Biological machines / Microfluidics

Angiogenesis / Vasculogenesis

Cancer

Simulation and modeling

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Biological machines / Microfluidics

Recent work has demonstrated the pervasive role of mechanics in biology. The most prominent examples include matrix stiffness influencing stem cell lineage and tumor progression, axonal tension regulating presynaptic vesicle clustering, and stiffness gradients guiding migrating cells. Mechanotransduction, the mechanism underlying these cellular responses to mechanical stimuli, has been studied in detail for the past decade and offers a new paradigm for directing the form and function of integrated cellular systems.

Over the past 5 years, we developed various microfluidic platforms for mimicking the three dimensional microenvironment and investigating the role of mechanical stimuli, such as interstitial flow, cyclic strain, and ECM stiffness gradients, on cellular processes including cell migration, angiogenesis, and differentiation.

Recently, we have drawn upon our understanding of mechanobiology to direct the function of multicellular systems. For example, we extended our angiogenesis model to build functional vascular networks in vitro, and we directed stem cell differentiation into cardiomyocytes by applying cyclic strain. As we increase the complexity of synthetic modules toward building biological machines, mechanics will play a more significant role, particularly in the engineering of neurons and myocytes for sensing and actuation. We will employ mechanical engineering as a tool to address this complexity while simultaneously extending our understanding of mechanotransduction.

Investigators: Sebastien Uzel, Vivek Sivathanu, Jordy Whisler.

References:

(1) Wan C.R., Chung S., Kamm R.D. (2011) "Differentiation of embryonic stem cells into cardiomyocytes in a compliant microfluidic system." Ann. Biomed. Eng. 1840-7.
(2) Jeon J.S., Chung S., Kamm R.D., Charest J.L. (2011). "Hot embossing for fabrication of a microfluidic 3D cell culture platform." Biomed. Microdevices. 13(2):325-33.
(3) Kothapalli, C.R., van Veen E., de Valence S., Chung S., Zervantonakis I.K., Gertler F.B., Kamm R.D. (2011). "A high-throughput microfluidic assay to study neurite response to growth factor gradients." Lab Chip. 11(3):497-507.
(4) Vickerman, V., Blundo, J., Chung, S., and Kamm, R. (2008) "Design, fabrication and implementation of a novel multi-parameter control microfluidic platform for three-dimensional cell culture and real-time imaging." Lap Chip 8(9): 1468-1477 [PDF].


Angiogenesis / Vasculogenesis

Formation of new blood vessel from an existing branch, by a regulated process known as angiogenesis, governs vascular patterning in the body and determines the distribution of nutrients and oxygen supply. Angiogenesis has essential roles in development, reproduction and repair but also occurs in tumor formation and in a variety of diseases [1, 2]. Our lab studies the angiogenic process by computational modeling across multiple scales [3, 4] and by in vitro microfluidic experiments that mimics in vivo biophysical and biochemical microenvironment. We showed that angiogenic endothelial cells seeded in contact with collagen gel can be induced to form nascent angiogenic sprouts in microfluidic which later develop into a vascular network [5, 6, 7].

To understand the single cell decisions in angiogenesis at the signaling level, we model individual cell as a decision making entities and follow individual cell as they make decisions in angiogenic conditions [8]. In collaboration with the Lauffenburger lab at MIT, we attempt to elucidate how such single cell decision might be governed by an intracellular signaling by measuring the intracellular changes in signaling activities upon stimulating cells with potent factors that induce and suppress sprout formation [9].

