Ms. Anwaar Al-Zireeni (presently CEO and Co-founder of Diagnostics Startup Privail Inc.)
Dr. Ehsaneddin Asgari (presently Post-doc at Helmholtz Center for Infection and UC Berkeley)
Dr. Amirhossein Arzani (presently Assistant Professor of Mechanical Engineering at Northern Arizona University)
Dr. Mohammad Azimi (presently Quantum Software Engineering Manager at Rigetti Computing)
Mr. Alex Baelde (presently in Healthcare Project Manager at Poly-Shape)
Dr. Ahmed Bakhaty [co-advised with Prof. Sanjay Govindjee](presently Data Scientist at WorkDay)
Ms. Heather Bowerman (presently CEO and Founder at Dot Labs)
Mr. Jack Bulat (presently at U. Penn School of Medicine)
Dr. Preethi Chandran (presently Associate Professor of Chemical Engineering at Howard University)
Dr. Javad Golji (presently Senior Research Scientist at Novartis Institute for Biomedical Research (NIBR))
Dr. Zainab Haydari
Dr. Zeinab Jahed (presently Assistant Professor of Nanoengineering at UCSD)
Dr. Yousef Jamali (presently Assistant Professor of Bio-Mathematics at Tarbiat Modares University)
Dr. Helene Karcher [co-advised with Prof. Roger Kamm](presently Senior Research Scientist at Novartis)
Dr. Reza Karimi (presently Lead Data Scientist at Elsevier)
Dr. Ahmad Khalil (presently Associate Professor of Biomedical Engineering at Boston University)
Dr. Kevin S. Kolahi (presently Medical Resident at Stanford School of Medicine)
Dr. Joseph Leach [co-advised with Prof. David Saloner](presently Assistant Professor of Radiology and Biomedical Imaging at UCSF)
Dr. Seung Lee [co-advised with Prof. Roger Kamm](presently Co-Founder at Vintage Lab)
Dr. Ali Madani (presently Post-Doc at Salesforce Research)
Dr. Mehrdad Mehrbod (presently CEO at NextGen Sequencing startup Riz-Araye Sharif)
Dr. Seyed Hanif Mahboobi (presently Senior Data Scientist at Amazon Web Services (AWS))
Dr. Kranthi Mandadapu (presently Assistant Professor of Chemical and Biomolecular Engineering at UC Berkeley)
Dr. Hassan P. Modarres(presently Founder and CEO of biomedical start-up company)
Dr. Ruhollah Moussavi-Baygi (presently Post-Doc at UCSF)
Dr. Ioanna Pagani [co-advised with Prof. Sanjay Kumar](presently a Staff Scientist, Bioinformatics at Life Technologies Corporation)
Dr. Sephalie Patel (presently Anesthesiologist at Moffitt Cancer Center)
Mr. Stephen Peter (presently Senior R&D Engineer at Ceterix Orthopaedics)
Dr. Mohaddeseh Peyro (presently Post-Doc)
Dr. Nur Aida Abdul Rahim [co-advised with Profs. Roger Kamm and Peter So](presently technology consultant at Luna Innovations Inc)
Dr. Seyyede Fatemeh Seyyedsalehi (visiting PhD student)
Dr. Amir Shamloo (presently Associate Professor of Mechanical Engineering at Sharif University of Technology)
Dr. Dena Shahriari (presently Assistant Professor of medicine at UBC)
Dr. Hengameh Shams (presently Post-Doc at UCSF Weill Institute for Neurosciences)
Dr. Mohammad Soheilypour (presently at CEO and Co-Founder of Nexilico Inc.)
Mr. Daniel Shreter (presently at Director WW OEM Marketing at Microsoft)
Mr. Nima Vahdati Nia (presently R&D Engineer at Edwards Lifesciences)
Dr. Ashkan Vaziri (presently Professor of Mechanical Engineering at Northeastern University)
Dr. Eli J. Weinberg (presently at McKinsey & Co.)
Dr. Sang-Hee Yoon (presently Associate Professor of Mechanical Engineering at Inha University, South Korea)
Mr. Peter White (presently Vice-President of Product Management at Salesforce)
Dr. Ting Zhu (presently Associate Professor of Life Sciences at Tsinghua University, China)
The mission of our research program is to understand the molecular basis of human diseases via state-of-the-art molecular biophysics and computational biology/biomechanics approaches.
Mechanical phenomena affect nearly every aspect of cellular biology and function, yet the underlying mechanisms of how mechanical forces and biochemical signals interact is not clearly understood. By understanding the regulatory mechanisms involved in these cellular signaling processes, we aim to shed light on their role in human disease. Currently, our lab is engaged in the following specific projects:
I. Molecular pathways involved in cell mechano-chemical signal transduction (or “mechanotransduction”), coupling the extracellular matrix to the nucleus
II. Bacterial mechanotransduction and microbiomes
III. Deep learning for biology (“deep proteomics” and “deep genomics”) and medicine (“deep medicine”)
IV. Multiscale models of cardiovascular biomechanics
These projects are briefly described below:
I. LOOKING “UNDER THE HOOD” OF CELLULAR MECHANOTRANSDUCTION
Mechanics of Integrin-Mediated Focal Adhesions. The underlying mechanics and mechanisms of mechanotransduction are not yet clearly understood. One hypothesis is that forces transmitted via individual proteins, either at the site of cell adhesion to its surroundings or within the stress-bearing members of the cytoskeleton, cause conformational changes that change the binding affinity of these proteins to other intracellular molecules. This altered equilibrium state can subsequently initiate biochemical signaling cascades or produce immediate structural changes. Force-induced conformational changes (‘deformations’) in proteins may play a critical role in initiating and controlling cell signaling pathways. To understand mechanotransduction at the molecular level requires detailed analysis of protein molecular conformational changes that occur in response to forces, which can be exerted by extracellular matrix through the cellular membrane or the cytoskeleton.
Cytoskeletal Organization and Mechanics. We study actin cross-linking by α-actinin and its further reinforcement with vinculin (Shams et al. Submitted).
Axonal Microtubule Mechanics. We also investigate the mechanical properties of microtubules and microtubular bundles, e.g. in neuronal axons under tension (Mehrbod et al. 2011; Peter et al. 2012) along with buckling (Soheilypour et al. 2015) and torsional behavior (Lazarus et al. 2015). Axonal microtubules (MTs) are bundled by interconnections mediated by MT-associated protein (MAP) tau and form polarized arrays located in the interior portion of the axon. While today’s imaging methods are not able to detect these microscopic damages to axons, computational modeling can efficiently simulate the neuronal components subjected to external forces and substantially improve contemporary understanding of the underlying damage mechanisms. We have developed a computational model of axonal MTs to explore the biomechanical behavior of axon components under excessive compressive, tensile, and torsional forces. This work has implications in our understanding of traumatic brain injury.
Actin Reorganization through Dynamic Interactions with Single-Wall Carbon Nanotubes.In another project, we investigated the direct interaction of actin with single-walled carbon nanotubes (SWCNT) using all-atom molecular dynamics simulations. Single-wall carbon nanotubes (SWCNTs) have been widely used for biological applications in recent years, and thus, it is critical to understand how these inert nanomaterials influence cell behavior. We showed that actin can stably bind to the SWCNT surfaces via hydrophobic interactions but still allows nanotubes to slide and rotate on the actin surface. Our results establish several nanoscale conformational changes for the actin–SWCNT complexes, and we suggest these changes likely induce reorganization of actin filaments observed at larger scales (Shams et al. 2013).
The Connection of Cytoskeleton to Nucleus. The genetic information of eukaryotic cells is enclosed within a double-layered nuclear envelope, which comprises an inner and outer nuclear membrane. Several transmembrane proteins locate to the nuclear envelope; however, only two integral protein complexes span the nuclear envelope and connect the inside of the nucleus to the cytoplasm. The nuclear pore complex (NPC) acts as a gateway for molecular exchange between the interior of the nucleus and the cytoplasm, whereas so-called LINC complexes physically link the nucleoskeleton and the cytoskeleton. The assembly of NPCs and their even distribution throughout the nuclear envelope is dependent on components of the LINC complex. Additionally, LINC complex formation is dependent on the successful localization of inner nuclear membrane components of LINC complexes and their transport through the NPC. Furthermore, the architecture of the nuclear envelope depends on both protein complexes. Finally, the LINC complexes can affect nucleocytoplasmic transport through the NPC. Understanding the NPC and LINC complexes, the relation between them and how they govern the mechanics and transport of nucleo-cytoskeletal connection offers important insight into mechanobiology of several human diseases.
