The directed propulsion of small scale objects in water is problematic because of the combination of low Reynolds number and strong thermal fluctuations at these length scales. How can we start with a pen and a blank sheet of paper, and use our knowledge of physics and chemistry (and perhaps a bit of inspiration from biology) to design synthetic micro- and nanoswimmers? In this talk, I introduce a number of simple prototypes for model low Reynolds number swimmers and examine their physical properties. I also discuss a number of recent experimental realizations of such devices.
Discovery of Principles of Nature from Matrix and Tensor Modeling of Large-Scale Molecular Biological Data
Host: Joel Cohen
In my Genomic Signal Processing Lab, we believe that future discovery and
control in biology and medicine will come from the mathematical modeling of
large-scale molecular biological data, such as DNA microarray data, just as
Kepler discovered the laws of planetary motion by using mathematics to
describe trends in astronomical data [1]. In this talk, I will first
describe novel generalizations of the matrix and tensor computations that
underlie theoretical physics (e.g., [2,3]), that we are developing for
comparison and integration of multiple genomic datasets recording different
aspects of, e.g., the cell division cycle and cancer. Second, I will
describe our recent experiments [4] that verify a computationally predicted
genome-wide mode of regulation [5,6], and demonstrate that singular value
decomposition (SVD) and higher-order SVD (HOSVD) modeling of DNA microarray
data can be used to correctly predict previously unknown cellular
mechanisms. Third, I will show that mode-1 HOSVD modeling of rRNA sequence
alignments suggests a new way of looking at evolution as a composition of
changes rather than a hierarchy of changes, and might be used to predict
evolutionary mechanisms, i.e., evolutionary pathways and the underlying
structural changes that these pathways are correlated, possibly even
coordinated with [7]. Last, I will describe the computational prognosis of
brain cancers by using generalized SVD to compare global DNA copy numbers in
patient-matched normal and tumor samples from the Cancer Genome Atlas [8,9].
1. Alter, PNAS 103, 16063 (2006); http://dx.doi.org/10.1073/pnas.0607650103
2. Alter, Brown & Botstein, PNAS 100, 3351 (2003);
http://dx.doi.org/10.1073/pnas.0530258100
3. Ponnapalli, Saunders, Van Loan and Alter, under review.
4. Omberg, Meyerson, Kobayashi, Drury, Diffley & Alter, Nature MSB 5, 312
(2009);
http://dx.doi.org/10.1038/msb.2009.70
5. Alter & Golub, PNAS 101, 16577 (2004);
http://dx.doi.org/10.1073/pnas.0406767101
6. Omberg, Golub & Alter, PNAS 104, 18371 (2007);
http://dx.doi.org/10.1073/pnas.0709146104
7. Muralidhara, Gross, Gutell & Alter, PLoS One 6, e18768 (2011),
http://dx.doi.org/10.1371/journal.pone.0018768
8. Lee & Alter, 60th Annual Meeting of the American Society of Human
Genetics (Washington, DC, November 2-6, 2010).
9. Lee, Alpert, Sankaranarayanan & Alter, World DNA and Genome Day (Dalian,
China, April 25-30, 2011).
During solid tumor progression, cells gradually acquire the ability to reproduce in ways deleterious to their host, to acquire nutrients and oxygen, to evade the host immune system and eventually, to remodel their environment, invade surrounding tissues and recapitulate their parent tumor organization in metastases. Progression is often regarded as an inevitable sequel of tumor initiation and its genetic and biomolecular bases are widely addressed. Less studied is somatic evolution within solid tumors, which combine rapid heritable mutation and strong selection (almost all cells in a tumor have no progeny) with great heterogeneity, ideal conditions for rapid evolution. Considering progression from an evolutionary perspective may help explain several apparent paradoxes. E.g., 1) Selection is intrinsically undirected, but results in deterministic progression. 2) Tumor initiation requires the loss of cooperativity typical of normal tissue function, but metastasis requires tumor cells to evolve novel cooperative quasi-organismal regulatory mechanisms. 3) Chemotherapies often result in substantial reduction of tumor mass, but ultimately lead to greater tumor mass and more invasive phenotypes.
Gilberto Thomas, Abbas Shirinifard and I are currently developing multi-cell computer simulations of tumor growth and evolution to address these issues. While the simulations I will show are generic, they are based on experimentally-identified cell-cell interactions and individual cell behaviors. Our simulations show that nutrient limitation, by itself, inevitably leads to reduction in cell-cell adhesivity within a solid tumor, promoting invasiveness. Adding a simplified model of immune clearance of tumor cells leads to an intriguing result in which short-term and long-term survival can conflict: weak immune clearance promotes the appearance of secondary tumors, while strong immune clearance results in increased cell-cell adhesion, preventing the formation of secondary tumors and ultimately producing spontaneous remission. These preliminary results suggest that improved understanding of the basic mechanisms of cancer evolution may ultimately suggest novel, potentially more effective, therapeutic approaches.
