions of interacting species to be homogeneous, neglecting by doing so the impact of clustering. As there is experimental evidence that receptors tend to group in clusters inside membrane domains, we investigated the effects of receptor clustering on cellular receptor ligand binding.

Results

We implemented a model of receptor binding using a Monte-Carlo algorithm to simulate ligand diffusion and binding. In some simple cases, analytic solutions for binding equilibrium of ligand on clusters of receptors are provided, and supported by simulation results. Our simulations show that the so-called "apparent" affinity of the ligand for the receptor decreases with clustering although the microscopic affinity remains constant.

Conclusions

Changing membrane receptors clustering could be a simple mechanism that allows cells to change and adapt its affinity/sensitivity toward a given stimulus.

Novel mathematical models for cell-mediated cytotoxicity assays without applying enzyme kinetics but with combinations and probability: bystanders in bulk effector cells influence results of cell-mediated cytotoxicity assays

Cell-mediated cytotoxicity assays are widely implemented to evaluate cell-mediated cytotoxic activity, and some assays are analyzed using the analogy of enzyme kinetics. In the analogy, the effector cell is regarded as the enzyme, the target cell as the substrate, the effector cell-target cell conjugate as the enzyme-substrate complex and the dead target cell as the product. However, the assumptions analogous to those of enzyme kinetics are not always true in cell-mediated cytotoxicity assays, and the parameter analogous to the Michaelis-Menten constant is not constant but is dependent on the number of effector cells. Therefore I present novel mathematical models for cell-mediated cytotoxicity assays without applying enzyme kinetics. I instead use combinations and probability, because analysis of cell-mediated cytotoxicity assays by applying enzyme kinetics seems controversial. With my original models, I demonstrate simulations of the data in previously published papers. The results are exhibited in the same forms as the corresponding data. Comparing the simulation results with the published data, the results seem to agree well with the data. From simulations of cytotoxic assays with bulk effector cells, it appears that bystanders in bulk effector cells increase both the cytotoxic activity and the motility of effector cells.

Monte Carlo simulations of single- and multistep enzyme-catalyzed reaction sequences: effects of diffusion, cell size, enzyme fluctuations, colocalization, and segregation

Following the discovery of slow fluctuations in the catalytic activity of an enzyme in single-molecule experiments, it has been shown that the classical Michaelis-Menten (MM) equation relating the average enzymatic velocity and the substrate concentration may hold even for slowly fluctuating enzymes. In many cases, the average velocity is that given by the MM equation with time-averaged values of the fluctuating rate constants and the effect of enzyme fluctuations is simply averaged out. The situation is quite different for a sequence of reactions. For colocalization of a pair of enzymes in a sequence to be effective in promoting reaction, the second must be active when the first is active or soon after. If the enzymes are slowly varying and only rarely active, the product of the first reaction may diffuse away before the second enzyme is active, and colocalization may have little value. Even for single-step reactions the interplay of reaction and diffusion with enzyme fluctuations leads to added complexities, but for multistep reactions the interplay of reaction and diffusion, cell size, compartmentalization, enzyme fluctuations, colocalization, and segregation is far more complex than for single-step reactions. In this paper, we report the use of stochastic simulations at the level of whole cells to explore, understand, and predict the behavior of single- and multistep enzyme-catalyzed reaction systems exhibiting some of these complexities. Results for single-step reactions confirm several earlier observations by others. The MM relationship, with altered constants, is found to hold for single-step reactions slowed by diffusion. For single-step reactions, the distribution of enzymes in a regular grid is slightly more effective than a random distribution. Fluctuations of enzyme activity, with average activity fixed, have no observed effects for simple single-step reactions slowed by diffusion. Two-step sequential reactions are seen to be slowed by segregation of the enzymes for each step, and results of the calculations suggest limits for cell size. Colocalization of enzymes for a two-step sequence is seen to promote reaction, and rates fall rapidly with increasing distance between enzymes. Low frequency fluctuations of the activities of colocalized enzymes, with average activities fixed, can greatly reduce reaction rates for sequential reactions.