Mechanochemistry of the Nuclear Pore Complex. Nuclear pore complex (NPC) is the sole gateway for bidirectional transport of vital cargos, ranging from different functional proteins to RNAs and ribosomes, between the cytoplasm and the nucleus in eukaryotic cells. The complex, yet delicate, geometry of the NPC and the fine spatiotemporal resolution at which the nucleocytoplasmic transport takes place have so far hindered the direct, experimental investigation of this exquisite nanopore. Using a hybrid of state-of-the-art computational modeling approaches, ranging from finite element (Wolf et al. 2008) to coarse-grained Brownian dynamics (Moussavi-Baygi et al. 2016) to molecular dynamics techniques (Zhao et al. 2014) to new agent-based modeling methods (Azimi et al. 2014), we study the structure and function of the nuclear pore complex and the dynamics of nucleocytoplasmic traffic. Understanding the biomechanics of the nuclear pore complex and nucleocytoplasmic transport will broadly impact our understanding of viral diseases and will revolutionize therapeutic approaches (e.g. gene therapy) and open the door to many industrial applications of biomimetic artificial nanopores.
mRNA export and quality control. Following transcription, messenger ribonucleic acids (mRNAs) are transported to the cytoplasm to transfer genetic information and direct synthesis of functional proteins. Export of mRNAs into the cytoplasm is meticulously quality controlled by intricate mechanisms in eukaryotic cells. Despite the significance of these processes, many aspects of them are still poorly understood. We are developing and employing different computational techniques, such as agent-based modeling and molecular dynamics, to shed light on these essential processes in mRNA biogenesis (Soheilypour et al. 2016).
II. BACTERIAL MECHANOTRANSDUCTION AND MICROBIOMES
Bacterial cells involve mechanisms that are similar to mammalian cells to some extent but are also different in many aspects. Bacterial adhesion, which is subject to biochemical and mechanical factors, is relevant in the pathogenesis of bacterial infections and community-level dynamics such as the human microbiome. Bacteria are lined with cell wall anchored proteins, which play a critical role in the molecular basis of adhesion to many surfaces and proteins. A broad range of human diseases is associated with bacterial infections, often initiated by specific adhesion of a bacterium to the target environment. Despite the significant role of bacterial adhesion in human infectious diseases, details and mechanisms of bacterial adhesion have remained elusive. We combine experimental and computational methods to study the adhesion characteristics of Staphylococcus aureus.
Staphylococcus Aureus Adhesion at the Cellular and Molecular Levels.We are exploring the interaction of S. Aureus bacterial cells with nanostructures of various size and geometry (Jahed et al. 2014), and studying force sensing of S. Aureus surface proteins by examining the interplay of fibronectin binding protein A (FnBPA) of S. Aureus with fibrinogen, fibronectin and integrin. We are also examining the structural mechanics of a characteristic bacterial collagen-binding adhesion (CNA) of Staphylococcus Aureus (Madani et al. 2017).
III. DEEP LEARNING FOR BIOLOGY AND MEDICINE
“Deep Genomics” and “Deep Proteomics”: Application of Deep Learning and Language Processing in Biology.
A broad and simple definition of `language’ is a set of sequences constructed from a finite set of symbols. By this definition, biological sequences, natural languages, and many sequential phenomena exist in the world can be considered as languages. Although this definition is simple, it includes languages employing very complicated grammars in the creation of their sequences of symbols. Examples are biophysical principles governing biological sequences (e.g., DNA, RNA, and protein sequences), as well as grammars of natural languages determining the structure of clauses and sentences (Asgari and Mofrad, 2019).
We have a data-driven and language-agnostic point of view in the processing of biological sequences.
We use two main strategies for this purpose:
- (i) character-level or more accurately subsequence-level processing
of languages, which allows for simple modeling of the sequence similarities based on local information or bag-of-subsequences
- (ii) language model-based representation of sequences encoding contextual information of sequence elements trained using neural networks.
We work on data-driven and subsequence-based language processing to address important research problems in proteomics and metagenomics:
(i) Machine Learning and Deep Language Processing for Protein Informatics
One of the main challenges in proteomics is that there exists a large gap between the number of known protein sequences and known protein tertiary structures and functions. The central question we address is how to efficiently use a large amount of existing primary sequences to achieve a better performance in the structural and functional annotation of protein sequences. We proposed a data-driven subsequence-based representations of protein sequences and their language model-based embeddings trained over a large dataset of protein sequences, called protein vectors (Asgari and Mofrad, 2015). In addition, we introduced a motif discovery approach benefiting from probabilistic segmentation of protein sequences to find functional and structural motifs (Asgari et al., 2019). This segmentation is also inferred from large protein sequence datasets in a data-driven manner. Protein vectors had a seminal contribution in protein informatics and now are widely used for machine learning based protein structure and function annotations.
(ii) Machine Learning and Deep Language Processing for Microbial Informatics
Microbial communities exist almost on every accessible surface on earth, supporting, regulating, and even causing unwanted conditions (e.g. diseases) to their hosts and environments. Detection of the host phenotype and the phenotype-specific taxa from the microbial samples is one of the prominent problems in metagenomics. For instance, identifying distinctive taxa for microbiome-related diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on the accuracy of microbiome analysis techniques. We develop data-driven methods to perform machine learning on 16S rRNA sequencing, which is the most cost-effective approach for sequencing of microbial communities to date.
We have propose alignment- and reference- free methods, called MicroPheno (Asgari et al., 2018) and DiTaxa (Asgari et al., 2018), which are mainly designed for microbial phenotype and biomarker detection, respectively.
MicroPheno is a k-mer based approach achieving the state-of-the-art performance in the host phenotype prediction from 16S rRNA outperforming conventional features, Operational Taxonomic Units (OTUs) (Asgari et al., 2018).
DiTaxa, substitutes standard OTU-clustering by segmenting 16S rRNA reads into the most frequent variable-length subsequences. We compared the performance of DiTaxa to the state-of-the-art methods in phenotype and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis, and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa performed competitively to MicroPheno (the state-of-the-art approach) in phenotype prediction while outperforming the OTU-based state-of-the-art approach in finding biomarkers in both resolution and coverage evaluated over known links from literature and synthetic benchmark datasets (Asgari et al., 2018).
“Deep Medicine”: Deep Learning for Clinical Classifications and Diagnostics. Advances in artificial intelligence have the potential for transformative change in the field of medicine. We are using computer vision techniques for varying imaging modalities from CT, MR, ultrasound, and X-ray to automate disease classification and segmentation (Madani et al. Deep Echocardiography. 2018).
In addition, through the use of unsupervised learning, we are gaining further insights in analyzing cardiovascular disease. Many of our deep learning models outperform clinician performance for routine tasks. However, we hope to focus our computational research efforts to assist and expand the capabilities of modern medicine to improve patient care.
IV. MECHANICS OF HUMAN DISEASE
A long-term goal of our research program has been to understand the role of mechanics and mechanotransduction in human diseases, in particular, cardiovascular diseases like atherosclerosis and aortic valve calcification, and cancer.
Multiscale Mechanics of the Aortic Heart Valve: A Mechanotransduction Perspective on Calcific Aortic Stenosis. The human heart is a pump system consisting of four chambers and four valves. When functioning correctly, the valves open widely to allow blood through and seal securely shut. Valvular disease inhibits a valve’s ability to open and close, decreasing the efficiency of the heart and leading to a variety of other cardiovascular disorders. Aortic valves (AV) control the flow of oxygen-rich blood from the left ventricle to the aorta and thereby the rest of the body. Normally, the aortic valve functions very efficiently, providing negligible resistance to forward flow and allowing minimal backflow. The mechanical function of the aortic valve can, however, be affected by pathological conditions. The most common valvular disease is calcific aortic stenosis (CAS). In CAS, the aortic valve undergoes changes very similar to those seen in atherosclerosis. First, inflammatory cells migrate to the site. Monocytes adhere to the endothelial layer, infiltrate it, and differentiate into macrophages. The macrophages send intracellular signals to nearby fibroblasts, causing the fibroblasts to promote cellular proliferation and matrix remodeling. Macrophages add calcium deposits to the matrix. Eventually, the remodeled matrix and calcium deposits build up to yield a thickened, stiffened leaflet. These changes can affect mechanical function, a result known as stenosis. To understand the mechanisms of calcific aortic stenosis, and to evaluate methods of prevention and treatment for this disease, we develop computational models of aortic valve mechanics.
Role of Mechanics and Mechanotransduction in Arterial Disease. We have a long-term interest in understanding and characterization of atherosclerotic plaques. Due to focal nature of this disease, mechanical factors (namely, arterial wall mechanics, hemodynamics, and mass transport patterns) are widely believed to play a key role in initiation and progression of atherosclerosis. Rigorous assessment of potential links between such mechanical factors and atherosclerosis, however, requires detailed studies using realistic, subject-specific models. Over the past several years, we have developed models for computational simulation of hemodynamics and wall mechanics in patient-specific carotid bifurcations.
Last updated October 2018
| On the Nuclear Pore Complex and Its Emerging Role in Cellular Mechanotransduction
Atsushi Matsuda, Mohammad R. K. Mofrad.
APL Bioengineering (special issue on Mechanobiology of the Cell Nucleus), 6, 011504 (2022); doi: 10.1063/5.0080480
(KEYWORDS: Nuclear Pore Complex, Mechanotransduction)
| Methylation at a conserved lysine residue modulates tau assembly and cellular functions
Hengameh Shams, Atsuko Matsunaga, Qin Ma, Mohammad R.K. Mofrad, and Alessandro Didonna
Molecular and Cellular Neuroscience 120 (2022) 103707.