Funding: NIGMS R01 GM077138 and R01 GM76692, U. S. Environmental Protection Agency grants RD-83500101-0 and “Texas-Indiana Virtual STAR Center,” the National Academies Keck Futures Initiative and Indiana University’s Faculty Research Support Program and Biocomplexity Institute.
Bio: Dr. Glazier received his B.A. in Physics and Mathematics from Harvard University and his M.S. and Ph.D. in Physics from the University of Chicago. His research focuses on experimental and multi-scale computational approaches to embryology and developmental diseases. He has held faculty appointments at the University of Notre Dame and Indiana University, Bloomington, where he is founding director of the Biocomplexity Institute, Professor of Physics and Adjunct Professor of Informatics and Biology.
Many sensory objects are characterized by continuity of boundaries in space or continuity of form through time. Human sensory perception exploits this continuity to make sense of the natural world. For example, neurons in visual processing streams reinforce each otherʼs responses to enhance the salience of smooth contours. Similarly, neural auditory mechanisms enhance the perception of continuity, both in grouping related portions of a temporal stream, and by filling gaps in auditory streams. Inspired by these properties of human psychophysics we consider how the continuity of form in natural sounds may be used to discover sparse time-frequency representations. To proceed, we describe a method to represent any time-series as a collection of contours in the time-frequency plane. By analysing the signal in many time-scales, an over- complete set of shapes is generated for a given sound. From this redundant set of shapes the simplest, most parsimonious mathematical forms may be selected.
We conclude with a description of two experiments that examine temporal sequence processing in songbirds.
Measuring time during growth and development in Drosophila
Host: S. Jamal Rahi
During its development, the fertilized egg undergoes a series of mitotic divisions, converting it from a single cell into an embryo of many cells, each of which programmed to distinct developmental fates. These mitoses and determinative events occur during a period when the embryo is simultaneously undergoing significant reorganization of its cytoplasm and its transcriptional activity. In these morphological, mitotic and determinative programs, transitions between one state and the next normally occur in a defined sequence, at specific times relative to each other. They are often rapid and switch-like. The internal clocks that govern and potentially coordinate these events are not known.
In my talk, I will describe genetic and molecular experiments that investigate the how Drosophila embryos measure time and produce robust morphological changes. Our experiments focus on the control of the cleavage mitoses, the pause that occurs at the midblastula transition, and the re-initiation of cell division after the embryo has completed gastrulation. We show that unlike similar cell cycle events in other organisms, Drosophila embryos achieve precise switch-like transitions without using feedback circuits between the cyclin-dependent kinase and its regulators, Wee1 and Cdc25. Entry into mitosis is controlled by the level of cdc25 in individual cells, and variations in that level account the variation in division patterns of similarly fated cells.
We also investigate how these division patterns are coordinated with initiation of zygotic transcription and the degradation of maternal messenger RNAs, and identify a population of RNAs whose expression is timed by the nucleo-cytoplasmic ratio. By genetically manipulating N/C ratios to position embryos at thresholds, we determine how tightly cell cycle and transcriptional decisions are coupled.
Gonadotrophin-releasing hormone (GnRH) is a hormone released from the brain to control the secretion of reproductive hormones. Pulsatile GnRH can increase fertility (e.g. in IVF programmes) whereas sustained GnRH reduces fertility (and is used to treat hormone-dependent cancer) but the ways in which the GnRH receptor and its intracellular signalling cascade decode these kinetic aspects of stimulation are essentially unknown. In addition, our knowledge is scarce of the intracellular mechanisms that govern frequency modulation of gonadotropins secretion, much less how such fine-tuning is regulated by different signal inputs. There is an emerging concept that differential expression of gonadotropin subunits gene is associated with modification of activation and/or stability of important regulatory proteins and transcription factors.
We present a signalling pathway model of GnRH-dependent transcriptional activation developed to dissect the dynamic mechanisms of differential regulation of gonadotropin subunits gene. The model incorporates key signalling molecules, including extracellular-signal regulated kinase (ERK) and calcium-dependent activation of Nuclear Factor of Activated T-Cells (NFAT), as well as translocation of activated/inactivated ERK and NFAT across the nuclear envelope. We show that simulations with varying in dose and frequency GnRH pulsatile inputs agree very well with experimental measurements of GnRH-dependent ERK and NFAT responses. In silico experiments designed to probe trancriptional effects downstream of ERK and NFAT reveal that interaction between transcription factors is sufficient to account for frequency discrimination. Finally, using parameter sensitivity and bifurcation analysis we identify key parameter relationships that govern differential expression of gonadotropin subunits gene.