Investigators: Michelle Chen, Jordy Whisler, Ran Li, Vivek Sivathanu, Anya Burkart

References:

(1) Wan C.R., Chung S., Kamm R.D. (2011) "Differentiation of embryonic stem cells into cardiomyocytes in a compliant microfluidic system." Ann. Biomed. Eng. 1840-7.
(2) Jeon J.S., Chung S., Kamm R.D., Charest J.L. (2011). "Hot embossing for fabrication of a microfluidic 3D cell culture platform." Biomed. Microdevices. 13(2):325-33.
(3) Kothapalli, C.R., van Veen E., de Valence S., Chung S., Zervantonakis I.K., Gertler F.B., Kamm R.D. (2011). "A high-throughput microfluidic assay to study neurite response to growth factor gradients." Lab Chip. 11(3):497-507.
(4) Vickerman, V., Blundo, J., Chung, S., and Kamm, R. (2008) "Design, fabrication and implementation of a novel multi-parameter control microfluidic platform for three-dimensional cell culture and real-time imaging." Lap Chip 8(9): 1468-1477 [PDF].


Cancer

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Tumor invasion has received a lot of attention as a critical step in metastatic disease for developing new cancer drugs. Current understanding of the role of biophysical and cellular microenvironment in tumor invasion is limited, because of the lack of appropriate in vitro and in vivo models. We have adapted our previous microfluidic platforms [1] for studying the role of the endothelium on tumor intravasation and the effects of interstitial flow on tumor cell migration, along with the development of new hard plastic devices for commercial transition.

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Recent results from the tumor-endothelial interaction assay demonstrated the capability to form a 3D endothelium on collagen type I matrices, in the presence of invading tumor cells in 3D (Figure 1). Upon stimulation with inflammatory cytokines we demonstrated an increase in diffusive permeability to fluorescent dextrans, in agreement with a measured increase in the number of intravasation events. These results demonstrate the utility of this assay for studying the role of the endothelial barrier function in tumor cell intravasation.

We developed a microfluidic system for investigating the role of interstitial flow in tumor cell migration (Figure 2). Tumor cells exposed to interstitial flow preferentially migrated along streamlines, and the relative percentage of cells migrating upstream and downstream is a function of chemokine receptor activity and cell density.
Caption
Interstitial flow stimulates downstream tumor cell migration through CCR7 autocrine signaling. However, flow also stimulates upstream cell migration through a competing, mechanically mediated pathway, as evidenced by significantly increased FAK activation in devices with flow. The relative strength of the autocrine and mechanical stimuli determines whether cells migrate upstream or downstream of the flow direction.

We applied the known commercially-viable manufacturing methods to a cyclic olefin copolymer (COC) material to fabricate a microfluidic device with controlled surface properties and improved potential to serve high-volume applications. Culture of cells in the new hard plastic device indicated no adverse effects of the COC material. Therefore, this transition of platform demonstrates a capability of using microfludic devices for 3D cell culture across the range from the scientific research to applications with broad clinical impact.

Investigators: Ran Li, Michelle Chen, Bai Jing, Alexandra Boussommier-Calleja, Anya Burkart.

References:

(1) Wan C.R., Chung S., Kamm R.D. (2011) "Differentiation of embryonic stem cells into cardiomyocytes in a compliant microfluidic system." Ann. Biomed. Eng. 1840-7.
(2) Jeon J.S., Chung S., Kamm R.D., Charest J.L. (2011). "Hot embossing for fabrication of a microfluidic 3D cell culture platform." Biomed. Microdevices. 13(2):325-33.
(3) Kothapalli, C.R., van Veen E., de Valence S., Chung S., Zervantonakis I.K., Gertler F.B., Kamm R.D. (2011). "A high-throughput microfluidic assay to study neurite response to growth factor gradients." Lab Chip. 11(3):497-507.
(4) Vickerman, V., Blundo, J., Chung, S., and Kamm, R. (2008) "Design, fabrication and implementation of a novel multi-parameter control microfluidic platform for three-dimensional cell culture and real-time imaging." Lap Chip 8(9): 1468-1477 [PDF].
(5) S. Chung, R. Sudo, P.J. Mack, C.R. Wan, V. Vickerman, R.D. Kamm. (2009). "Cell migration into scaffolds under co-culture conditions in a microfluidic platform". Lab Chip 9: 269-75.
(6) Polacheck W.J., Charest J.L., Kamm R.D. (2011) "Interstitial flow influences direction of tumor cell migration through competing mechanisms." Proc. Natl. Acad. Sci. 108 (27):11115-20.
(7) Jeon J.S., Chung S., Kamm R.D., Charest J.L. (2011). "Hot embossing for fabrication of a microfluidic 3D cell culture platform." Biomed. Microdevices. 13 (2):325-33.
(8) Zervantonakis I.K., Kothapalli C.R., Chung S., Sudo R., Kamm R.D. (2011) "Microfluidic devices for studying heterotypic cell-cell interactions and tissue specimen cultures under controlled microenvironments." Biomicrofluidics. 5 (1):13406.