(KEYWORDS: Tau Protein Methylation, Molecular Dynamics)
| A short HLA-DRA isoform binds the HLA-DR2 heterodimer on the outer
domain of the peptide-binding site
Hengameh Shams, Jill A. Hollenbach, Atsuko Matsunaga, Mohammad R.K. Mofrad, Jorge R. Oksenberg a, and Alessandro Didonna
Archives of Biochemistry and Biophysics 719 (2022) 109156
(KEYWORDS: immune system, human leukocyte antigen, antigen-presenting cells, Molecular Dynamics)
| Acid-Sensitive Surfactants Enhance the Delivery of Nucleic Acids
Joachim Justad Røise, Hesong Han, Jie Li, D. Lucas Kerr, Chung Taing, Kamyar Behrouzi, Maomao He, Emily Ruan, Lienna Y. Chan, Eli M. Espinoza, Sören Reinhard, Kanav Thakker, Justin Kwon, Mohammad R. K. Mofrad, and Niren Murthy.
Molecular Pharmaceutics. 19(1):67–79, 2022
(KEYWORDS: Nucleic Acids)
| Characterizing Binding Interactions That Are Essential for Selective Transport through the Nuclear Pore Complex
Lennon KM, Soheilypour M, Peyro M, Wakefield DL, Choo GE, Mofrad MRK* and Jovanovic-Talisman T*.
International Journal of Molecular Sciences. 22(19):10898, 2021
(KEYWORDS: Nuclear pore complex, Nucleoporins)
| Free Energy Calculations Shed Light on the Nuclear Pore Complex’s Selective Barrier Nature.
A. Matsuda, MRK Mofrad.
Biophysical Journal, 20(17):3628-3640, SEPTEMBER 07, 2021 [Cover]
(KEYWORDS: Nuclear pore complex, free energy, Selective Permeable.Entry in the Biophysical Journal Blog:
Nature’s Most Exquisite Nanopore and Its Vital Role in Cell Biology
| Nucleoporins’ exclusive amino acid sequence features regulate their transient interaction with and selectivity of cargo complexes in the nuclear pore.
Peyro M, Dickson AM, MRK Mofrad.
Molecular Biology of the Cell, 2021, In Press.
(KEYWORDS: Nuclear pore complex, Selective Permeable.)
| FG Nucleoporins feature unique amino acid sequence patterns that distinguish them from other IDPs.
M Peyro*, M Soheilypour*, VS Nibber, AM Dickson, MRK Mofrad.
Biophysical Journal, 120(16):3382-3391, AUGUST 17, 2021
(KEYWORDS: Nuclear pore complex, intrinsically disordered proteins.)
| TripletProt: Deep Representation Learning of Proteins based on Siamese Networks
Nourani E, Asgari E, McHardy AC, and Mofrad MRK.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021 (In Press).
(KEYWORDS: Proteomics, Deep Learning, Neural Networks.)
| Molecular models of LINC complex assembly at the nuclear envelope.
Z Jahed*, N Domkam, J Ornowski, G Yerima, MRK Mofrad*.
Journal of Cell Science 134 (12), jcs258194
(KEYWORDS: LINC Complex, Nuclear Mechanotransduction)
| Drug delivery and adhesion of magnetic nanoparticles coated nanoliposomes and microbubbles to atherosclerotic plaques under magnetic and ultrasound fields
Alishiri M, Ebrahimi S, Shamloo A, Boroumand A, Mofrad MRK
Engineering Applications of Computational Fluid Mechanics, 15(1):1703-725, 2021
(KEYWORDS: RNA, DNA, Proteins)
| Atomic Scale Interactions between RNA and DNA Aptamers with the TNF-α Protein
Asadzadeh H, Moosavi A, Alexandrakis G, Mofrad MRK
BioMed Research International (Special Issue on Non-coding RNA in Cardiovascular Disease), 2021
| EpitopeVec: Linear Epitope Prediction Using Deep Protein Sequence Embeddings.
Bahai A, Asgari E, Mofrad MRK, Kloetgen A, McHardy AC.
Bioinformatics:btab467. doi: 10.1093/bioinformatics/btab467.
(KEYWORDS: Deep Proteomics, ProtVec)
| Hydrodynamic interactions significantly alter the dynamics of actin networks and result in a length scale dependent loss modulus.
R Karimi, MR Alam, MRK Mofrad
Journal of Biomechanics 120, 110352
(KEYWORDS: Cytoskeleton, Cell mechanics, Cytoskeletal Rheology)
Intranuclear strain in living cells subjected to substrate stretching: A combined experimental and computational study.
| PFP-WGAN: Protein function prediction by discovering Gene Ontology term correlations with generative adversarial networks.
SF Seyyedsalehi, M Soleymani, HR Rabiee, MRK Mofrad
PLoS One 16 (2), e0244430.
(KEYWORDS: Protein function; Gene Ontology; GAN)
| Strain-stiffening and strain-softening responses in random viscoelastic fibrous networks: interplay between fiber orientation and viscoelastic softening.
N Zolfaghari, M Moghimi Zand, MRK Mofrad
Soft Materials 18 (4), 373-385.
(KEYWORDS: Random fibrous network; viscoelastic fibers; collagen; strain-softening; strain-stiffening)
| Quantification of human sperm concentration using machine learning-based spectrophotometry.
A Lesani, S Kazemnejad, MM Zand, M Azadi, H Jafari, MRK Mofrad, R. Nosrati
Computers in Biology and Medicine 127, 104061
(KEYWORDS: Machine Learning Application)
| Machine learning for endoleak detection after endovascular aortic repair
Talebi S*, Madani MH*, Madani A, Chien A, Shen J, Mastrodicasa D, Fleischmann D, Chan FP*, Mofrad MRK*.
Scientific Reports. 2020DOI: 10.1038/s41598-020-74936-7
(KEYWORDS: Cardiovascular disease, machine learning.)
|A splice acceptor variant in HLA-DRA affects the conformation and cellular localization of the class II DR alpha-chain
Didonna A, Damotte V, Shams H, Matsunaga A, Caillier S, Dandekar R, Misra M, Mofrad M, Oksenberg J, Hollenbach JImmunology. 2020
(KEYWORDS: neurodegenerative disease, human leukocyte antigen, immune response, antigen presentation, protein folding.)
|Killer Cell Immunoglobulin-like Receptor Variants Are Associated with Protection from Symptoms Associated with More Severe Course in Parkinson Disease
Anderson KM, Augusto DG, Dandekar R, Shams H, Zhao C, Yusufali T, Montero-Martın G, Marin WM, Nemat-Gorgani N, Creary LE, Caillier L, Mofrad MRK, Parham P, Fernandez-Vina M, Oksenberg JR, Norman PJ, Hollenbach JA
Journal of Immunology. 2020
DOI: 10.4049/jimmunol.2000144.(KEYWORDS: neurodegenerative disease, Killer cell immunoglobulin-like receptor 3DL1 protein)
|Nanoscale integrin cluster dynamics controls cellular mechanosensing via FAK Y397 phosphorylation.
Cheng B, Wan W, Huang G, Li Y, Genin GM, Mofrad MRK, Lu TJ, Xu F, Lin M.
Science Advance. 6(10):eaax1909
DOI: 10.1126/sciadv.aax19092020.(KEYWORDS: Cellular Mechanotransduction, Focal Adhesions, FAK, Integrin Clustering)
|Kindlin Assists Talin to Promote Integrin Activation
Haydari Z, Shams H, Jahed Z, Mofrad MRK.
Biophysical Journal. 118(8):1977-1991, APRIL 21, 2020(KEYWORDS: Cellular Mechanotransduction, Focal Adhesions, FAK, Integrin Activation, Kindlin, Talin)
|Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics.
Khaledi A, Weimann A, Schniederjans M, Asgari E, Kuo TH, Oliver A, Cabot G, Kola A, Gastmeier P, Hogardt M, Jonas D, Mofrad MRK, Bremges A, McHardy AC, Häussler S.
EMBO Molecular Medicine. e10264, 2020.(KEYWORDS: Bioinformatics, Machine Learning)
|The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.
Zhou N et al.
Genome Biology, 20:244, 2019(KEYWORDS: Proteomics, Bioinformatics, protein function prediction)
|Talin is required to increase stiffness of focal molecular complex in its early formation process.
Nakao N, Maki K, Mofrad MRK, Adachi T
Biochemical and Biophysical Research Communications, 518 (3):579-583, 2019.(KEYWORDS: Focal Adhesions, Talin, Mechanotransduction)
|Structural Basis of the Differential Binding of Engineered Knottins to Integrins αVβ3 and α5β1.
Van Agthoven JF, Shams H et al.
Structure, 27, 1443–1451, 2019.(KEYWORDS: Integrins, αVβ3, α5β1)
|Role of KASH domain lengths in the regulation of LINC complexes
Jahed Z, Hao H, Thakkar V, Vu UT, Valdez VA, Rathish A, Tolentino C, Kim SJC, Fadavi D, Starr DA, Mofrad MRK.
