Mass Action Kinetic Model Research Articles

Impact of Dissociation Constant on the Detection Sensitivity of Polymerization-Based Signal Amplification Reactions

Many studies have demonstrated the concept of using free-radical polymerization reactions to provide signal amplification so that molecular recognition events indicative of disease states may be detected in a simple and low-cost manner. We provide the first systematic study of how the dissociation constant impacts detection sensitivity in these assays, having chosen a range of dissociation constants (nanomolar to picomolar) that is typical of those encountered in molecular diagnostic applications that detect protein-protein binding events. In addition, we use experimental results to validate a mass-action kinetic model that may be used to predict assay performance as an alternative or supplement to the empirical approach to developing new polymerization-based amplification assays that has characterized the field to date.

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of “source” species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the “target” species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.

Identification of Amprenavir, Quinidine and Loperamide Kinetic Parameters for P-gp Tranpsorter in Caco-2 Confluent Cell Monolayer

P-glycoprotein (P-gp) is a member of the ATP binding cassette (ABC) family of proteins that has been extensively studied due to its ability of multidrug resistance and for causing clinically important drug-drug interaction (DDI). Structural knowledge and functional knowledge of transport kinetics in physiological relevant system have been intensively studied for a molecular understanding of P-gp activity. Using the mass action kinetic model without imposing the steady-state assumptions, previous studies have identified the kinetic parameters for a series of P-gp substrates in MDCKII-hMDR1 confluent cell monolayer. However, little is known whether this model can be extended to other cell lines to study P-gp. Here, we applied our model to another widely-used P-gp expressing cell line, human colon adenocarcinoma (Caco-2), and successfully fitted the elementary rate constants of P-gp for substrates amprenavir, quinidine and loperamide as well as P-gp surface density. Furthermore, the fitted rate constants of above drugs in Caco-2 cells are similar to those in MDCKII-hMDR1 cells. Our results suggest that the mass action kinetic model can identify P-gp rate constants in human cell line, Caco-2 cells and that this model can be used to predict and characterize P-gp pharmacokinetics in vivo.

A kinetic model of ERK cyclic pathway on substrate control

Extracellular signal-regulated kinase (ERK) is a key factor in the widely used signaling cascade of phosphorylation–dephosphorylation cycles and plays pivotal roles in many aspects of biological processes. Experimental studies in yeast and in Drosophila embryo have suggested that the phosphorylation and spatial localization of ERK are influenced by the level of its downstream substrates. However, the mechanism, through which these substrates control properties of ERK signaling, has been unclear. I propose a mass–action kinetic model of ERK cycle with its substrate, and demonstrate that the substrate can modulate the ERK activity by directly interacting with ERK. The model shows that the addition of substrate controls the level of ERK phosphorylation positively or negatively, depending on the balance between dissociation constants of ERK–substrate interaction and properties of ERK cyclic signaling in the absence of the substrate. In addition, by considering cellular compartments, cytosol and nucleus, the substrate can lead to nuclear accumulation of ERK, suggesting that the substrate can act as a nuclear anchor of ERK. The model gives a possible mechanism that can account for substrate-mediated modulation of ERK signaling.

Inducing sustained oscillations in mass action kinetic networks of a certain class

Engineering a synthetic oscillator requires an oscillatory model which can be implemented into practice. Some required conditions for a successful practical implementation include the robustness of the oscillation under realistic parameter values and the accessibility to the variables that need to be manipulated. For a particular class of theoretical oscillators derived from mass action kinetic models, oscillations appear provided that some of the concentrations are kept constant at given values. This condition is not trivially accomplished in practice. In this work we provide two different realizations of kinetic networks leading to the desired limit cycle oscillation: a mass action kinetic system with constant inflow and outflow for some of the species, and a stirred tank reactor configuration with some entrapped species where the nonchemical effect of keeping some concentrations constant is achieved by manipulating some accessible variables. The results, together with the robustness of the approach and the conditions for further practical implementation are discussed and illustrated through the well known Brusselator example.

Nonlinear physics approach to RNA cross-replication: Marginal stability, generalized logistic growth, and impacts of degradation

It is nowadays believed that the evolution of life involved as an intermediate step an RNA world. In such an RNA world RNA molecules replicate themselves in catalytic reactions. Recent experiments on cross-replicating RNA support the RNA world hypothesis. We derive a nonlinear mass-action kinetics model to explain logistic growth patterns and non-symmetric saturation levels observed in those experiments. We also demonstrate that fixed points of the RNA growth process are only marginally stable rather than asymptotically stable.