Simulation and modeling

Shear deformation of a computationally generated actin network. In silico actin networks were implemented to investigate the role of actin crosslinking proteins and molecular motors on the viscoelastic properties of physiologically relevant actin networks (From ref. 2).
Computational models aide with data interpretation and experimental design, and simulations can prove insight into biological mechanisms in instances where experiments are not feasible. Modeling and simulation are integral parts to the Mechanobiology Lab, and we have developed models spanning length scales from single molecules to cell populations. Furthermore, these models are not independent; we employ course-graining techniques to allow models developed at small length scales to inform larger scale models. For example, the bulk properties of a material have been estimated by course-graining simulations of the constitutive atoms, providing a quantitative link between the chemical composition and mechanics of biomaterials (1).

Schematic of the angiogenesis process within a microfluidic device. A stochastic cell population model was used with experimental data gathered from the microfluidic platform to develop a closed-loop control system model for angiogenesis (From ref. 4).
The actin cytoskeleton contributes to the mechanical rigidity of cells, and dynamic reorganization of the intracellular actin network is required for key cellular events such as proliferation and migration. Mechanical force plays a crucial role in governing the dynamics of the actin cytoskeleton, and we have developed a computational model derived from Brownian dynamics simulations to study the mechanical properties of actin networks (2). The model well captures experimentally observed viscoelastic properties of actin and provides novel insight into the contribution of molecular motors and actin crosslinking proteins to the viscoelastic moduli of actin networks. Recently, we have extended this model to investigate the role of molecular motors in the generation of actin stress fibers, molecular complexes under tension that provide direct mechanical connections to the extracellular environment.

Simulation results from a hybrid continuum-discrete model show similar topology to neovascular networks generated in microfluidic devices (From ref. 3).
In the MIT Mechanobiology Lab, our experiments are tightly coupled with computational models for investigating biological phenomena. The highly controlled cell microenvironment enabled by our microfluidic platforms allows validation of our cell-level models, which in turn, provide insight into the mechanism underlying experimentally observed cellular behavior. We have integrated the microfluidic platform for studying angiogenesis with a hybrid discrete-continuum model to investigate the effects of matrix and growth factors on vascular network topology (3), and we have explored the link between matrix degrading enzymes and angiogenic sprout structure through control theory (4). Recently we have developed finite element models for investigating the role of interstitial flow in perturbing the tumor cell microenvironment (5), and we are adapting these models to aide in the development of patterned synthetic tissues and biological machines.

Investigators: Michael Mak.

References:

(1) Hammond, N.A., Kamm, R.D. (2008). "Elastic deformation and failure in protein filament bundles: Atomistic simulation and course-grained modeling." Biomaterials. 29: 3152-3160.
(2) Kim T., Hwang W., Lee H., Kamm R.D. (2009). "Computational analysis of viscoelastic properties of crosslinked actin networks." PLoS Comput. Biol. 5(7): e1000439.
(3) Das A., Lauffenburger D., Asada H., Kamm R.D. (2010). "A hybrid continuum-discrete modelling approach to predict and control angiogenesis: analysis of combinatorial growth factor and matrix effects on vessel-sprouting morphology." Philos. Transact A Math. Phys. Eng. Sci. 368(1921): 2937-60.
(4) Wood L.B., Das A., Kamm R.D., Asada H.H. (2009). "A stochastic broadcast feedback approach to regulating cell population morphology for microfluidic angiogenesis platforms." IEEE Trans. Biomed. Eng. 56(9): 2299-303.
(5) Polacheck W.J., Charest J.L., Kamm R.D. (2011) "Interstitial flow influences direction of tumor cell migration through competing mechanisms." Proc. Natl. Acad. Sci. 108(27): 11115-20.