Molecular Biology of the Cell (MBoC), 30(16):2076-2086.(KEYWORDS: LINC Complex, SUN/KASH, Nuclear Mechanics, Mechanotransduction)
|Sex-specific Tau methylation patterns and synaptic transcriptional alterations are associated with neural vulnerability during chronic neuroinflammation
Didonna A, Cantó E, Shams H, Isobe N, Zhao C, Caillier SJ, Condello C, Yamate-Morgan H, Tiwari-Woodruff SK,
Mofrad MRK, Hauser SL, Oksenberg JR
Journal of Autoimmunity. 101:56-692, 2019.(KEYWORDS: Tau protein, multiple sclerosis, post-translational modifications, neuroinflammation)
|Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis
Madani A, Bakhaty A, Kim J, Mubarak Y, Mofrad MRK.
ASME J. Biomechanical Engineering. 141(8): 084502, 2019(KEYWORDS: Machine Learning, Deep Learning, Atherosclerois, Finite Element Modeling)
|Kindlin is mechanosensitive: A force-induced conformational switch mediates intracellular crosstalk among integrins
Jahed Z, Haydari Z, Mofrad MRK.
Biophysical Journal. 116(6): 1011-24, 2019.(KEYWORDS: Cellular mechanotransduction, Focal adhesions, kindlin, integrin)
|The Nucleus Feels the Force, LINCed In or Not!
Jahed Z, Mofrad MRK.
Current Opinion in Cell Biology. Volume 58, June 2019, Pages 114-119(KEYWORDS: Nucleus, Mechanotransduction, Nuclear envelop, LINC complex, SUN/KASH)
|Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).
Asgari E, McHardy AC, Mofrad MRK.
Scientific Reports. (2019) 9:3577 doi:10.1038/s41598-019-38746-w(KEYWORDS: Deep Learning, Protein Bioinformatics, Deep Proteomics, ProtVec)
|DiTaxa: Nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection.
Asgari E, Münch PC, Lesker TR, McHardy AC, Mofrad MRK.
Bioinformatics. 2018 Nov 30. doi: 10.1093/bioinformatics/bty954. (KEYWORDS: Deep Learning, Microbiome, 16S rRNA data analysis)
|Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease
Madani A, Rui Ong J, Tibrewal A, Mofrad MRK.
npj Digital Medicine. 1: 59 (2018)(KEYWORDS: Deep Learning, Machine Learning, Medicine, Cardiac Disease)
|A molecular model for LINC complex regulation: Activation of SUN2 for KASH binding
Jahed Z, Vu UT, Fadavi D, Ke H, Rathish A, Kim SCJ, Feng W, Mofrad MRK.
Molecular Biology of the Cell (MBoC), 29(16):2012-2023, 2018.(KEYWORDS: LINC Complex, SUN/KASH, Nuclear Mechanics, Mechanotransduction)
|Conserved SUN-KASH interfaces mediate LINC complex-dependent nuclear movement and positioning
Cain NC, Jahed Z, Schoenhofen A, Valdez VA, Elkin B,Hao H, Harris NJ, Herrera LA, Woolums BW, Mofrad MRK, Luxton G, Starr DA
Current Biology, 28:3086–3097, October 8, 2018.(KEYWORDS: LINC Complex, SUN/KASH, Nuclear Mechanics, Mechanotransduction)
|MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples
Asgari E, Garakani K, McHardy AC, Mofrad MRK.
Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages i32–i42(KEYWORDS: Microbiome, Phenotypes, Genomics, Bioinformatics, Deep Learning, Machine Learning)
|Agent-based Modeling in Molecular Systems Biology
Soheilypour M, Mofrad MRK.
BioEssays, 2018 Jul;40(7):e1800020. doi: 10.1002/bies.201800020Chosen for BioEssays Issue Highlights(KEYWORDS: Molecular systems biology, Complex systems, Computational biology)
|Quality Control of mRNAs at the Entry of the Nuclear Pore: Cooperation in a Complex Molecular System
Soheilypour M, Mofrad MRK.
Nucleus, Volume 9, Issue 1, Pages 202-211.(KEYWORDS: Nuclear Pore Complex, mRNA Export, Mechanotransduction)
|Molecular insights into the mechanisms of SUN1 oligomerization in the nuclear envelope
Jahed Z, Fadavi D, Vu UT, Asgari E, Luxton GWG, Mofrad MRK.
Biophysical Journal, Volume 114, Issue 5, p1190–1203, 13 March 2018.(KEYWORDS: LINC Complex, Nuclear Mechanics, Mechanotransduction)
|Mechanical LINCs of the nuclear envelope: Where SUN meets KASH
Jahed Z, Mofrad MRK.
Extreme Mechanics Letters (special issue on Mechanobiology), Volume 20, April 2018, Pages 99-103.(KEYWORDS: LINC Complex, Nuclear Mechanics, Mechanotransduction)
|ProtDataTherm: A database for thermostability analysis and engineering of proteins
Modarres HP, Mofrad MRK, Sanati-Nezhad A.
PLoS One, 13(1): e0191222, 2018.(KEYWORDS: Bioinformatics, Protein Engineering)
|The ‘stressful’ life of cell adhesion molecules: On the mechanosensitivity of integrin adhesome
Shams H, Hoffman BD, Mofrad MRK.
ASME Journal of Biomechanical Engineering, 2017 Dec 22. doi: 10.1115/1.4038812(KEYWORDS: Focal Adhesions, Mechanotransduction, Integrin, Vinculin, Talin)
|Fast and accurate view classification of echocardiograms using deep learning
Madani A, Arnaout R, Mofrad MRK*, Arnaout R*.
npj Digital Medicine, In Press.(KEYWORDS: Deep neural networks, Machine learning, Echocardiography, Precision Medicine)
|A strain-based finite element model for calcification progression in aortic valves
Arzani A, Mofrad MRK.
Journal of Biomechanics, 2017 Dec 8;65:216-220.
(KEYWORDS: cardiovascular biomechanics, aortic valve)
|Interaction with α-actinin induces a structural kink in the transmembrane domain of β3-integrin and impairs signal transduction
Shams H, Mofrad MRK.
Biophysical Journal, 113(4):948–956, 2017.
(KEYWORDS: mechanotransduction, focal adhesions)
|Molecular mechanics of staphylococcus aureus adhesin, CNA, and the inhibition of bacterial adhesion by stretching collagen
Madani A, Garakani K, Mofrad MRK.
PLoS One, 2017 Jun 30;12(6):e0179601.
(KEYWORDS: bacterial mechanics)
|Mechanical contact characteristics of PC3 human prostate cancer cell on complex shaped silicon micropillars.
Seo BB, Jahed Z, Coggan JA, Chau YY, Rogowski JL, Gu FX, Wen W, Mofrad MRK, Tsui TY.
Materials. 2017, 10(8), 892; doi:10.3390/ma10080892.
(KEYWORDS: cellular mechanotransduction)
|A multiscale systems biology model of calcific aortic valve disease progression
Arzani A, Masters K, Mofrad MRK.
ACS Biomaterials Science & Engineering, 2017, 3 (11), pp 2922–2933.
(KEYWORDS: cardiovascular biomechanics, aortic valve)
|Consistent Trilayer Biomechanical Modeling of the Aortic Valve Leaflet Tissue
Bakhaty A, Govindjee S, , Mofrad MRK.
Journal of Biomechanics, 61, 1-10.
(KEYWORDS: cardiovascular biomechanics, aortic valve)
|Looking “Under the Hood” of Cellular Mechanotransduction with Multiscale Computational Tools: A Systems Biomechanics Approach
Shams H, Soheilypour M, Peyro M, Moussavi-Baygi R, Mofrad MRK.
ACS Biomaterials Science & Engineering, 2017, 3 (11), pp 2712–2726.
(KEYWORDS: cellular mechanotransduction, focal adhesions, nuclear pore complex, LINC complex)
|Mechanosensitive Conformation of Vinculin Regulates Its Binding to MAPK1
Garakani K, Shams H, Mofrad MRK.
Biophysical Journal, 112, 1885–1893, May 9, 2017.
(KEYWORDS: mechanotransduction, focal adhesions, vinculin, MAP Kinase)
|Bacterial Networks on Hydrophobic Micropillars
Jahed Z, Shahsavan H, Verma MS, Rogowski JL, Seo BB, Zhao B, Tsui TY, Gu FX, Mofrad MRK.
ACS Nano, 11 (1):675–683, 2017 doi: 10.1021/acsnano.6b06985.
(KEYWORDS: mechanotransduction, bacterial adhesions, bacterial networks)
|An agent-based model for mRNA export through the nuclear pore complex
Azimi M*, Bulat E*, Weis K, Mofrad MRK.
Molecular Biology of the Cell (MBoC): Special Issue on Quantitative Biology, 2014 Nov 5;25(22):3643-53.
|Regulation of RNA-binding proteins affinity to export receptors enables the nuclear basket proteins to distinguish and retain aberrant mRNAs
Soheilypour M, Mofrad MRK.
Scientific Reports, 6, Article number: 35380 (2016).
|The LINC and NPC relationship: it’s complicated!
Jahed Z, Soheilypour M, Peyro M, Mofrad MRK.
Journal of Cell Science, J Cell Sci 129.17, 3219-3229 (2016).
|Differential Collective and Single Cell Behaviors on Silicon Micropillar Arrays
Jahed Z, Zareian R, Chau Y, Seo B, West M, Tsui T, Wen W, Mofrad MRK.