A mechanistic model of PCR for accurate quantification of quantitative PCR data.

BackgroundQuantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle () standard curve quantification, which requires the time- and labor-intensive construction of a standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as standard curve quantification.Principal FindingsWe have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as standard curve quantification for a variety of DNA targets and a wide range of concentrations.SignificanceWe anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sample throughput, where construction of a standard curve is impractical.

Comparison of Kinetic and Dynamical Models of DNA−Protein Interaction and Facilitated Diffusion

It has long been asserted that proteins such as transcription factors may locate their target in DNA sequences at rates that surpass by several orders of magnitude the three-dimensional diffusion limit thanks to facilitated diffusion, that is, the combination of one-dimensional (sliding along the DNA) and three-dimensional diffusion. This claim has been supported throughout the years by several mass action kinetic models, while the dynamical model we proposed recently (J. Chem. Phys. 2009, 130, 015103) suggests that acceleration of targeting due to facilitated diffusion cannot be large. In order to solve this apparent contradiction, we performed additional simulations to compare the results obtained with our model to those obtained with the kinetic model of Klenin et al. (Phys. Rev. Lett. 2006, 96, 018104). We show in this paper that the two models actually support each other and agree in predicting a low efficiency for facilitated diffusion. Extrapolation of these results to real systems even indicates that facilitated diffusion necessarily slows down the targeting process compared to three-dimensional diffusion.

If the KI Is Defined by the Free Energy of Binding to P-Glycoprotein, Which Kinetic Parameters Define the IC50 for the Madin-Darby Canine Kidney II Cell Line Overexpressing Human Multidrug Resistance 1 Confluent Cell Monolayer?

From previous fits of drug transport kinetics across confluent Madin-Darby canine kidney II cell line overexpressing human multidrug resistance 1 cell monolayers, we found that a drug's binding constant to P-glycoprotein (P-gp) was significantly smaller than its IC(50) when that drug was used as an inhibitor against another P-gp substrate. We tested several IC(50) candidate functions, including the standard function, the Kalvass-Pollack function, and the efflux ratio, to determine whether any of them yielded an IC(50) = K(I), as would be expected for water-soluble enzymes. For the confluent cell monolayer, the IC(50)/K(I) ratio is greater than 1 for all candidate functions tested. From the mass action kinetic model, we have derived a simple approximate equation that shows how the IC(50)/K(I) ratio depends on the elementary rate constants from our mass action model. Thus, the IC(50) will differ between cell lines and tissues, for the same probe substrate and inhibitor, if there are different membrane concentrations of P-gp, or the probe substrate's elementary rate constants, partition coefficient, binding constant to P-gp, passive permeability, and ability to access the other transporters (if any) in the two cell lines. The mass action model and the approximate equation for the IC(50)/K(I) ratio derived here can be used to estimate the elementary rate constants needed to extrapolate in vitro drug-drug interactions for compounds to the in vivo environment.

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

BackgroundTo understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem.ResultsWe investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in C. glutamicum. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis.ConclusionA mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings.

Enhanced identification and exploitation of time scales for model reduction in stochastic chemical kinetics

Widely different time scales are common in systems of chemical reactions and can be exploited to obtain reduced models applicable to the time scales of interest. These reduced models enable more efficient computation and simplify analysis. A classic example is the irreversible enzymatic reaction, for which separation of time scales in a deterministic mass action kinetics model results in approximate rate laws for the slow dynamics, such as that of Michaelis-Menten. Recently, several methods have been developed for separation of slow and fast time scales in chemical master equation (CME) descriptions of stochastic chemical kinetics, yielding separate reduced CMEs for the slow variables and the fast variables. The paper begins by systematizing the preliminary step of identifying slow and fast variables in a chemical system from a specification of the slow and fast reactions in the system. The authors then present an enhanced time-scale-separation method that can extend the validity and improve the accuracy of existing methods by better accounting for slow reactions when equilibrating the fast subsystem. The resulting method is particularly accurate in systems such as enzymatic and protein interaction networks, where the rates of the slow reactions that modify the slow variables are not a function of the slow variables. The authors apply their methodology to the case of an irreversible enzymatic reaction and show that the resulting improvements in accuracy and validity are analogous to those obtained in the deterministic case by using the total quasi-steady-state approximation rather than the classical Michaelis-Menten. The other main contribution of this paper is to show how mass fluctuation kinetics models, which give approximate evolution equations for the means, variances, and covariances of the concentrations in a chemical system, can feed into time-scale-separation methods at a variety of stages.

Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems-level behavior

BackgroundTissue morphogenesis is a complex process whereby tissue structures self-assemble by the aggregate behaviors of independently acting cells responding to both intracellular and extracellular cues in their environment. During embryonic development, morphogenesis is particularly important for organizing cells into tissues, and although key regulatory events of this process are well studied in isolation, a number of important systems-level questions remain unanswered. This is due, in part, to a lack of integrative tools that enable the coupling of biological phenomena across spatial and temporal scales. Here, we present a new computational framework that integrates intracellular signaling information with multi-cell behaviors in the context of a spatially heterogeneous tissue environment.ResultsWe have developed a computational simulation of mesendoderm migration in the Xenopus laevis explant model, which is a well studied biological model of tissue morphogenesis that recapitulates many features of this process during development in humans. The simulation couples, via a JAVA interface, an ordinary differential equation-based mass action kinetics model to compute intracellular Wnt/β-catenin signaling with an agent-based model of mesendoderm migration across a fibronectin extracellular matrix substrate. The emergent cell behaviors in the simulation suggest the following properties of the system: maintaining the integrity of cell-to-cell contact signals is necessary for preventing fractionation of cells as they move, contact with the Fn substrate and the existence of a Fn gradient provides an extracellular feedback loop that governs migration speed, the incorporation of polarity signals is required for cells to migrate in the same direction, and a delicate balance of integrin and cadherin interactions is needed to reproduce experimentally observed migratory behaviors.ConclusionOur computational framework couples two different spatial scales in biology: intracellular with multicellular. In our simulation, events at one scale have quantitative and dynamic impact on events at the other scale. This integration enables the testing and identification of key systems-level hypotheses regarding how signaling proteins affect overall tissue-level behavior during morphogenesis in an experimentally verifiable system. Applications of this approach extend to the study of tissue patterning processes that occur during adulthood and disease, such as tumorgenesis and atherogenesis.

Mass fluctuation kinetics: Capturing stochastic effects in systems of chemical reactions through coupled mean-variance computations

The intrinsic stochastic effects in chemical reactions, and particularly in biochemical networks, may result in behaviors significantly different from those predicted by deterministic mass action kinetics (MAK). Analyzing stochastic effects, however, is often computationally taxing and complex. The authors describe here the derivation and application of what they term the mass fluctuation kinetics (MFK), a set of deterministic equations to track the means, variances, and covariances of the concentrations of the chemical species in the system. These equations are obtained by approximating the dynamics of the first and second moments of the chemical master equation. Apart from needing knowledge of the system volume, the MFK description requires only the same information used to specify the MAK model, and is not significantly harder to write down or apply. When the effects of fluctuations are negligible, the MFK description typically reduces to MAK. The MFK equations are capable of describing the average behavior of the network substantially better than MAK, because they incorporate the effects of fluctuations on the evolution of the means. They also account for the effects of the means on the evolution of the variances and covariances, to produce quite accurate uncertainty bands around the average behavior. The MFK computations, although approximate, are significantly faster than Monte Carlo methods for computing first and second moments in systems of chemical reactions. They may therefore be used, perhaps along with a few Monte Carlo simulations of sample state trajectories, to efficiently provide a detailed picture of the behavior of a chemical system.

Managing models of signaling networks

Signaling pathways participate in complex information processing networks. These networks handle housekeeping functions of the cell as well as specialized functions such as synaptic plasticity. I report two developments in managing such networks: a compilation of mass-action kinetic models of signaling pathways, and shared motifs in the chemistry of interactions between signaling pathways. These motifs may prove useful in abstracting signaling networks, without compromising chemical reaction details. The combination of a library of signaling pathway models, and high-level rules to connect these pathways, may simplify development of complex signaling network models.