Molecular mechanics

Amyloid fibers formed by the NM domains from S. cerevisiae sup35 grown in-situ from the glass cover slip.
Actin is one of the primary protein components of the cellular cytoskeleton. By forming networks of filaments spanning considerable intracellular distances, it provides the cell with structural support. However, actin also plays central roles in cell motility, cell division and force transmission through the cell. Consequently, the dynamics of actin are pivotal to the initiation of mechanotransduction or the physio-chemical response of cells to mechanical stimuli. The varied functions of actin also mean that it has tremendous implications in medicine and disease. The dynamics of actin filament polymerization and the protein-protein interactions responsible for the regulation of the actin network have been implicated in the tumorigenisis, the pathogenesis of cardiovascular disease, bacterial infections and viral entry. Moreover, actin filaments are of keen interest as a new platform for the delivery of gene therapies and as a model material system for energy storage technology.

To understand better the remarkable behavior of filamentous actin, we investigate the mechanochemistry and dynamics of actin regulatory proteins using optical microscopy and force spectroscopy. Specifically, we are interested in learning how these proteins use mechanical signals to regulate the polymerization/de-polymerization of actin filaments at the single-molecule level.

Amyloids are fibrous protein aggregates that are the basis for many diseases such as Alzheimer’s, type 2 diabetes, Parkinsons, Huntington’s, and scrapie, among many others. It has been found however, that there are many instances of functional amyloids that are used by biology for structural purposes, such as the E. coli curli proteins and spider silk; for sensing, such as HET-s from P. anserina; or as part of a system to adapt to new environments, such as the yeast prions, eg: [PSI] (sup35).

A great deal of work has been done to characterize amyloids biochemically, genetically, and biophysically, but there is still quite a lot that is still unknown regarding the mechanisms involved in assembly of amyloid fibers and the structure of the constituent proteins in the amyloid state. We are using applied force via optical tweezers as a probe to better understand the organization of the monomers within the amyloid fibril, and to gain insight into the structure of the monomers within the fiber. The overarching goal of this project is to determine if amyloids have similar mechanical properties, and thus potentially similar organizations of the proteins within amyloid fibers.

Investigators: Ted Feldman, Bill Hesse.

References:

(1) Jorge M. Ferrer, Hyungsuk Lee, Jiong Chen, Benjamin Pelz, Fumihiko Nakamura, Roger D. Kamm and Matthew J. Lang. (2008). "Measuring molecular rupture forces between single actin filaments and actin-binding proteins." PNAS 105(27): 9221-9226; doi:10.1073/pnas.0706124105
(2) Park, J., Kahng, B., Kamm, R.D., Hwang, W. (2006). "Atomistic simulation approach to a continuum description of self-assembled ß-sheet filaments." Biophys J 90: 2510-2524.
(3) Hwang, W., Zhang, S.,Kamm, R.D., and Karplus, M. (2004). "Kinetic control of dimer structure formation in amyloid fibrillogenesis." Proc Natl Acad Sci U S A 101: 12916-12921.
(4) Hwang, W., Marini, D.M.,Kamm, R.D., Zhang, S. (2003). "Supramolecular structure of helical ribbons self-assembled from a ß-sheet peptide." J Chem Phys. 118(1):389-397.
(5) Dong J, Castro CE, Boyce MC, Lang MJ, Lindquist S. (2010). "Optical Trapping with High Forces Reveals Unexpected Behaviors of Prion Fibrils." Nat. Struct. Mol. Biol. 17: 1422-30.
(6) Castro CE, Dong J, Boyce MC, Lindquist S, Lang MJ. (2011). "Physical properties of polymorphic yeast prion amyloid fibers." Biophys. J. 101(2): 439-48.