Stochastic simulation of biological reactions, and its applications for studying actin polymerization

Molecular events in biological cells occur in local subregions, where the molecules tend to be small in number. The cytoskeleton, which is important for both the structural changes of cells and their functions, is also a countable entity because of its long fibrous shape. To simulate the local environment using a computer, stochastic simulations should be run. We herein report a new method of stochastic simulation based on random walk and reaction by the collision of all molecules. The microscopic reaction rate P(r) is calculated from the macroscopic rate constant k. The formula involves only local parameters embedded for each molecule. The results of the stochastic simulations of simple second-order, polymerization, Michaelis-Menten-type and other reactions agreed quite well with those of deterministic simulations when the number of molecules was sufficiently large. An analysis of the theory indicated a relationship between variance and the number of molecules in the system, and results of multiple stochastic simulation runs confirmed this relationship. We simulated Ca²(+) dynamics in a cell by inward flow from a point on the cell surface and the polymerization of G-actin forming F-actin. Our results showed that this theory and method can be used to simulate spatially inhomogeneous events.

Fractal kinetic analysis of the enzymatic saccharification of cellulose under different conditions

Fractal kinetic analysis has been applied for the kinetics of enzymatic saccharification of cellulose. Based on the first order cellulose degradation kinetic, a fractal kinetic model with two parameters (rate coefficient and fractal exponent) has been developed. The model fits very well with the experimental data of enzymatic saccharification of cellulose under different conditions, including cellulase loading, nature of substrate, hydrolysis temperature, and addition of nonionic surfactant or polymer in the reaction medium. It indicates that the complex kinetics of enzymatic saccharification of cellulose can be described with the fractal kinetic model. The model exhibits that an addition of nonionic surfactant or polymer increases the rate coefficient and decreases the fractal exponent at the same time. A nonionic surfactant or polymer aqueous solution may be a potential medium engineering method for enzymatic saccharification of cellulose in biofuel industry.

Long-lasting target binding and rebinding as mechanisms to prolong in vivo drug action

An increasing number of examples in the literature suggest that the in vivo duration of drug action not only depends on macroscopic pharmacokinetic properties like plasma half-life and the time needed to equilibrate between the plasma and the effect compartments, but is also influenced by long-lasting target binding and rebinding. The present review combines information from different research areas and simulations to explore the nature of these mechanisms and the conditions in which they are most prevalent. Simulations reveal that these latter phenomena become especially influential when there is no longer sufficient free drug around to maintain high levels of receptor occupancy. There is not always a direct link between slow dissociation and long-lasting in vivo target protection, as the rate of free drug elimination from the effect compartment is also a key influencing factor. Local phenomena that hinder the diffusion of free drug molecules away from their target may allow them to consecutively bind to the same target and/or targets nearby (denoted as 'rebinding') even when their concentration in the bulk phase has already dropped to insignificant levels. The micro-anatomic properties of many effect compartments are likely to intensify this phenomenon. By mimicking the complexity of tissues, intact cells offer the opportunity to investigate both mechanisms under the same, physiologically relevant conditions.

The structure of an Arf-ArfGAP complex reveals a Ca2+ regulatory mechanism

Arfs are small G proteins that have a key role in vesicle trafficking and cytoskeletal remodeling. ArfGAP proteins stimulate Arf intrinsic GTP hydrolysis by a mechanism that is still unresolved. Using a fusion construct we solved the structure of the ArfGAP ASAP3 in complex with Arf6 in the transition state. This structure clarifies the ArfGAP catalytic mechanism and shows a glutamine((Arf6)) and an arginine finger((ASAP3)) as the important catalytic residues. Unexpectedly the structure shows a calcium ion, liganded by both proteins in the complex interface, stabilizing the interaction and orienting the catalytic machinery. Calcium stimulates the GAP activity of ASAPs, but not other members of the ArfGAP family. This type of regulation is unique for GAPs and any other calcium-regulated processes and hints at a crosstalk between Ca(2+) and Arf signaling.

A Monte Carlo study of crowding effects on the self-assembly of amphiphilic molecules

In this work, lattice Monte Carlo was used to study the effects of crowding on the self-assembly of surfactants. Simulation results show that crowding strongly shifts the critical micelle concentration (CMC) of surfactants from the bulk value. Two effects originated from crowding are found to govern the CMC shift: one is the depletion effect by crowding agents and the other is the available volume for micelle formation. The depletion effects inevitably result in the enrichment of surfactants in crowding-free regions and cause the decrease in CMC. On the other hand, the appearance of crowding agents decreases the available volume for micelle formation, which reduces the conformational entropy and impedes the micelle formation. Three factors, including the radius of crowding agents, the arrangement of crowding agents, and the volume fraction of crowding agents, are considered in this work to study the crowding effects. The trends of CMC shifts are interpreted from the competition between the depletion effects and the available volume for micelle formation.