Measuring pKa of Activation and pKi of Inactivation for Influenza Hemagglutinin from Kinetics of Membrane Fusion of Virions and of HA Expressing Cells

The data for the pH dependence of lipid mixing between influenza virus (A/PR/8/34 strain) and fluorescently labeled liposomes containing gangliosides has been analyzed using a comprehensive mass action kinetic model for hemaglutinin (HA)-mediated fusion. Quantitative results obtained about the architecture of HA-mediated membrane fusion site from this analysis are in agreement with the previously reported results from analyses of data for HA-expressing cells fusing with various target membranes. Of the eight or more HAs forming a fusogenic aggregate, only two have to undergo the “essential” conformational change needed to initiate fusion. The mass action kinetic model has been extended to allow the analysis of the pKa for HA activation and pKi for HA inactivation. Inactivation and activation of HA following protonation were investigated for various experimental systems involving different strains of HA (A/PR/8/34, X:31, A/Japan). We find that the pKa for the final protonation site on each monomer of the trimer molecule is 5.6 to 5.7, irrespective of the strain. We also find that the pKi for the PR/8 strain is 4.8 to 4.9. The inactivation rate constants for HA, measured from experiments done with PR/8 virions fusing with liposomes and X:31 HA-expressing cells fusing with red blood cells, were both found to be of the order of 10 −4 s −1. This number appears to be the minimal rate for HA's essential conformational change at low HA surface density. At high HA surface densities, we find evidence for cooperativity in the conformational change, as suggested by other studies.

Mechanisms for Temporal Tuning and Filtering by Postsynaptic Signaling Pathways

Networks of signaling pathways perform complex temporal decoding functions in diverse biological systems, including the synapse, development, and bacterial chemotaxis. This paper examines temporal filtering and tuning properties of synaptic signaling pathways as a possible substrate for emergent temporal decoding. A mass action kinetic model of 16 synaptic signaling pathways was used to dissect out the contribution of these pathways in linear cascades and when coupled to form a network. The model predicts two primary mechanisms of temporal tuning of pathways: a weighted summation of responses of pathways with different timings and the presence of biochemical feedback loop(s) with emergent dynamics. Regulatory inputs act differently on these two tuning mechanisms. In the first case, regulators act like a gain-control on pathways with different intrinsic tuning. In the case of feedback loops, the temporal properties of the loop itself are changed. These basic tuning mechanisms may underlie specialized temporal tuning functions in more complex signaling systems in biology.

Comprehensive Kinetic Analysis of Influenza Hemagglutinin-Mediated Membrane Fusion: Role of Sialate Binding

The data of Danieli et al. ( J. Cell Biol. 133:559–569, 1996) and Blumenthal et al. ( J. Cell Biol. 135:63–71, 1996) for fusion between hemagglutinin (HA)-expressing cells and fluorescently labeled erythrocytes has been analyzed using a recently published comprehensive mass action kinetic model for HA-mediated fusion. This model includes the measurable steps in the fusion process, i.e., first pore formation, lipid mixing, and content mixing of aqueous fluorescent markers. It contains two core parameters of the fusion site architecture. The first is the minimum number of aggregated HAs needed to sustain subsequent fusion intermediates. The second is the minimal number of those HAs within the fusogenic aggregate that must undergo a slow “essential” conformational change needed to initiate bilayer destabilization. Because the kinetic model has several parameters, each data set was exhaustively fitted to obtain all best fits. Although each of the data sets required particular parameter ranges for best fits, a consensus subset of these parameter ranges could fit all of the data. Thus, this comprehensive model subsumes the available mass action kinetic data for the fusion of HA-expressing cells with erythrocytes, despite the differences in assays and experimental design, which necessitated transforming fluorescence dequenching intensities to equivalent cumulative waiting time distributions. We find that HAs bound to sialates on glycophorin can participate in fusion as members of the fusogenic aggregate, but they cannot undergo the essential conformational change that initiates bilayer destabilization, thus solving a long-standing debate. Also, the similarity in rate constants for lipid mixing and content mixing found here for HA-mediated fusion and by Lee and Lentz ( Proc. Natl. Acad. Sci. U.S.A. 95:9274–9279, 1998) for PEG-induced fusion of phosphatidylcholine liposomes supports the idea that subsequent to stable fusion pore formation, the evolution of fusion intermediates is determined more by the lipids than by the proteins.