ACS Applied Materials & Interfaces. 8 (36):23604–23613, 2016.
|Gelatin/chondroitin sulfate nanofibrous scaffolds for stimulation of wound healing: In-vitro and in-vivo study
Pezeshki-Modaress M, Mirzadeh H, Zandi M, Rajabi-Zeleti S, Sodeifi N, Aghdami N, Mofrad MRK.
J Biomed Mater Res A. (2016). doi: 10.1002/jbm.a.35890.
|Interferon Beta: From Molecular Level to Therapeutic Effects
Haji Abdolvahab M, Mofrad MRK, Schellekens H.
Int Rev Cell Mol Biol. (2016). 26:343-72. doi: 10.1016/bs.ircmb.2016.06.001.
|On the nuclear pore complex and its roles in nucleo-cytoskeletal coupling and mechanobiology
Soheilypour M, Peyro M, Jahed Z, Mofrad MRK.
Cellular and Molecular Bioengineering, DOI:10.1007/s12195-016-0443-x.
|Rapid Brownian Motion Primes Ultrafast Reconstruction of Intrinsically Disordered Phe-Gly-Repeats Inside the Nuclear Pore Complex
Moussavi-Baygi R, Mofrad MRK.
Scientific Reports, 6, Article number: 29991 (2016).
|Dynamic Regulation of α-Actinin’s Calponin Homology Domains on F-Actin
Shams H, Golji J, Garakani K, Mofrad MRK.
Biophysical Journal. 110, no. 6 (2016): 1444-1455.
(KEYWORDS: mechanotransduction, focal adhesions, α-actinin, actin)
|Protein thermostability engineering
Modarres HP, Mofrad MRK, Sanati-Nezhad A.
RSC Advances. 6, 115252-115270, 2016.(KEYWORDS: bioinformatics)
|Micro and nanotechnologies in heart valve tissue engineering
Hasan A, Saliba J, Pezeshgi-Modarres H, Bakhaty A, Nasajpour A, Mofrad MRK, Sanati-Nezhad A.
Biomaterials 103 (2016) 278-292
(KEYWORDS: Heart Valves)
|Enhanced intracellular delivery of small molecules and drugs via non-covalent ternary dispersions of single wall carbon nanotubes
Boyer PD, Shams H, Baker SL, Mofrad MRK, Islam MF, Dahl KN.
Journal of Materials Chemistry B. 4, 1324-1330, 2016
|Coupled Simulation of Heart Valves: Applications to Clinical Practice
Bakhaty A, Mofrad MRK.
Annals of Biomedical Engineering, 43(7)1626–1639, 2015.
(KEYWORDS: cardiovascular biomechanics, aortic valve)
|Nucleoporin’s Like Charge Regions Are Major Regulators of FG Coverage and Dynamics Inside the Nuclear Pore Complex
Peyro M, Soheilypour M, Ghavami A, Mofrad MRK.
PLoS ONE 10(12): e0143745. doi:10.1371/journal.pone.0143745, 2015
|Evolutionarily Conserved Sequence Features Regulate the Formation of FG Network at the Center of the Nuclear Pore Complex
Peyro M, Soheilypour M, Lee B, Mofrad MRK.
Scientific Reports. Vol:5, Article Number:15795, 2015
|Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
Asgari E, Mofrad MRK.
PLoS ONE 10(11): e0141287. doi:10.1371/journal.pone.0141287.
|A disulfide bond is required for the transmission of forces through SUN-KASH complexes
Jahed Z, Shams H, Mofrad MRK.
Biophysical Journal. 2015 Aug 4;109(3):501-509.
|Cooperation within Von Willebrand Factors Enhances Adsorption Mechanism
Heidari M, Mehrbod M, Ejtehadi MR, Mofrad MRK. Journal of The Royal Society Interface, 12(109), 20150334.
|Mechanisms of integrin and filamin binding and their interplay with talin during early focal adhesion formation
Truong T*, Shams H*, Mofrad MRK.
Integrative Biology. 2015
|Torsional Behavior of Axonal Microtubule Bundles
Lazarus C, Soheilypour M, Mofrad MRK.
Biophysical Journal, Volume 109, Issue 2, p231–239, 21 July 2015
|Adhesion characteristics of Staphylococcus aureus bacterial cells on funnel-shaped palladium–cobalt alloy nanostructures cells
Gu J, Chen PZ, Seo BB, Jardin JM, Verma MS, Jahed Z, Mofrad MRK, Gu FX, Tsui TY,
Journal of Experimental Nanoscience. 2015.
|Trapping polystyrene and latex nanospheres inside hollow nanostructures using Staphylococcus aureus cells
Seo BB, Chen PZ, Jahed Z, Mofrad MRK, Gu FX, Tsui TY.
Journal of Experimental Nanoscience. (ahead-of-print), 1-11, 2015.
|Buckling Behavior of Individual and Bundled Microtubules
Soheilypour M, Peyro M, Peter S, Mofrad MRK.
Biophysical Journal, Volume 108, Issue 7, p. 1718–1726, 7 April 2015.
|Directional migration and differentiation of neural stem cells within three-dimensional microenvironments
Shamloo A, Heibatollahi M, Mofrad MRK.
Integrative Biology, 2015, 7(3), 335-344.
|The interaction of RNA helicase DDX3 with HIV-1 Rev-CRM1-RanGTP complex during the HIV replication cycle
Mahboobi SH, Javanpour AA, Mofrad MRK.
PloS one, 2015, 10(2), e0112969.
|The Talin Dimer Structure Orientation Is Mechanically Regulated
Golji J, Mofrad MRK.
Biophysical Journal, 2014, 107(8), 1802-1809.
|Cell responses to metallic nanostructure arrays with complex geometries
Jahed Z, Molladavoodi S, Seo BB, Gorbet M, Tsui TY#, Mofrad MRK#.
Biomaterials, 2014 Nov;35(34):9363-71.
|The α-subunit regulates stability of the metal ion at the ligand-associated metal-ion binding site
in β3 integrins
Rui X*, Mehrbod M*, Van Agthoven JF, Xiong JP, Mofrad MRK#, Arnaout MA#.
Journal of Biological Chemistry, 2014 Jun 28. [Epub ahead of print].
|Atomic basis for the species-specific inhibition of αV integrins by mAb 17E6 is revealed by the crystal structure of αVβ3 ectodomain-17E6 Fab complex
Mahalingam B, Van Agthoven JF, Xiong JP, Alonso JL, Adair BD, Rui X, Anand S, Mehrbod M, Mofrad MRK, Burger D, Goodman SL, Arnaout MA.
Journal of Biological Chemistry, 2014 Apr 1. [Epub ahead of print]
|Mechanotransduction Pathways Linking the Extracellular Matrix to the Nucleus
Jahed Z, Shams H, Mehrbod M, Mofrad MRK.
International Review of Cell and Molecular Biology, 2014;310:171-220.
|The Interaction of CRM1 and the Nuclear Pore Protein Tpr
Zhao C*, Mahboobi SH*, Moussavi-Baygi R, Mofrad MRK.
PLoS One, April 2014, Volume 9 | Issue 4 | e93709.
|Responses of Staphylococcus Aureus Bacterial Cells to Nanocrystalline Nickel Nanostructures
Jahed Z, Lin P, Seo BB, Verma MS, Gu FX, Tsui TY#, Mofrad MRK#. Biomaterials. 2014 Feb 24. doi: 10.1016/j.biomaterials.2014.01.080.
|Filamin: A structural and functional biomolecule with important roles in cell biology, signaling and mechanics
Modarres HP, Mofrad MRK. Molecular and Cellular Biomechanics, 2014, 11, 039-065.
|Actin Reorganization through Dynamic Interactions with Single-Wall Carbon Nanotubes
Shams H, Holt B, Mahboobi SH, Jahed Z, Islam M, Dahl KN, Mofrad MRK. ACS nano, 2013, 8(1), 188-197.
|Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import.
Azimi M, Mofrad MRK. PLoS One. 2013 Nov 25;8(11):e81741. doi: 10.1371/journal.pone.0081741.
|Quantifying intracellular protein binding thermodynamics during mechanotransduction based on FRET spectroscopy.
Abdul Rahim NA, Pelet S, Mofrad MRK, So PTC, Kamm RD. Methods. 2013 Oct 31. doi:pii: S1046-2023(13)00403-9. 10.1016/j.ymeth.2013.10.007.
|Emerging trends in heart valve engineering: part I. Solutions for future.
Kheradvar A, Groves EM, Dasi LP, Alavi SH, Tranquillo R, Grande-Allen KJ, Simmons CA, Griffith B, Falahatpisheh A, Goergen CJ, Mofrad MR, Baaijens F, Little SH, Canic S. Annals of Biomedical Engineering, 2015 Apr;43(4):833-43.
|Emerging Trends in Heart Valve Engineering: Part II. Novel and Standard Technologies for Aortic Valve Replacement.
