Dynamics of Complex Biological Systems

At the convergence of physics with biology, our group is motivated both by the desire to gain fundamental insights into the behavior of living systems and by the drive to contribute to the pressing challenges associated with the explosion of quantitative information in medical research.

Our analysis of shape dynamics of migrating cells has led us to discover mechanical waves as a ubiquitous underlying motors in many fast-migrating cells. Another project in the Biodynamics lab is to elucidate how surface chemistry and topography affects migratory machinery, and how internal waves may be harnessed to control cell behavior. We also developed new tools to control the arrangement and dynamics of cell groups via holographic laser tweezers and to investigate the mechanical properties of models of circulating tumor cells. Other projects apply Complex Systems approaches to investigate cancer related biological processes as part of a Cancer Technology interaction between the University of Maryland and the National Cancer Institute. Examples of the many ways studying complex systems provides insight into biological systems are available on this page.

Unidirectional Cell Guidance promoted by Asymmetric Nanotopography

Xiaoyu Sun, Postdoctoral Research Associate
Meghan Driscoll, Former Graduate Student

with J. Fourkas (University of Maryland)

Many biological and physiological processes depend upon directed migration of cells, which is typically mediated by chemical gradients, physical gradients, and signal relay. My work focuses on the effect that local topographic asymmetries on the nano/micro-scale have on attempting to guide Dictyostelium discoideum and lamellipod-driven human neutrophils to migrate in single, preferred directions. Given that these asymmetries can be repeated we can thereby attempt to provide directional guidance over arbitrarily large areas. We are currently studying the relationship between the direction and strength of the guidance and the details of the nano/microtopography. We have thus far demonstrated that asymmetric nano/microtopography guides the direction of internal actin polymerization waves and that cells move in the same direction as these waves. The conservation of this mechanism across cell types and the asymmetric shape of many natural scaffolds suggests that actin-wave-based guidance is important in biology and physiology.

X. Sun, et al., "Asymmetric nanotopography biases cytoskeletal dynamics and promotes unidirectional cell guidance", Proceedings of the National Academy of Sciences, 112(41), pp. 12557-12562, 2015.

Supported by the National Institutes of Health

Left: Sixty-frame (2.55-min) space/time plot of actin waves along a ridge composed of asymmetric sawteeth. Top, right: Actin-wave directionality angle histogram. Bottom, right: Average actin flux around a sawtooth.

Collective Migration during Cancer Progression

Rachel Lee, Postdoctoral Research Associate

with Dr. Stuart Martin (University of Maryland School of Medicine)
Rachel is supported by the T32 Cancer Biology Training Grant at the University of Maryland, Baltimore

In addition to playing a role in processes such as wound healing and development, collective migration is seen in the progression of diseases such as cancer. As tumor cells become more malignant, they gain the ability to migrate throughout the body; in addition to migrating as individual cells, they have been seen to migrate collectively in vivo and there is increasing evidence that collective behavior plays a role metastsis.

Using automated image analysis techniques we are able to extract information such as velocities from images of migrating human epithelial cells. Inspired by tools developed to study fluid flows and moving grains of sand, we have quantified the motion of migrating epithelial sheets and measured differences in malignant and non-malignant cells . We are currently using these tools to understand how the motion of epithelial cells is regulated and how changes in cell migration are linked to metastatic cancer.

Publications:
R.M. Lee, D.H. Kelley, K.N. Nordstrom, N.T. Ouellette, and W. Losert, New J Phys 2013 [IOP]
R.M. Lee, C.H. Stuelten, C.A. Parent, W. Losert, CSPO 2016 [IOP]

Cell Sheet and PIV
Two images of a migrating sheet of MCF10A cells (top) compared to the velocity information derived using particle image velocimetry on these images (bottom).

Determination of Cell Shape PhenoTypes Associated with Micro-Environmental Cues and Stem Cell Fates with Machine Learning Based Methodology

Desu Chen, Ph.D. student, Biophysics

with Julian Candia (National Institutes of Health) and Sumona Sarkar (National Institute of Standards and Technology)

Differentiation of stem cells can be guided by their mechanical responses to the environment. It has been found that human bone marrow stromal cells (hBMSCs) develop osteogenic lineage in some polymer scaffold structures in the absence of chemicals while staying in their original states on some other kinds of substrates (Kumar et al. 2011). Morphological responses of hBMSCs to different microenvironments may play a crucial roles in the early stage of this process despite the fact that the chemical signatures of differentiation usually appear much later. In order to understand the correlation between a cell's morphological response to its microenvironment and its differentiation, we quantify the morphological phenotypes of hBMSCs at early stages with multiple shape descriptors and introduce a machine learning algorithm to find the optimal set of shape descriptors for distinguishing cells in different microenvironments. This approach allows us to account for both multi-parametric complexity and biological heterogeneity. The algorithm also identifies individual representative cell shapes that could be used for cell shape templating to control cell function in current and future studies.

Supported by the NIST-UMD Cooperative Agreement

From molecules to cells to organisms: understanding health and disease with multidimensional single-cell methods

Yang Shen, Ph.D. student, Chemical Physics
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).

Publication: J. Candia et al. PLoS Comput Biol 2013, in press [PLoS] [arXiv]

Supported by a joint appointment from the Department of Physics, University of Maryland (College Park) and the School of Medicine, University of Maryland (Baltimore)

Full-text access to all publications, CV, research interests, and contact information are available here.

(a) Due to cell heterogeneity, single-cell measurements often lead to highly overlapping populations. (b) The novel supercell framework, however, allows us to uncover molecular phenotypes that separate different diseases.

3D Structure Analysis in Biological Systems

Leonard Campanello, Ph.D. student, Physics

with Maria Traver (Postdoc, Uniformed Services University) and Brian Schaefer (Professor, Uniformed Services University)

My research focuses on the extraction, analysis, and visualization of complex, amorphous structures within three dimensional super-resolution images of various types of cells. I am currently studying the role of protein migration, degradation, and co-localization in activated T-cells and the roles that these proteins play in regulating antigen-mediated signaling from the T-cell receptor to the transcription factor, NF-kB. Performing the analysis, aside from the standard image processing, draws on concepts from combinatorics, topology, and graph theory.

Mathematical Modeling in Physics for the Life Sciences

Deborah Hemingway, Ph.D. student, Biophysics

with Joe Redish (University of Maryland)

I am investigating the barriers to using mathematical modeling in physics for the life sciences (IPLS) as part of an exploratory collaborative project with Joe Redish’s Physics Education Research Group. My focus is on researching pre-medical and biology student problem solving amongst a variety of other students; creating analytical tools for describing student resources in the context of the NEXUS/Physics interdisciplinary IPLS course; and developing a set of materials on mathematical modeling.

Maryland Day: Welcome to Cells in Motion!

Lab Outreach

As part of the 2013 Maryland Day event, the Losert Lab prepared a video, "Cells in Motion," which was shown in the inflatable Biomolecular Discovery Dome. We also created an interactive demonstration of our cell tracking software where participants were able to track their motion while playing games such as follow the leader. To watch our video or to learn more, visit our Maryland Day page!

MarylandDay
University of Maryland

Contact

Please contact ljcamp @ umd.edu for updates to this page (last updated June 28th, 2017) and wlosert @ umd.edu for questions about the Dynamics of Complex Systems lab.