Analysis of reaction-diffusion systems with anomalous subdiffusion

Reaction-diffusion equations are the cornerstone of modeling biochemical systems with spatial gradients, which are relevant to biological processes such as signal transduction. Implicit in the formulation of these equations is the assumption of Fick's law, which states that the local diffusive flux of species i is proportional to its concentration gradient; however, in the context of complex fluids such as cytoplasm and cell membranes, the use of Fick's law is based on empiricism, whereas evidence has been mounting that such media foster anomalous subdiffusion (with mean-squared displacement increasing less than linearly with time) over certain length scales. Particularly when modeling diffusion-controlled reactions and other systems where the spatial domain is considered semi-infinite, assuming Fickian diffusion might not be appropriate. In this article, two simple, conceptually extreme models of anomalous subdiffusion are used in the framework of Green's functions to demonstrate the solution of four reaction-diffusion problems that are well known in the biophysical context of signal transduction: fluorescence recovery after photobleaching, the Smolochowski limit for diffusion-controlled reactions in solution, the spatial range of a diffusing molecule with finite lifetime, and the collision coupling mechanism of diffusion-controlled reactions in two dimensions. In each case, there are only subtle differences between the two subdiffusion models, suggesting how measurements of mean-squared displacement versus time might generally inform models of reactive systems with partial diffusion control.

Time series analysis of particle tracking data for molecular motion on the cell membrane

Biophysicists use single particle tracking (SPT) methods to probe the dynamic behavior of individual proteins and lipids in cell membranes. The mean squared displacement (MSD) has proven to be a powerful tool for analyzing the data and drawing conclusions about membrane organization, including features like lipid rafts, protein islands, and confinement zones defined by cytoskeletal barriers. Here, we implement time series analysis as a new analytic tool to analyze further the motion of membrane proteins. The experimental data track the motion of 40 nm gold particles bound to Class I major histocompatibility complex (MHCI) molecules on the membranes of mouse hepatoma cells. Our first novel result is that the tracks are significantly autocorrelated. Because of this, we developed linear autoregressive models to elucidate the autocorrelations. Estimates of the signal to noise ratio for the models show that the autocorrelated part of the motion is significant. Next, we fit the probability distributions of jump sizes with four different models. The first model is a general Weibull distribution that shows that the motion is characterized by an excess of short jumps as compared to a normal random walk. We also fit the data with a chi distribution which provides a natural estimate of the dimension d of the space in which a random walk is occurring. For the biological data, the estimates satisfy 1 < d < 2, implying that particle motion is not confined to a line, but also does not occur freely in the plane. The dimension gives a quantitative estimate of the amount of nanometer scale obstruction met by a diffusing molecule. We introduce a new distribution and use the generalized extreme value distribution to show that the biological data also have an excess of long jumps as compared to normal diffusion. These fits provide novel estimates of the microscopic diffusion constant. Previous MSD analyses of SPT data have provided evidence for nanometer-scale confinement zones that restrict lateral diffusion, supporting the notion that plasma membrane organization is highly structured. Our demonstration that membrane protein motion is autocorrelated and is characterized by an excess of both short and long jumps reinforces the concept that the membrane environment is heterogeneous and dynamic. Autocorrelation analysis and modeling of the jump distributions are powerful new techniques for the analysis of SPT data and the development of more refined models of membrane organization. The time series analysis also provides several methods of estimating the diffusion constant in addition to the constant provided by the mean squared displacement. The mean squared displacement for most of the biological data shows a power law behavior rather the linear behavior of Brownian motion. In this case, we introduce the notion of an instantaneous diffusion constant. All of the diffusion constants show a strong consistency for most of the biological data.