Kheradvar A, Groves EM, Goergen CJ, Alavi SH, Tranquillo R, Simmons CA, Dasi LP, Grande-Allen KJ, Mofrad MR, Falahatpisheh A, Griffith B, Baaijens F, Little SH, Canic S. Annals of Biomedical Engineering, 2015 Apr;43(4):844-57.
|Emerging Trends in Heart Valve Engineering: Part III. Novel Technologies for Mitral Valve Repair and Replacement.
Kheradvar A, Groves EM, Simmons CA, Griffith B, Alavi SH, Tranquillo R, Dasi LP, Falahatpisheh A, Grande-Allen KJ, Goergen CJ, Mofrad MR, Baaijens F, Canic S, Little SH. Annals of Biomedical Engineering, 2015 Apr;43(4):858-70.
|Emerging Trends in Heart Valve Engineering: Part IV. Computational Modeling and Experimental Studies
Kheradvar A, Groves EM, Falahatpisheh A, Mofrad MRK, Alavi SH, Tranquillo R, Dasi LP, Simmons CA, Grande-Allen KJ, Goergen CJ, Baaijens F, Little SH, Canic S, Griffith B. Annals of Biomedical Engineering, 2015.
|Biomechanical properties of native and tissue engineered heart valve constructs.
Hasan A, Ragaert K, Swieszkowski W, Selimović S, Paul A, Camci-Unal G, Mofrad MRK, Khademhosseini A. Journal of Biomechanics. 2013 Oct 21. doi:pii: S0021-9290(13)00446-6. 10.1016/j.jbiomech.2013.09.023.
|On the Activation of Integrin αIIbβ3: Outside-In and Inside-Out Pathways.
Mehrbod M, Trisno S, Mofrad MRK. Biophysical Journal, 2013 Sept; 105(6).
|An Agent Based Model of Integrin Clustering: Exploring the Role of Ligand Clustering, Integrin Homo-Oligomerization, Integrin-Ligand Affinity, Membrane Crowdedness and Ligand Mobility.
Jamali Y, Jamali T, Mofrad MRK. Journal of Computational Physics. 2013; 244:264–278
|The Interaction of Vinculin with Actin.
Golji J, Mofrad MRK. PLoS Computational Biology, 2013 Apr;9(4):e1002995.
|Localized Lipid Packing of Transmembrane Domains Impedes Integrin Clustering.
Mehrbod M, Mofrad MRK. PLoS Computational Biology, 2013 Mar; 9(3): e1002948.
|Molecular Trajectory of Alpha-Actinin Activation.
Shams H, Golji J, Mofrad MRK. Biophysical Journal, November 2012, 103(10):2050-2059.
|Altered Cell Mechanics from the Inside: Dispersed Single Wall Carbon Nanotubes Integrate with and Restructure Actin.
Holt BD, Shams H, Horst TA, Basu S, Rape DA, Wang Y-L, Rohde GK, Mofrad MRK, Islam MF and Dahl KN. Journal of Functional Biomaterials, 2012 May; 3:398-417.
|Phosphorylation Primes Vinculin for Activation.
Golji J, Wendorff TJ, Mofrad MRK. Biophysical Journal, 2012 May; 102:2022-2030.
|Passive control of cell locomotion using micropatterns: the effect of micropattern geometry on the migratory behavior of adherent cells. Lab Chip.
Yoon SH, Kim YK, Han ED, Seo YH, Kim BH, Mofrad MRK. 2012 Jul 7;12(13):2391-402[highlighted in Lab Chip Research Highlights, 2012 Jul 7;12(13):2391-4023.]
|Computational Modeling of Axonal Microtubule Bundles under Tension.
Peter SJ, Mofrad MRK. Biophysical Journal, 2012 February; 102(3):749-757.
|Biophysical Coarse-Grained Modeling Provides Insights into Transport through the Nuclear Pore Complex.
Moussavi-Baygi RM, Jamali Y, Karimi R, Mofrad MRK. Biophysical Journal. 2011; 100(6):1410-1419[cover].
|Brownian Dynamics Simulation of Nucleocytoplasmic Transport: A Coarse-Grained Model for the Functional State of the Nuclear Pore Complex.
Moussavi-Baygi RM, Jamali Y, Karimi R, Mofrad MRK. PLoS Computational Biology. 2011 June; 7(6): e1002049.
|Nuclear Pore Complex: Biochemistry and Biophysics of Nucleocytoplasmic Transport in Health and Disease.
Jamali T, Jamali Y, Mehrbod M, Mofrad MRK. International Review of Cell and Molecular Biology, 2011; 287: 233-286.
|On the Significance of Microtubule Flexural Behavior in Cytoskeletal Mechanics.
Mehrbod M, Mofrad MRK. PLoS One. 2011; 6(10):e25627.
|Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion.
Azimi M, Jamali Y, Mofrad MRK. PLoS One. 2011;6(9):e25306.
|A biological breadboard platform for cell adhesion and detachment studies.
Yoon SH, Chang J, Lin L, Mofrad MRK. Lab on a Chip, 2011; 11(20):3555-62[highlighted in Lab Chip Research Highlights, December 21, 2011, 11(24):4141-3].
|Cell adhesion and detachment on gold surfaces modified with a thiol-functionalized RGD peptide.
Yoon SH, Mofrad MRK. Biomaterials, 2011; 32: 7286-7296.
|Viscoelastic characterization of the retracting cytoskeleton using subcellular detachment.
Yoon SH, Lee C, Mofrad MRK. Applied Physics Letters, 2011; 98, 133701-3.
|MEMS Based Dynamic Cell-to-Cell Culture Platforms Using Electrochemical Surface Modifications.
Chang J, Yoon SH, Mofrad MRK, Lin L. Journal of Micromechanics and Microengineering, 2011; 21, 054028.
|Vinculin Activation Is Necessary for Complete Talin Binding.
Golji J, Lam J, Mofrad MRK. Biophysical Journal 2011 Jan 19;100(2):332-40.
|A Sub-Cellular Viscoelastic Model for Cell Population Mechanics.
Jamali Y, Azimi, M, Mofrad MRK. PLoS One. 2010; 5(8):e12097
|Analysis of Circular PDMS Microballoons with Hyper-Deflection for MEMS Design.
Yoon S, Reyes-Ortiz V, Kim K, Seo YH, Mofrad MRK. J. MEMS. 2010. 19(4):854-864.
|On the multiscale modeling of heart valve biomechanics in health and disease.
Weinberg EJ, Shahmirzadi D, Mofrad MR. Biomech Model Mechanobiol. 2010 Aug;9(4):373-87.
|Liver-assist device with a microfluidics-based vascular bed in an animal model.
Hsu WM, Carraro A, Kulig KM, Miller ML, Kaazempur-Mofrad M, Weinberg E, Entabi F, Albadawi H, Watkins MT, Borenstein JT, Vacanti JP, Neville C. Ann Surg. 2010 Aug;252(2):351-7.
|Pulmonary tissue engineering using dual-compartment polymer scaffolds with integrated vascular tree.
Fritsche CS, Simsch O, Weinberg EJ, Orrick B, Stamm C, Kaazempur-Mofrad MR, Borenstein JT, Hetzer R, Vacanti JP. Int J Artif Organs. 2009 Oct;32(10):701-10.
|A Molecular Dynamics Investigation of Vinculin Activation.
Golji J, Mofrad MRK. Biophysical Journal. 2010 August; 99(3): 1073–1081.
|Averaged Implicit Hydrodynamic Model of Semiflexible Filaments.
Chandran PL, Mofrad MRK. Physical Rev. E 2010 Mar;81(3 Pt 1):031920.
|Molecular Biomechanics: The Molecular Basis of How Forces Regulate Cellular Function.
Bao G, Kamm RD, Thomas W, Hwang W, Fletcher DA, Grodzinsky A, Zhu C, Mofrad MRK. Cellular & Molecular Bioeng. 2010; 3(2):91-105.
|Hemodynamic environments from opposing sides of human aortic valve leaflets evoke distinct endothelial phenotypes in vitro.
Weinberg EJ, Mack PJ, Schoen FJ, Garcia-Cardena G, Kaazempur Mofrad MR. Cardiovasc Eng. 2010 Mar;10(1):5-11.
|Carotid atheroma rupture observed in vivo and FSI-predicted stress distribution based on pre-rupture imaging.
Leach JR, Rayz VL, Soares B, Wintermark M, Mofrad MR, Saloner D. Ann Biomed Eng. 2010 Aug;38(8):2748-65.
|An efficient two-stage approach for image-based FSI analysis of atherosclerotic arteries.
Leach JR, Rayz VL, Mofrad MR, Saloner D. Biomech Model Mechanobiol. 2010 Apr;9(2):213-23.
Mechanotransduction: a major regulator of homeostasis and development.
Kolahi KS, Mofrad MR. Wiley Interdiscip Rev Syst Biol Med. 2010 Nov-Dec;2(6):625-39.
Phosphorylation Facilitates the Integrin Binding of Filamin Under Force.
Chen HS, Kolahi KS, Mofrad MRK. Biophysical Journal 2009 Dec 16;97(12):3095-104.
Rheology of the cytoskeleton.
Mofrad MRK. Ann. Rev. of Fluid Mechanics. 2009; 41:433-453.
