Species-specific differences in the Pro-Ala rich region of cardiac myosin binding protein-C

Cardiac myosin binding protein-C (cMyBP-C) is an accessory protein found in the A-bands of vertebrate sarcomeres and mutations in the cMyBP-C gene are a leading cause of familial hypertrophic cardiomyopathy. The regulatory functions of cMyBP-C have been attributed to the N-terminus of the protein, which is composed of tandem immunoglobulin (Ig)-like domains (C0, C1, and C2), a region rich in proline and alanine residues (the Pro-Ala rich region) that links C0 and C1, and a unique sequence referred to as the MyBP-C motif, or M-domain, that links C1 and C2. Recombinant proteins that contain various combinations of the N-terminal domains of cMyBP-C can activate actomyosin interactions in the absence of Ca2+, but the specific sequences required for these effects differ between species; the Pro-Ala region has been implicated in human cMyBP-C whereas the C1 and M-domains appear important in mouse cMyBP-C. To investigate whether species-specific differences in sequence can account for the observed differences in function, we compared sequences of the Pro-Ala rich region in cMyBP-C isoforms from different species. Here we report that the number of proline and alanine residues in the Pro-Ala rich region varies significantly between different species and that the number correlates directly with mammalian body size and inversely with heart rate. Thus, systematic sequence differences in the Pro-Ala rich region of cMyBP-C may contribute to observed functional differences in human versus mouse cMyBP-C isoforms and suggest that the Pro-Ala region may be important in matching contractile speed to cardiac function across species

Distributed multi-scale muscle simulation in a hybrid MPI–CUDA computational environment

We present Mexie, an extensible and scalable software solution for distributed multi-scale muscle simulations in a hybrid MPI–CUDA environment. Since muscle contraction relies on the integration of physical and biochemical properties across multiple length and time scales, these models are highly processor and memory intensive. Existing parallelization efforts for accelerating multi-scale muscle simulations imply the usage of expensive large-scale computational resources, which produces overwhelming costs for the everyday practical application of such models. In order to improve the computational speed within a reasonable budget, we introduce the concept of distributed calculations of multi-scale muscle models in a mixed CPU–GPU environment. The concept is applied to a two-scale muscle model, in which a finite element macro model is coupled with the microscopic Huxley kinetics model. Finite element calculations of a continuum macroscopic model take place strictly on the CPU, while numerical solutions of the partial differential equations of Huxley’s cross-bridge kinetics are calculated on both CPUs and GPUs. We present a modular architecture of the solution, along with an internal organization and a specific load balancer that is aware of memory boundaries in such a heterogeneous environment. Solution was verified on both benchmark and real-world examples, showing high utilization of involved processing units, ensuring high scalability. Speed-up results show a boost of two orders of magnitude over any previously reported distributed multi-scale muscle models. This major improvement in computational feasibility of multi-scale muscle models paves the way for new discoveries in the field of muscle modeling and future clinical applications.Author's versio

Active contraction of cardiac cells: a reduced model for sarcomere dynamics with cooperative interactions

We propose a reduced ODE model for the mechanical activation of cardiac myofilaments, which is based on explicit spatial representation of nearest-neighbour interactions. Our model is derived from the cooperative Markov Chain model of Washio et al. (Cell Mol Bioeng 5(1):113–126, 2012), under the assumption of conditional independence of specific sets of events. This physically motivated assumption allows to drastically reduce the number of degrees of freedom, thus resulting in a significantly large computational saving. Indeed, the original Markov Chain model involves a huge number of degrees of freedom (order of (Formula presented.)) and is solved by means of the Monte Carlo method, which notoriously reaches statistical convergence in a slow fashion. With our reduced model, instead, numerical simulations can be carried out by solving a system of ODEs, reducing the computational time by more than 10, 000 times. Moreover, the reduced model is accurate with respect to the original Markov Chain model. We show that the reduced model is capable of reproducing physiological steady-state force–calcium and force–length relationships with the observed asymmetry in apparent cooperativity near the calcium level producing half activation. Finally, we also report good qualitative and quantitative agreement with experimental measurements under dynamic conditions