Elucidating anomalous protein diffusion in living cells with fluorescence correlation spectroscopy-facts and pitfalls

Anomalous protein diffusion has been frequently observed in intracellular fluids and on membranes of living cells. Indeed, a large variety of specimen, from bacteriae to mammalian cells, and several non-invasive measurement techniques, e.g. fluorescence correlation spectroscopy, have revealed that the mean square displacement (MSD) of proteins in vivo is often characterized by an anomalous power-law increase mean value of tau(t)(2) mean value of ~ t(alpha) with 0.5 < alpha </= 0.8. Here, we review these results with a particular focus on fluorescence correlation spectroscopy, and we report on possible causes of variations of the anomaly degree alpha. Moreover, we highlight generic consequences of anomalous diffusion that are likely to play an important role in the cellular context.

Exploring a mathematical model for the kinetics of beta-amyloid molecular imaging probes through a critical analysis of plaque pathology

Amyloid plaques are highly heterogeneous in content, size, density, and macromolecular crowding, as they are composed of masses of fibrils and other cellular material. Given this target architecture, the aggregated microenvironment offers a unique imaging target for ligands and positron emission tomography (PET) molecular imaging probes (MIPs). In this work, we address how the heterogeneous microenvironment of a plaque and its evolution may affect the kinetic rate constant of PET MIPs. We argue that macromolecular crowding will result in anomalous diffusion within plaque regions. To account for anomalous diffusion within plaques, we propose a diffusion-limited ligand-receptor compartmental model. Given the current state of knowledge about the pathological progression of Alzheimer's disease (AD), the model's parameters may be a function of the pathological progression of AD, which could result in biased estimates of the true amyloid load. The bias may be partially overcome through evaluation in conjunction with other measures of AD progression including cerebral glucose metabolism rate, neuronal cell loss, and activated inflammatory presence.

Platform for controlled supramolecular nanoassembly

We here present a two-dimensional (2D) micro/nano-fluidic technique where reactant-doped liquid-crystal films spread and mix on micro- and nanopatterned substrates. Surface-supported phospholipid monolayers are individually doped with complementary DNA molecules which hybridize when these lipid films mix. Using lipid films to convey reactants reduces the dimensionality of traditional 3D chemistry to 2D, and possibly to 1D by confining the lipid film to nanometer-sized lanes. The hybridization event was observed by FRET using single-molecule-sensitive confocal fluorescence detection. We could successfully detect hybridization in lipid streams on 250 nm wide lanes. Our results show that the number and density of reactants as well as sequence of reactant addition can be controlled within confined liquid crystal films, providing a platform for nanochemistry with potential for kinetic control.

Effect of macromolecular crowding on reaction rates: a computational and theoretical study

The effect of macromolecular crowding on the rates of association reactions are investigated using theory and computer simulations. Reactants and crowding agents are both hard spheres, and when two reactants collide they form product with a reaction probability, p(rxn). A value of p(rxn) < 1 crudely mimics the fact that proteins must be oriented properly for an association reaction to occur. The simulations show that the dependence of the reaction rate on the volume fraction of crowding agents varies with the reaction probability. For reaction probabilities close to unity where most of encounters between reactants lead to a reaction, the reaction rate always decreases as the volume fraction of crowding agents is increased due to the reduced diffusion coefficient of reactants. On the other hand, for very small reaction probabilities where, in most of encounters, the reaction does not occur, the reaction rate increases with the volume fraction of crowding agents--in this case, due to the increase probability of a recollision. The Smoluchowski theory refined with the radiation boundary condition and the radial distribution function at contact is in quantitative agreement with simulations for the reaction rate constant and allows the quantitative analysis of both effects separately.

Finite element analysis of drug electrostatic diffusion: inhibition rate studies in N1 neuraminidase

This article describes a numerical solution of the steady-state Poisson-Boltzmann-Smoluchowski (PBS) and Poisson-Nernst-Planck (PNP) equations to study diffusion in biomolecular systems. Specifically, finite element methods have been developed to calculate electrostatic interactions and ligand binding rate constants for large biomolecules. The resulting software has been validated and applied to the wild-type and several mutated avian influenza neurominidase crystal structures. The calculated rates show very good agreement with recent experimental studies. Furthermore, these finite element methods require significantly fewer computational resources than existing particle-based Brownian dynamics methods and are robust for complicated geometries. The key finding of biological importance is that the electrostatic steering plays the important role in the drug binding process of the neurominidase.

