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Biolab - Dynamics of Complex Systems
University of Maryland - Wolfgang Losert PI
Biological Systems         Granular Materials


H. Jeong et al., Nature 411, 41 (2001)
Complex networks (such as the protein-protein interaction network of S. Cerevisiae shown in this figure from H. Jeong et al., Nature 411, 41 (2001)) uncover key features of complex living systems at different scales.

 
From molecules to cells to organisms: understanding health and disease with multidimensional single-cell methods
Julian Candia, Research Associate for the Cancer Technology Partnership

An amazing feature of living systems is that the behavior of organisms is based on the concerted action occurring on a wide range of scales from the molecular to the organismal level.  Molecular properties control the function of a cell, and cell ensembles function as organisms.  While some operations of a cell can be inferred from the study of its molecules one by one, the overall behavior of a cell is an emergent property that cannot be understood simply based on its components.  Similarly, organs and organisms are expected to have emergent properties based on the collective action of the constituent cells that can best be inferred by studies of cell ensembles.

Our goal is to apply and develop a variety of computational and analytical tools to uncover the underlying emergent behavior that links molecules and cells to human health. Our different approaches include dimensional reduction based on singular value decomposition, the perceptron and other machine learning algorithms, and network theory applied to high-throughput multidimensional single-cell data. We expect to gain a better understanding of different disease phenotypes based on single-cell molecular markers (which would improve clinical diagnosis) and link those findings to overexpressed genes within specific disease-related cell subpopulations (which would improve clinical treatment). 

With W. Losert, J. Banavar (University of Mayland), Lai Wei, Robert Nussenblatt (NIH)

Supported by Dept. of Physics, University of Maryland

Darkfield image of Dictyostelium discoideum cells migrating to a pipette releasing the chemoattractant cyclic AMP.  The cells release their own chemoattractant, attracting nearby cells and forming 'streams' as they collectively move to the pipette tip.
Collective Dynamics of Cells
Colin McCann, PhD student, Physics

Collective migration of gradient-sensing cells is a key feature of many biological processes, such as morphogenesis, immune response, and some cancer metastasis.  In the model system Dictyostelium discoideum the binding of chemoattractants leads to the production and secretion of additional chemoattractant and the relay of the signal to neighboring cells.  This guides cells to migrate collectively in a head-to-tail fashion.  We used signal relay defective mutants to elucidate which quantitative metrics of cell migration are most strongly affected by signal relay and collective motion. Neither signal relay nor collective motion impacted the speed of cell migration. Cells maintained an overall directional persistence for several minutes regardless of whether or not they were attracted to neighbors, moving while in contact with their neighbors, or following a fixed exogenous signal. In contrast, we found that signal relay not only increased the number of cells responding to a chemotactic signal, but also transmitted information about the location of the source accurately over large distances, independent of the signal strength.  We envision that signal relay plays a similar key role in the migration of a variety of chemotaxing mammalian cells that can relay chemoattractant signals.

Recent Publications:
C. McCann, P. Kriebel, C. Parent, and W. Losert, Journal of Cell Science, 2010.

(with C. Parent, P. Kriebel, National Cancer Institute)

Supported by the NCI-UMD Partnership for Cancer Technology



An initially round Dictyostelium discoideum cell breaks its circular symmetry by polarizing, or elongating. As it moves, it gradually speeds up, extending protrusions to the right and left of the direction it migrates in. The light gray background is formed from all the cell shapes extracted in all frames, while the alternating dark gray and colored boundaries show the cell shape every 3 minutes 20 seconds. In some outlines, a measure of local shape, curvature, is represented by boundary point color, which allows us to see how curvature varies along the boundary.

Cell Shape Dynamics:  From Waves to Migration
Meghan Driscoll, PhD student, Physics

Dictyostelium discoideum is a model system for the study of cellular migration. Though chemoattractants steer these cells over long time-scales, on short time-scales we found that they do not significantly straighten their paths.

Analysis of their dynamic cell shape shows that localized waves of protrusive motion emanate at the front in a zig-zag fashion, suggestive of the robust straightness. As the waves travel backwards, they get closer to the region in which the cell is in contact with the surface. These waves are present even when the majority of a cell is extended over the edge of a cliff.