Band-like stress fiber propagation in a continuum and implications for myosin contractile stresses.
Chandran PL, Wolf CB, Mofrad MRK. 2009; 2(1):13-27.
A computational model of aging and calcification in the aortic heart valve.
Weinberg EJ, Schoen FJ, Mofrad MR. PLoS One. 2009 Jun 18;4(6):e5960.
Molecular mechanics of the alpha-actinin rod domain: bending, torsional, and extensional behavior.
Golji J, Collins R, Mofrad MR. PLoS Comput Biol. 2009 May;5(5):e1000389.
Quantitative analysis of epithelial morphogenesis in Drosophila oogenesis: New insights based on morphometric analysis and mechanical modeling.
Kolahi KS, White PF, Shreter DM, Classen AK, Bilder D, Mofrad MR. Dev Biol. 2009 Jul 15;331(2):129-39.
Rods-on-string idealization captures semiflexible filament dynamics.
Chandran PL, Mofrad MR. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jan;79(1 Pt 1):011906.
Estimation of nonlinear mechanical properties of vascular tissues via elastography.
Karimi R, Zhu T, Bouma BE, Mofrad MR. Cardiovasc Eng. 2008 Dec;8(4):191-202.
A multiscale computational comparison of the bicuspid and tricuspid aortic valves in relation to calcific aortic stenosis.
Weinberg EJ, Kaazempur Mofrad MR. J Biomech. 2008 Dec 5;41(16):3482-7.
Concept and computational design for a bioartificial nephron-on-a-chip.
Weinberg E, Kaazempur-Mofrad M, Borenstein J. Int. J. Artificial Organs. 2008 Jun;31(6):508-14.
In vitro analysis of a hepatic device with intrinsic microvascular-based channels.
Carraro A, Hsu WM, Kulig KM, Cheung WS, Miller ML, Weinberg EJ, Swart EF, Kaazempur-Mofrad M, Borenstein JT, Vacanti JP, Neville C. Biomed Microdevices. 2008 Dec;10(6):795-805.
On the octagonal structure of the nuclear pore complex: insights from coarse-grained models.
Wolf C, Mofrad MR. Biophys J. 2008 Aug;95(4):2073-85.[cover].
Molecular dynamics study of talin-vinculin binding.
Lee SE, Chunsrivirot S, Kamm RD, Mofrad MR. Biophys J. 2008 Aug;95(4):2027-36.
On the cytoskeleton and soft glassy rheology.
Mandadapu KK, Govindjee S, Mofrad MR. J Biomech. 2008;41(7):1467-78.
Transient, three-dimensional, multiscale simulations of the human aortic valve.
Weinberg EJ, Kaazempur Mofrad MR. Cardiovasc Eng. 2007 Dec;7(4):140-155.
Molecular mechanics of filamin’s rod domain.
Kolahi KS, Mofrad MR. Biophys J. 2008 Feb 1;94(3):1075-83.
Microfluidic environment for high density hepatocyte culture.
Zhang MY, Lee PJ, Hung PJ, Johnson T, Lee LP, Mofrad MR. Biomedical Microdevices. 2008 Feb;10(1):117-21.
Microfabrication of three-dimensional engineered scaffolds.
Borenstein JT, Weinberg EJ, Orrick BK, Sundback C, Kaazempur-Mofrad MR, Vacanti JP. Tissue Eng. 200LE Aug;13(8):1837-44.
Force-induced activation of Talin and its possible role in focal adhesion mechanotransduction.
Lee SE, Kamm RD, Mofrad MRK. Journal of Biomechanics, 2007;40(9):2096-106.
Mechanics and Deformation of the Nucleus in Micropipette Aspiration Experiment.
Vaziri A, Kaazempur Mofrad MR. Journal of Biomechanics, 2007;40(9):2053-62.
A Computational Study on Power-Law Rheology of Soft Glassy Materials with Application to Cell Mechanics
Vaziri A, Xue Z, Kamm RD, Kaazempur Mofrad MR. Computer Methods in Applied Mechanics and Engineering, 196: 2965-2971, 2007.
A finite shell element for heart mitral valve leaflet mechanics, with large deformations and 3D constitutive material model
Weinberg EJ, Kaazempur Mofrad MR. Journal of Biomechanics, 2007;40(3):705-11.
A Combined FEM/Genetic Algorithm for Vascular Soft Tissue Elasticity Estimation
Khalil AS, Bouma BE, Kaazempur Mofrad MR. Cardiovasc Eng. 2006 Sep;6(3):93-102.
Deformation of the cell Nucleus under Indentation: Mechanics and Mechanisms
Vaziri A, Lee H, Kaazempur Mofrad MR. Journal of Material Research, 2006 Aug; 21(8):1-10.
A Coarse-grained Model for Force-induced Protein Deformation and Kinetics
Karcher H, Lee SE, Kaazempur-Mofrad MR, Kamm RD. Biophysical Journal, 2006 Apr 15;90(8):2686-97.
A Large-Strain Finite Element Formulation for Biological Tissues with Application to Mitral Valve Leaflet Tissue Mechanics
Weinberg EJ, Kaazempur-Mofrad MR. Journal of Biomechanics, 2006;39(8):1557-61..
Non-rigid registration for fusion of carotid vascular ultrasound and MRI volumetric datasets
Chan RC, Sokka S, Hinton D, Houser S, Manzke R, Hanekamp A, Reddy VY, Kaazempur-Mofrad MR, and Rasche V. Proc. SPIE Vol. 6144 61442E1-8.
Tissue Elasticity Estimation with Optical Coherence Elastography: Toward Mechanical Characterization of In Vivo Soft Tissue
Khalil AS, Chan RC, Chau AH, Bouma BE, Kaazempur-Mofrad MR. Annals of Biomedical Engineering, 2005 Nov;33(11):1631-9..
Mass Transport and Fluid Flow in Stenotic Arteries: Axisymmetric and Asymmetric Models
Kaazempur-Mofrad MR, Wada S, Myers JG, and Ethier CR. International Journal of Heat and Mass Transfer, 2005; 48:4510-4517.
Exploring the molecular basis for mechanosensation, signal transduction, and cytoskeletal remodeling
Kaazempur-Mofrad MR, Abdul-Rahim NA, Karcher H, Mack PJ, Yap B, Kamm RD. Acta BioMaterial, 2005 May;1(3):281-93.
Endothelialized Microvasculature Based on a Biodegradable Elastomer
Fidkowski C, Kaazempur-Mofrad MR, Borenstein JT, Vacanti JP, Langer R, Wang Y. Tissue Engineering, Engineering, 2005 Jan-Feb;11(1-2):302-9.
On the Constitutive Models for Heart Valve Leaflet Mechanics.
Weinberg EJ, Kaazempur-Mofrad MR. Cardiovascular Engineering, 2005; 5(1): 37-43.
Endothelialized networks with a vascular geometry in microfabricated poly(dimethyl siloxane)
Shin M, Matsuda K, Ishii O, Terai H, Kaazempur-Mofrad M, Borenstein J, Detmar M, Vacanti JP. Biomedical Microdevices, 2004 Dec;6(4):269-78.
How Flexible is α-Actinin’s Rod Domain?
Zaman MH, Kaazempur-Mofrad MR. Mechanics & Chemistry of Biosystems. 2004 Dec;1(4):291-302.
Force-induced Conformational Changes in Focal Adhesion Targeting Region of Focal Adhesion Kinase: A Steered Molecular Dynamics Study
Kaazempur-Mofrad MR, Golji J, Abdul Rahim NA, Kamm RD. Mechanics & Chemistry of Biosystems. 2004 Dec;1(4):253-65.
Distinct Endothelial Phenotypes Evoked by Arterial Waveforms Derived from Atherosclerosis-Susceptible and Resistant Regions of Human Vasculature
Dai G, Kaazempur-Mofrad MR, Natarajan S, Zhang Y, Vaughn S, Blackman BR, Kamm RD, García-Cardena G, Gimbrone MA, Jr. PNAS, 2004 Oct 12;101(41):14871-6.
On the Molecular Basis for Mechanotransduction
Kamm RD and Kaazempur-Mofrad MR. Mechanics & Chemistry of Biosystems, 2004 Sep;1(3):201-209.
OCT-based Arterial Elastography: Robust Estimation exploiting Tissue Biomechanics
Chan RC, Chau AH, Karl WC, Nadkarni S, Khalil AS, Shishkov M, Tearney GJ, Kaazempur-Mofrad MR, Bouma BE. Optics Express, 2004 Sep 20;12(19):4558-72.
Mechanical Analysis of Atherosclerotic Plaques based on Optical Coherence Tomography
Chau AH, Chan RC, Shishkov M, MacNeill, B, Iftima N, Tearney GJ, Kamm RD, Bouma B, Kaazempur-Mofrad MR. Annals of Biomedical Engineering, 2004 Nov;32(11):1494-503.
King KR, Wang CJ, Kaazempur-Mofrad MR, Vacanti JP, Borenstein JT. Advanced Materials, 2004; 16(22):2007-2012.