Robustness: confronting lessons from physics and biology

The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self-organisation) developed in physics are relevant to biology, or whether specific extensions and novel frameworks are required to account for the robustness properties of living systems. To clarify this issue, the different meanings covered by this unique term are discussed; it is argued that they crucially depend on the kind of perturbations that a robust system should by definition withstand. Possible mechanisms underlying robust behaviours are examined, either encountered in all natural systems (symmetries, conservation laws, dynamic stability) or specific to biological systems (feedbacks and regulatory networks). Special attention is devoted to the (sometimes counterintuitive) interrelations between robustness and noise. A distinction between dynamic selection and natural selection in the establishment of a robust behaviour is underlined. It is finally argued that nested notions of robustness, relevant to different time scales and different levels of organisation, allow one to reconcile the seemingly contradictory requirements for robustness and adaptability in living systems.

Increasing the efficiency of bacterial transcription simulations: when to exclude the genome without loss of accuracy

Background

Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome. The key parameter is the on-rate of RNAP binding to the promoter (k_on), and we compare the total number of transcripts produced from a plasmid vector generated as a function of this rate constant, for two versions of our gene expression model, one incorporating the host cell genome and one excluding it. By sweeping parameters, we identify the k_on range for which the difference between the genome and no-genome models drops below 5%, over a wide range of doubling times, mRNA degradation rates, plasmid copy numbers, and gene lengths.

Results

We assess the effect of the simulating the presence of the genome over a four-dimensional parameter space, considering: 24 min <= bacterial doubling time <= 100 min; 10 <= plasmid copy number <= 1000; 2 min <= mRNA half-life <= 14 min; and 10 bp <= gene length <= 10000 bp. A simple MATLAB user interface generates an interpolated k_on threshold for any point in this range; this rate can be compared to the ones used in other transcription studies to assess the need for including the genome.

Conclusion

Exclusion of the genome is shown to yield less than 5% difference in transcript numbers over wide ranges of values, and computational speed is improved by two to 24 times by excluding explicit representation of the genome.

Superdiffusion in a model for diffusion in a molecularly crowded environment

We present a model for diffusion in a molecularly crowded environment. The model consists of random barriers in a percolation network. Random walks in the presence of slowly moving barriers show normal diffusion for long times but anomalous diffusion at intermediate times. The effective exponents for square distance vs time usually are below one at these intermediate times, but they can also be larger than one for high barrier concentrations. Thus, we observe sub- and superdiffusion in a crowded environment.

Mathematical analysis of models for reaction kinetics in intracellular environments

Two models that have been proposed in the literature for description of kinetics in intracellular environments characterized by macromolecular crowding and inhomogeneities, are mathematically analyzed and discussed. The models are first derived by using phenomenological arguments that lead to generalizations of the law of mass action. The prediction of these models in the case of bimolecular binding reaction is then analyzed. It is mathematically proved that the models may predict qualitatively different behavior of progress curves. In particular, they also predict asymptotic steady state concentrations that cannot be reconciled. In this paper we propose and discuss generalizations of these models which under specified conditions lead to qualitatively similar behavior of reaction progress curves. We believe that these generalized models are better suited for data analysis.

Signal transduction at point-blank range: analysis of a spatial coupling mechanism for pathway crosstalk

The plasma membrane provides a physical platform for the orchestration of molecular interactions and biochemical conversions involved in the early stages of receptor-mediated signal transduction in living cells. In that context, we introduce here the concept of spatial coupling, wherein simultaneous recruitment of different enzymes to the same receptor scaffold facilitates crosstalk between different signaling pathways through the local release and capture of activated signaling molecules. To study the spatiotemporal dynamics of this mechanism, we have developed a Brownian dynamics modeling approach and applied it to the receptor-mediated activation of Ras and the cooperative recruitment of phosphoinositide 3-kinase (PI3K) by activated receptors and Ras. Various analyses of the model simulations show that cooperative assembly of multimolecular complexes nucleated by activated receptors is facilitated by the local release and capture of membrane-anchored signaling molecules (such as active Ras) from/by receptor-bound signaling proteins. In the case of Ras/PI3K crosstalk, the model predicts that PI3K is more likely to be recruited by activated receptors bound or recently visited by the enzyme that activates Ras. By this mechanism, receptor-bound PI3K is stabilized through short-range, diffusion-controlled capture of active Ras and Ras/PI3K complexes released from the receptor complex. We contend that this mechanism is a means by which signaling pathways are propagated and spatially coordinated for efficient crosstalk between them.