Mohammad R. K. Mofrad

Cytoskeletal mechanics : models and measurements

1. Introduction and the biological basis for cell mechanics Mohammad R. K. Mofrad and Roger Kamm 2. Experimental measurements of intracellular mechanics Paul Janmey and Christoph Schmidt 3. The cytoskeleton as a soft glassy material Jeffrey Fredberg and Ben Fabry 4. Continuum elastic or viscoelastic models for the cell Mohammad R. K. Mofrad, Helene Karcher and Roger Kamm 5. Multiphasic models of cell mechanics Farshid Guuilak, Mansoor A. Haider, Lori A. Setton, Tod A. Laursen and Frank P. T. Baaijens 6. Models of cytoskeletal mechanics based on tensegrity Dimitrije Stamenovic 7. Cells, gels and mechanics Gerald H. Pollack 8. Polymer-based models of cytoskeletal networks F. C. MacKintosh 9. Cell dynamics and the actin cytoskeleton James L. McGrath and C. Forbes Dewey, Jr 10. Active cellular motion: continuum theories and models Marc Herant and Micah Dembo 11. Summary Mohammad R. K. Mofrad and Roger Kamm.

Biomechanical properties of native and tissue engineered heart valve constructs

Due to the increasing number of heart valve diseases, there is an urgent clinical need for off-the-shelf tissue engineered heart valves. While significant progress has been made toward improving the design and performance of both mechanical and tissue engineered heart valves (TEHVs), a human implantable, functional, and viable TEHV has remained elusive. In animal studies so far, the implanted TEHVs have failed to survive more than a few months after transplantation due to insufficient mechanical properties. Therefore, the success of future heart valve tissue engineering approaches depends on the ability of the TEHV to mimic and maintain the functional and mechanical properties of the native heart valves. However, aside from some tensile quasistatic data and flexural or bending properties, detailed mechanical properties such as dynamic fatigue, creep behavior, and viscoelastic properties of heart valves are still poorly understood. The need for better understanding and more detailed characterization of mechanical properties of tissue engineered, as well as native heart valve constructs is thus evident. In the current review we aim to present an overview of the current understanding of the mechanical properties of human and common animal model heart valves. The relevant data on both native and tissue engineered heart valve constructs have been compiled and analyzed to help in defining the target ranges for mechanical properties of TEHV constructs, particularly for the aortic and the pulmonary valves. We conclude with a summary of perspectives on the future work on better understanding of the mechanical properties of TEHV constructs.

A multiscale computational comparison of the bicuspid and tricuspid aortic valves in relation to calcific aortic stenosis.

Patients with bicuspid aortic valve (BAV) are more likely to develop a calcific aortic stenosis (CAS), as well as a number of other ailments, as compared to their cohorts with normal tricuspid aortic valves (TAV). It is currently unknown whether the increase in risk of CAS is caused by the geometric differences between the tricuspid and bicuspid valves or whether the increase in risk is caused by the same underlying factors that produce the geometric difference. CAS progression is understood to be a multiscale process, mediated at the cell level. In this study, we employ multiscale finite-element simulations of the valves. We isolate the effect of one geometric factor, the number of cusps, in order to explore its effect on multiscale valve mechanics, particularly in relation to CAS. The BAV and TAV are modeled by a set of simulations describing the cell, tissue, and organ length scales. These simulations are linked across the length scales to create a coherent multiscale model. At each scale, the models are three-dimensional, dynamic, and incorporate accurate nonlinear constitutive models of the valve leaflet tissue. We compare results between the TAV and BAV at each length scale. At the cell-scale, our region of interest is the location where calcification develops, near the aortic-facing surface of the leaflet. Our simulations show the observed differences between the tricuspid and bicuspid valves at the organ scale: the bicuspid valve shows greater flexure in the solid phase and stronger jet formation in the fluid phase relative to the tricuspid. At the cell-scale, however, we show that the region of interest is shielded against strain by the wrinkling of the fibrosa. Thus, the cellular deformations are not significantly different between the TAV and BAV in the calcification-prone region. This result supports the assertion that the difference in calcification observed in the BAV versus TAV may be due primarily to factors other than the simple geometric difference between the two valves.

Tissue elasticity estimation with optical coherence elastography: toward mechanical characterization of in vivo soft tissue.