Minimal Aggregate Size and Minimal Fusion Unit for the First Fusion Pore of Influenza Hemagglutinin-Mediated Membrane Fusion

The data of Melikyan et al. ( J. Gen. Physiol. 106:783, 1995) for the time required for the first measurable step of fusion, the formation of the first flickering conductivity pore between influenza hemagglutinin (HA) expressing cells and planar bilayers, has been analyzed using a new mass action kinetic model. The analysis incorporates a rigorous distinction between the minimum number of HA trimers aggregated at the nascent fusion site (which is denoted the minimal aggregate size) and the number of those trimers that must to undergo a slow essential conformational change before the first fusion pore could form (which is denoted the minimal fusion unit). At least eight (and likely more) HA trimers aggregated at the nascent fusion site. Remarkably, of these eight (or more) HAs, only two or three must undergo the essential conformational change slowly before the first fusion pore can form. Whether the conformational change of these first two or three HAs are sufficient for the first fusion pore to form or whether the remaining HAs within the aggregate must rapidly transform in a cooperative manner cannot be determined kinetically. Remarkably, the fitted halftime for the essential HA conformational change is roughly 10 4 s, which is two orders of magnitude slower than the observed halftime for fusion. This is because the HAs refold with distributed kinetics and because the conductance assay monitored the very first aggregate to succeed in forming a first fusion pore from an ensemble of hundreds or thousands (depending upon the cell line) of fusogenic HA aggregates within the area of apposition between the cell and the planar bilayer. Furthermore, the average rate constant for this essential conformational change was at least 10 7 times slower than expected for a simple coiled coil conformational change, suggesting that there is either a high free energy barrier to fusion and/or very many nonfusogenic conformations in the refolding landscape. Current models for HA-mediated fusion are examined in light of these new constraints on the early structure and evolution of the nascent fusion site. None completely comply with the data.

Interactions of influenza virus with cultured cells: detailed kinetic modeling of binding and endocytosis.

We performed a detailed kinetic analysis of the uptake of influenza virus (A/PR8/34) by Madin Darby canine kidney (MDCK) cells in culture. Experimental procedures were based on the relief of fluorescence self-quenching of the fluorescent probe octadecylrhodamine B chloride (R18) incorporated in the viral envelope. Equilibrium for binding of influenza virus to MDCK cells (2.5 x 10(6)/mL) was reached quicker with temperature increases due to a faster dynamic mobility of the particles. We deduced that there are two kinds of binding sites for influenza virus in MDCK cells and determined the kinetic parameters of the binding process (adhesion and detachment rate constants), using a mass action kinetic model. As the temperature increases, the number of binding sites for influenza virus decreases, especially the high-affinity binding sites, whereas the value of the affinity constant for virus binding to the binding site, k, increases. Nevertheless, the binding association constant at equilibrium Ki, which is given by Ki = Niki, where Ni is the number of binding sites per cell, declines as the temperature increases. When endocytosis occurs, the total uptake of virions by the cells is larger than that observed in the process of binding at the same temperature, and the uptake proceeds for longer times. Using our mass kinetic model, we determined that at 20 degrees C, the rate constant of endocytosis, epsilon, for influenza virus with this cell line is 2.6 x 10(-)4 s-1, i.e., in the same range as in studies on endocytosis of liposomes.

Compliant Realignment of Binding Sites in Muscle: Transient Behavior and Mechanical Tuning

The presence of compliance in the lattice of filaments in muscle raises a number of concerns about how one accounts for force generation in the context of the cross-bridge cycle—binding site motions and coupling between cross-bridges confound more traditional analyses. To explore these issues, we developed a spatially explicit, mechanochemical model of skeletal muscle contraction. With a simple three-state model of the cross-bridge cycle, we used a Monte Carlo simulation to compute the instantaneous balance of forces throughout the filament lattice, accounting for both thin and thick filament distortions in response to cross-bridge forces. This approach is compared to more traditional mass action kinetic models (in the form of coupled partial differential equations) that assume filament inextensibility. We also monitored instantaneous force generation, ATP utilization, and the dynamics of the cross-bridge cycle in simulations of step changes in length and variations in shortening velocity. Three critical results emerge from our analyses: 1) there is a significant realignment of actin-binding sites in response to cross-bridge forces, 2) this realignment recruits additional cross-bridge binding, and 3) we predict mechanical behaviors that are consistent with experimental results for velocity and length transients. Binding site realignment depends on the relative compliance of the filament lattice and cross-bridges, and within the measured range of these parameters, gives rise to a sharply tuned peak for force generation. Such mechanical tuning at the molecular level is the result of mechanical coupling between individual cross-bridges, mediated by thick filament deformations, and the resultant realignment of binding sites on the thin filament.