Force-induced Focal Adhesion Translocation: Effects of Force Amplitude and Frequency
Mack PJ, Kaazempur-Mofrad MR, Karcher H, Lee RT, Kamm RD. American Journal of Physiology – Cell Physiology, 2004 Oct;287(4):C954-62 .
Hemodynamics and wall mechanics in human carotid bifurcation and its consequences for atherogenesis: investigation of inter-individual variation.
Younis HF, Kaazempur-Mofrad MR, Chan RC, Isasi AG, Hinton DP, Chau AH, Kim LA, Kamm RD. Biomech Model Mechanobiol. 2004 Sep;3(1):17-32.
Characterization of the Atherosclerotic Carotid Bifurcation using MRI, Finite Element Modeling and Histology
Kaazempur-Mofrad MR, Younis HF, Isasi AG, Chan RC, Hinton DP, Sukhova G, LaMuraglia GM, Lee RT and Kamm RD. Annals of Biomedical Engineering, 2004 July;32(7):932-46.
A Three-Dimensional Viscoelastic Model for Cell Deformation with Experimental Validation
Karcher H, Lammerding J, Huang H, Lee RT, Kamm RD, and Kaazempur-Mofrad MR. Biophysical Journal, 2003 Nov;85(5):3336-49.
Cyclic Strain in Human Carotid Bifurcation and its Potential Correlation to Atherogenesis: Idealized and Anatomically-Realistic Models
Kaazempur-Mofrad MR, Younis HF, Patel S, Isasi AG, Chung C, Chan RC, Hinton DP, Lee RT, Kamm RD. Journal of Engineering Mathematics, 2003; 47(3-4):299-314.
Role of simulation in understanding biological systems.
Kaazempur-Mofrad MR, Bathe M, Karcher H, Younis HF, Seong HC, Shim EB, Chan RC, Hinton DP, Powers MJ, Griffith LG, and Kamm RD. Computer & Structures, 2003; 81(8):715-726.
Computational Analysis of the Effects of Exercise on Hemodynamics in the Carotid Bifuration
Younis HF, Kaazempur-Mofrad MR, Chan R, Chung C, Kamm RD. Annals of Biomedical Engineering, 2003 Sep;31(8):995-1006.
A Dynamic Rotational Seeding and Cell Culture System for Vascular Tube Formation.
Nasseri BA, Pomeranteseva I, Kaazempur-Mofrad MR, Perry T, Ochoa E, Thomson CA, Oesterle SN, Vacanti JP. Tissue Engineering, 2003 Apr;9(2):291-9.
On the Sensitivity of Wall Stresses in Diseased Arteries to Variable Material Properties
Williamson SD, Lam Y, Younis HF, Huang H, Patel S, Kaazempur-Mofrad MR, Kamm RD. Journal of Biomechanical Engineering, 2003 Feb;125(1):147-55.
A Characteristic/Finite Element Algorithm for Time-Dependent 3-D Advection-Dominated Transport using Unstructured Grids
Kaazempur-Mofrad MR, Minev PD and Ethier CR. Computer Methods in Applied Mechanics and Engineering, 2003; 192(11-12):1281-1298.
An Efficient Characteristic Galerkin Scheme for the Advection Equation in 3-D.
Kaazempur-Mofrad MR and Ethier CR. Computer Methods in Applied Mechanics and Engineering, 2002; 191(46):5345-5363.
Microfabrication Technology for Vascularized Tissue Engineering.
Borenstein JT, Terai H, King KR, Weinberg EJ, Kaazempur-Mofrad MR and Vacanti JP, Biomedical Microdevices, 2002 July; 4(3):167-175.
A Microfabricated Array Bioreactor for Perfused 3D Liver Culture
Powers, MJ, Domansky K, Kaazempur-Mofrad MR, Kalezi A, Capitano A, Upadhyaya A, Kurzawski P, Wack KE, Stolz DB, Kamm RD, Griffith LG. Biotechnology & Bioengineering, 2002 May 5;78(3):257-69.
Mass Transport in an Anatomically Realistic Human Right Coronary Artery
Kaazempur-Mofrad MR and Ethier CR. Annals of Biomedical Engineering, 2001 Feb;29(2):121-7.
BOOK CHAPTERS (selected)
|Tissue Engineering of Microvascular Networks.
Borenstein JT, Weinberg EJ, Kaazempur-Mofrad MR, and Vacanti JP. in Encyclopedia of Biomaterials and Biomedical Engineering (Eds.: Wnek GE and Bowlin GL), Marcel Dekker Publication, 2004.
|Tissue Engineering: Multi-Scaled Representation of Tissue Architecture and Function.
Kaazempur-Mofrad MR, Weinberg EJ, Borenstein JT, Vacanti JP. in Complex Systems Science in Biomedicine (Eds.: Deisboeck TS, Kresh JY), Springer, NY, 2006.
|Microvascular Engineering: Design, Modeling, and Microfabrication.
Borenstein JT, Weinberg EJ, Vacanti JP, Mofrad MRK. in Micro- and Nanoengineering of the Cell Microenvironment, Technologies and Applications (Eds.: Khademhosseini et al.). Engineering in Medicine & Biology. 2008
|Technological Approaches to Renal Replacement Therapies.
Reyes-Ortiz, Mofrad MRK, Weinberg EJ, Vacanti JP, Borenstein JT. in Micro- and Nanoengineering of the Cell Microenvironment, Technologies and Applications (Eds.: Khademhosseini et al.). Engineering in Medicine & Biology. 2008
|Computational modeling of aortic heart valve mechanics across multiple scales.
Croft LR and Mofrad MRK. in Computational Cardiovascular Mechanics (Eds.: Guccione et al.). Springer, 2009
|Computational models of vascular mechanics.
Leach JR, Mofrad MRK, Saloner D. in Computational Modeling in Biomechanics (Eds.: De et al.). Springer, 2010
|Computational modeling of aortic heart valves.
Croft LR and Mofrad MRK. in Computational Modeling in Biomechanics (Eds.: De et al.). Springer, 2010
|Cytoskeletal Mechanics and Cellular Mechanotransduction: A Molecular Perspective.
Hatami H and Mofrad MRK. in Cellular and Biomolecular Mechanics and Mechanobiology (Ed.: Gefen A.). Springer, 2011
|Cytoskeletal Mechanics and Rheology.
Hatami H and Mofrad MRK. in Advances in Cell Mechanics (Eds.: Li S and Bohua S). Springer, 2011.
|Mechanobiological Approaches for the Control of Cell Motility.
Yoon SH, Mofrad MRK. in Microfluidic Cell Culture Systems (Eds.: Bettinger et al.), Elsevier, 2012.
|Rheology and Mechanics of the Cell Cytoskeleton: A Complex Biopolymer Network Structure.
Hatami H and Mofrad MRK. Complex Fluids in Biological Systems (Ed.: Spagnolie SE) Springer Biological and Medical Physics / Biological Engineering, In press.
ULAB: Intro to Computational Biology
This two-semester course in computational biology aims to give undergraduates an opportunity to gain fundamental research skills, explore relevant background knowledge, and conduct their own research projects in groups. The goal is to help students feel confident and prepared to seek out on-campus opportunities in the exciting field of computational biology.
BioEc112/c215 / MEc115/MEc216: Molecular Biomechanics and Mechanobiology of the Cell
This course develops and applies scaling laws and the methods of statistical and continuum mechanics to biomechanical phenomena over a range of length scales, from molecular to cellular levels. It is intended for senior undergraduate students and graduate students who have been exposed to differential equations, mechanics and certain aspects of modern biology.
ME 211: The Cell as a Machine
This course offers a modular and systems mechanobiology (or “machine”) perspective of the cell. Two vitally important components of the cell machinery will be studied in depth: (1) the integrin-mediated focal adhesions system that enables the cell to adhere to, and communicate mechano-chemical signals with, the extracellular environment, and (2) the nuclear pore complex, a multi-protein gateway for traffic in and out of the nucleus that regulates gene expression and affects protein synthesis. This course is intended for graduate students in Mechanical Engineering. No prior knowledge in Biology is assumed.
ME 120: Computational Biomechanics Across Multiple Scales
This course applies the methods of computational modeling and continuum mechanics to biomedical phenomena spanning various length scales ranging from molecular to cellular to tissue and organ levels. The course is intended for upper level undergraduate students who have been exposed to undergraduate continuum mechanics (statics and strength of materials).
BioE102: Biomechanics: Analysis and Design
This (junior level undergraduate) course develops and applies the methods of continuum mechanics to biomechanical phenomena over a range of length scales, from cell to tissue and organ levels. It is intended for junior undergraduate students in Bioengineering who have been exposed to undergraduate physics, linear algebra and differential equations. The course will equip the students with a deep understanding of principles of biomechanics. The intuitions gained in this course will guide the design of biomedical devices and help the understanding of biological/medical phenomena in health and disease.
BioE104: Biological Transport Phenomena
This course develops and applies scaling laws and the methods of continuum mechanics to biological transport phenomena over a range of length and time scales. It is intended for undergraduate students who have taken a course in differential equations, and an introductory course in physics. Preliminary understanding of biology and physiology is useful but not assumed. Example application areas include biomolecular transport in biological tissues, living organs, and in biomedical microdevices.