High-resolution imaging provides a significant means for accurate material modulus estimation and mechanical characterization. Within the realm of in vivo soft tissue characterization, particularly on small biological length scales such as arterial atherosclerotic plaques, optical coherence tomography (OCT) offers a desirable imaging modality with higher spatial resolution and contrast of tissue as compared with intravascular ultrasound (IVUS). Based on recent advances in OCT imaging and elastography, we present a fully integrated system for tissue elasticity reconstruction, and assess the benefits of OCT on the distribution results of four representative tissue block models. We demonstrate accuracy, with displacement residuals on the order of 10−6 mm (more than 3 orders of magnitude less than average calculated displacements), and high-resolution estimates, with the ability to resolve inclusions of 0.15 mm diameter.

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We propose a new approach for representing biological sequences. This method, named protein-vectors or ProtVec for short, can be utilized in bioinformatics applications such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. Using the Skip-gram neural networks, protein sequences are represented with a single dense n-dimensional vector. This method was evaluated by classifying protein sequences obtained from Swiss-Prot belonging to 7,027 protein families where an average family classification accuracy of 94%± 0.03% was obtained, outperforming existing family classification methods. In addition, our model was used to predict disordered proteins from structured proteins. Two databases of disordered sequences were used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences were distinguished from structured Protein Data Bank (PDB) sequences with 99.81% accuracy, and unstructured DisProt sequences from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, information about protein structure can be determined with high accuracy. This so-called embedding model needs to be trained only once and can then be used to ascertain a diverse set of information regarding the proteins of interest. In addition, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. Our Web-based tool and trained data is available at Life Language Processing Website: http://llp.berkeley.edu, and will be regularly updated for calculation/classification of ProtVecs as well as visualization of biological sequences.We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.

Nuclear pore complex: biochemistry and biophysics of nucleocytoplasmic transport in health and disease.

Nuclear pore complexes (NPCs) are the gateways connecting the nucleoplasm and cytoplasm. This structures are composed of over 30 different proteins and 60-125 MDa of mass depending on type of species. NPCs are bilateral pathways that selectively control the passage of macromolecules into and out of the nucleus. Molecules smaller than 40 kDa diffuse through the NPC passively while larger molecules require facilitated transport provided by their attachment to karyopherins. Kinetic studies have shown that approximately 1000 translocations occur per second per NPC. Maintaining its high selectivity while allowing for rapid translocation makes the NPC an efficient chemical nanomachine. In this review, we approach the NPC function via a structural viewpoint. Putting together different pieces of this puzzle, this chapter confers an overall insight into what molecular processes are engaged in import/export of active cargos across the NPC and how different transporters regulate nucleocytoplasmic transport. In the end, the correlation of several diseases and disorders with the NPC structural defects and dysfunctions is discussed.

On the multiscale modeling of heart valve biomechanics in health and disease

Theoretical models of the human heart valves are useful tools for understanding and characterizing the dynamics of healthy and diseased valves. Enabled by advances in numerical modeling and in a range of disciplines within experimental biomechanics, recent models of the heart valves have become increasingly comprehensive and accurate. In this paper, we first review the fundamentals of native heart valve physiology, composition and mechanics in health and disease. We will then furnish an overview of the development of theoretical and experimental methods in modeling heart valve biomechanics over the past three decades. Next, we will emphasize the necessity of using multiscale modeling approaches in order to provide a comprehensive description of heart valve biomechanics able to capture general heart valve behavior. Finally, we will offer an outlook for the future of valve multiscale modeling, the potential directions for further developments and the challenges involved.

Computational modeling of axonal microtubule bundles under tension.

Microtubule bundles cross-linked by tau protein serve a variety of neurological functions including maintaining mechanical integrity of the axon, promoting axonal growth, and facilitating cargo transport. It has been observed that axonal damage in traumatic brain injury leads to bundle disorientation, loss of axonal viability, and cognitive impairment. This study investigates the initial mechanical response of axonal microtubule bundles under uniaxial tension using a discrete bead-spring representation. Mechanisms of failure due to traumatic stretch loading and their impact on the mechanical response and stability are also characterized. This study indicates that cross-linked axonal microtubule bundles in tension display stiffening behavior similar to a power-law relationship from nonaffine network deformations. Stretching of cross-links and microtubule bending were the primary deformation modes at low stresses. Microtubule stretch was negligible up to tensile stresses of ∼1 MPa. Bundle failure occurred by failure of cross-links leading to pull-out of microtubules and loss of bundle integrity. This may explain the elongation, undulation, and delayed elasticity of axons following traumatic stretch loading. More extensively cross-linked bundles withstood higher tensile stresses before failing. The bundle mechanical behavior uncovered by these computational techniques should guide future experiments on stretch-injured axons.

