Axonal transport, via molecular motors kinesin and dynein, is a critical process in supplying the necessary constituents to maintain normal neuronal function. In this study, we predict the role of cooperativity by motors of the same polarity across the entire spectrum of physiological axonal transport. That is, we examined how the number of motors, either kinesin or dynein, working together to move a cargo, results in the experimentally determined velocity profiles seen in fast and slow anterograde and retrograde transport. We quantified the physiological forces exerted on a motor by a cargo as a function of cargo size, transport velocity, and transport type. Our results show that the force exerted by our base case neurofilament (DNF=10nm, LNF=1.6μm) is ~1.25pN at 600nm/s; additionally, the force exerted by our base case organelle (DOrg=1μm) at 1,000nm/s is ~5.7pN. Our results indicate that while a single motor can independently carry an average cargo, cooperativity is required to produce the experimental velocity profiles for fast transport. However, no cooperativity is required to produce the slow transport velocity profiles; thus, a single dynein or kinesin can carry the average neurofilament retrogradely or anterogradely, respectively. The potential role cooperativity may play in the hypothesized mechanisms of motoneuron transport diseases such as Amyotrophic Lateral Sclerosis (ALS) is discussed.
Background
Population inference is an important problem in genetics used to remove population stratification in genome-wide association studies and to detect migration patterns or shared ancestry. An individual’s genotype can be modeled as a probabilistic function of ancestral population memberships, Q, and the allele frequencies in those populations, P. The parameters, P and Q, of this binomial likelihood model can be inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient based approaches such as sequential quadratic programming. This paper proposes a least-squares simplification of the binomial likelihood model motivated by a Euclidean interpretation of the genotype feature space. This results in a faster algorithm that easily incorporates the degree of admixture within the sample of individuals and improves estimates without requiring trial-and-error tuning.
Results
We show that the expected value of the least-squares solution across all possible genotype datasets is equal to the true solution when part of the problem has been solved, and that the variance of the solution approaches zero as its size increases. The Least-squares algorithm performs nearly as well as Admixture for these theoretical scenarios. We compare least-squares, Admixture, and FRAPPE for a variety of problem sizes and difficulties. For particularly hard problems with a large number of populations, small number of samples, or greater degree of admixture, least-squares performs better than the other methods. On simulated mixtures of real population allele frequencies from the HapMap project, Admixture estimates sparsely mixed individuals better than Least-squares. The least-squares approach, however, performs within 1.5% of the Admixture error. On individual genotypes from the HapMap project, Admixture and least-squares perform qualitatively similarly and within 1.2% of each other. Significantly, the least-squares approach nearly always converges 1.5- to 6-times faster.
Conclusions
The computational advantage of the least-squares approach along with its good estimation performance warrants further research, especially for very large datasets. As problem sizes increase, the difference in estimation performance between all algorithms decreases. In addition, when prior information is known, the least-squares approach easily incorporates the expected degree of admixture to improve the estimate.
Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment.
Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.
The Fontan procedure, although an imperfect solution for children born with a single functional ventricle, is the only reconstruction at present short of transplantation. The haemodynamics associated with the total cavopulmonary connection, the modern approach to Fontan, are severely altered from the normal biventricular circulation and may contribute to the long-term complications that are frequently noted. Through recent technological advances, spear-headed by advances in medical imaging, it is now possible to virtually model these surgical procedures and evaluate the patient-specific haemodynamics as part of the pre-operative planning process. This is a novel paradigm with the potential to revolutionise the approach to Fontan surgery, help to optimise the haemodynamic results, and improve patient outcomes. This review provides a brief overview of these methods, presents preliminary results of their clinical usage, and offers insights into its potential future directions.
Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement.
by
Brittany K. Taylor;
Michaela R. Frenzel;
Jacob A. Eastman;
Christine M. Embury;
Oktay Agcaoglu;
Yu-Ping Wang;
Julia M. Stephen;
Vince D. Calhoun;
Tony W. Wilson
Adolescence is a critical period of structural and functional neural maturation among regions serving the cognitive control of emotion. Evidence suggests that this process is guided by developmental changes in amygdala and striatum structure and shifts in functional connectivity between subcortical (SC) and cognitive control (CC) networks. Herein, we investigate the extent to which such developmental shifts in structure and function reciprocally predict one another over time. 179 youth (9-15 years-old) completed annual MRI scans for three years. Amygdala and striatum volumes and connectivity within and between SC and CC resting state networks were measured for each year.
We tested for reciprocal predictability of within-person and between-person changes in structure and function using random-intercept cross-lagged panel models. Within-person shifts in amygdala volumes in a given year significantly and specifically predicted deviations in SC-CC connectivity in the following year, such that an increase in volume was associated with decreased SC-CC connectivity the following year. Deviations in connectivity did not predict changes in amygdala volumes over time. Conversely, broader group-level shifts in SC-CC connectivity were predictive of subsequent deviations in striatal volumes. We did not see any cross-predictability among amygdala or striatum volumes and within-network connectivity measures.
Within-person shifts in amygdala structure year-to-year robustly predicted weaker SC-CC connectivity in subsequent years, whereas broader increases in SC-CC connectivity predicted smaller striatal volumes over time. These specific structure function relationships may contribute to the development of emotional control across adolescence.
The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFβ and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFβ positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.
Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases for which obtaining supervised data is difficult or impossible. Here, we describe an approach for movement decoding that does not require access to simultaneously measured neural activity and motor outputs. We use the statistics of movement - much like cryptographers use the statistics of language - to find a mapping between neural activity and motor variables, and then align the distribution of deoder outputs with the typical distribution of motor outputs by minimizing their Kullback-Leibler divergence. By using datasets collected from the motor cortex of three non-human primates performing either a reaching task or an isometric force-production task, we show that the performance of such a distribution-alignment decoding algorithm is comparable to the performance of supervised approaches. Distribution-alignment decoding promises to broaden the set of potential applications of brain decoding.
Background: A clinical study comparing the hemodynamic outcomes of transcatheter mitral valve replacement (TMVR) with vs. without Laceration of the Anterior Mitral leaflet to Prevent Outflow Obstruction (LAMPOON) has never been designed nor conducted. Aims: To quantify the hemodynamic impact of LAMPOON in TMVR using patient-specific computational (in silico) models. Materials: Eight subjects from the LAMPOON investigational device exemption trial were included who had acceptable computed tomography (CT) data for analysis. All subjects were anticipated to be at prohibitive risk of left ventricular outflow tract (LVOT) obstruction from TMVR, and underwent successful LAMPOON immediately followed by TMVR. Using post-procedure CT scans, two 3D anatomical models were created for each subject: (1) TMVR with LAMPOON (performed procedure), and (2) TMVR without LAMPOON (virtual control). A validated computational fluid dynamics (CFD) paradigm was then used to simulate the hemodynamic outcomes for each condition. Results: LAMPOON exposed on average 2 ± 0.6 transcatheter valve cells (70 ± 20 mm2 total increase in outflow area) which provided an additional pathway for flow into the LVOT. As compared to TMVR without LAMPOON, TMVR with LAMPOON resulted in lower peak LVOT velocity, lower peak LVOT gradient, and higher peak LVOT effective orifice area by 0.4 ± 0.3 m/s (14 ± 7% improvement, p = 0.006), 7.6 ± 10.9 mmHg (31 ± 17% improvement, p = 0.01), and 0.2 ± 0.1 cm2 (17 ± 9% improvement, p = 0.002), respectively. Conclusion: This was the first study to permit a quantitative, patient-specific comparison of LVOT hemodynamics following TMVR with and without LAMPOON. The LAMPOON procedure achieved a critical increment in outflow area which was effective for improving LVOT hemodynamics, particularly for subjects with a small neo-left ventricular outflow tract (neo-LVOT).