(with J. Fourkas, Chemistry, University of Maryland and C. Parent, NCI)
 
Supported by NSF- Physics of Living Systems




Carboxyl coated polystyrene bead is moved 10 micron at 10 micron/s via an optical trap, held for 10s and then released. 
A.) Snapshots of the locally strained actin network. 
B.)  Bead position as a function of time (dashed line), and force on the bead (solid line). Key features of the localized intermediate network strains are break/slip events at large strains, formation of a localized wake-like structures, and persistent bead recoil.  Each image shown represents the average of two images taken 70ms apart and has been smoothed with a gaussian filter and adjusted for brightness and contrast.
Local Deformations of Biological Networks
Erin Rericha, Postdoc
Actin, a semi-flexible, double-stranded bio-polymer, is the primary component of the eukaryotic cell's cytoskeleton, the scaffolding that provides shape and mechanical strength to a cell.  The regulated polymerization and depolymerization of actin filaments produces forces that propel the cell forward.    Cells contain an array of proteins which bind actin, promoting crosslinking and bundling of filaments within the network, as well as molecular motors which translate filaments creating contractions of the polymer network.  These crosslinkers, bundlers, and motors lead to localized deformations and significant stresses within the actin filament network that have been shown to increase the apparent stiffness of the actin filament network by nearly 100-fold.

The behavior of the actin network in response to localized deformations that alter the immediate region around a network cage are not well understood, but biologically relevant since, it is on this scale that external mechanical loads often act on cells and that molecular motors deform the cytoskeleton.  Insight into the local rheological response of actin networks may prove useful in distinguishing disease phenotypes;  for instance,  metastatic cancer cells often exhibit defects in actin expression and spatial organization yielding, on average, a softer rheology than the parent cell type.    It is speculated that changes in actin rheology are a precursor to cancer invasiveness.

We impose localized strains in actin networks (with optical tweezers) and monitor the deformation and relaxation of the network.  Simultaneous, high speed confocal imaging of fluorescently labeled filaments shows that these intermediate scale deformations of the actin network lead to slowly relaxing void and wake structures.  When stress is released, the network returns to near its original equilibrium position for fast deformations.  Slower deformations are irreversible due to creep of the network.  In addition, we observe intermittent fracture events that suddenly relax imposed strains by altering void structure and network topology.

(with A. Pomerance (Ph.D student), D. Sisan, and J. Urbach (Physics, Georgetown University)


Patterns in Biological Systems
Joshua Parker, Ph.D. Student, Physics

 With recent advances in high-resolution microscopy techniques, biological researchers are finding the need to extract quantitative information out of images, videos, and point patterns to aid in distinguishing patterns that visually appear similar. Many standard statistical techniques, such as the pair-wise correlation function, can fail to distinguish patterns that involve higher-order clustering.

However, topology derived metrics (Minkowski functionals, Betti numers) take into account all orders simultaneosly and can therefore supply additional information. Using pre-existing and developed software, we're able to use the techniques to characterize patterns in both inter-cellular and intra-cellular systems.

(with C. Guven (Ph.D. Student), M. van der Raa (University of Twente, Netherlands), Eilon Sherman (NIH/NCI), E. Rericha (Vanderbilt University), and Larry Samelson (NIH/NCI).

A.) Microfluidic chamber with multiple MicroNets; Simulator: B.) Turning the laser on to move the desired cell; C. Turning the laser off to let the flow drag the cell out of the chamber.
Automatic Cell Manipulation in Optical Tweezers Assisted Microfluidic Systems
Sagar Chowdhury , Ph.D student, Mechanical Engineering
Chenlu Wang, Ph.D student, Biophysics

 Microfluidic devices are well suited for the study of biological objects because of their indirect nature of manipulation and high throughput. However, the cell manipulation process solely depends on the fluid flow and hence precise control is difficult to attain inside a microfluidic chamber. Utilizing optical tweezers as a complementary tool provides precise manipulation control. We have developed an automated cell manipulation approach using optical tweezers operating inside a microfluidic chamber. To test and demonstrate the effectiveness of the approach we have developed a physics-based simulator that is completely automated and allows high precision of manipulation.

(with S.K. Gupta, Mechanical Engineering, University of Maryland, K. Seale and J. Wikswo, Biosystems Research and Education, Vanderbilt University)

Supported by NSF- Cyber-Physical Systems








Last updated by Rachel on Feb 3, 2012.  Please contact rmlee @ umd.edu for updates to this page and wlosert @ umd.edu for questions about the Dynamics of Complex Systems lab.