Cellular mechanotransduction : diverse perspectives from molecules to tissues

1. Introduction Roger D. Kamm and Mohammad R. K. Mofrad 2. Endothelial mechanotransduction Peter F. Davies and Brian P. Helmke 3. Role of the plasma membrane in endothelial cell mechanosensation of shear stress Peter J. Butler and Shu Chien 4. Mechanotransduction by membrane mediated activation of G protein coupled receptors and G proteins Yan-Liang Zhang, John A. Frangos and Mirianas Chachisvilis 5. Cellular mechanotransduction: interactions with the extracellular matrix Andrew D. Doyle and Kenneth M. Yamada 6. Role of ion channels in cellular mechanotransduction: lessons from the vascular endothelium Abdul I. Barakat and Andrea Gojova 7. Towards a modular analysis of cell mechano-sensing and transduction: an operations manual for cell mechanics Benjamin J. Dubin-Thaler and Michael P. Sheetz 8. Tensegrity as a mechanism for integrating molecular and cellular mechanotransduction mechanisms Donald E. Ingber 9. Nuclear mechanics and mechanotransduction Shinji Deguchi and Masaaki Sato 10. Microtubule bending and breaking in cellular mechanotransduction Andrew D. Bicek, Dominique Seetapun, and David J. Odde 11. A molecular perspective on mechanotransduction in focal adhesions Seung E. Lee, Roger D. Kamm and Mohammad R. K. Mofrad 12. Protein conformational change: a molecular basis of mechanotransduction Gang Bao 13. Translating mechanical force into discrete biochemical signal changes: multimodularity imposes unique properties to mechanotransductive proteins Vesa P. Hytonen, Michael L. Smith and Viola Vogel 14. Mechanotransduction through local autocrine signaling Nikola Kojic and Daniel J. Tschumperlin 15. The interaction between fluid-wall shear stress and solid circumferential strain affects endothelial cell mechanobiology John M. Tarbell 16. Micro- and nanoscale force techniques for mechanotransduction Nathan J. Sniadecki, Wesley R. Legant and Christopher S. Chen 17. Mechanical regulation of stem cells: implications in tissue remodeling Kyle Kurpinski, Randall R. R. Janairo, Shu Chien and Song Li 18. Mechanotransduction: role of nuclear pore mechanics and nucleocytoplasmic transport Christopher B. Wolf and Mohammad R. K. Mofrad 19. Summary and outlook Mohammad R. K. Mofrad and Roger D. Kamm.

Brownian Dynamics Simulation of Nucleocytoplasmic Transport: A Coarse-Grained Model for the Functional State of the Nuclear Pore Complex

The nuclear pore complex (NPC) regulates molecular traffic across the nuclear envelope (NE). Selective transport happens on the order of milliseconds and the length scale of tens of nanometers; however, the transport mechanism remains elusive. Central to the transport process is the hydrophobic interactions between karyopherins (kaps) and Phe-Gly (FG) repeat domains. Taking into account the polymeric nature of FG-repeats grafted on the elastic structure of the NPC, and the kap-FG hydrophobic affinity, we have established a coarse-grained model of the NPC structure that mimics nucleocytoplasmic transport. To establish a foundation for future works, the methodology and biophysical rationale behind the model is explained in details. The model predicts that the first-passage time of a 15 nm cargo-complex is about 2.6±0.13 ms with an inverse Gaussian distribution for statistically adequate number of independent Brownian dynamics simulations. Moreover, the cargo-complex is primarily attached to the channel wall where it interacts with the FG-layer as it passes through the central channel. The kap-FG hydrophobic interaction is highly dynamic and fast, which ensures an efficient translocation through the NPC. Further, almost all eight hydrophobic binding spots on kap-β are occupied simultaneously during transport. Finally, as opposed to intact NPCs, cytoplasmic filaments-deficient NPCs show a high degree of permeability to inert cargos, implying the defining role of cytoplasmic filaments in the selectivity barrier.