Research Groups

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Institute of Science and Technology Austria

Tobias Bollenbach
Research: Biological physics and systems biology

  • Sensing of signaling gradients in cell migration
  • Integration of multiple input signals in cell differentiation
  • Cellular responses to conflicting signals
  • Antibiotic resistance and drug combinations
  • Theoretical models of bacterial growth
  • Optimization of gene regulatory responses
  • Theoretical models of animal development

Weizmann Institute

Uri Alon
Research: Systems Biology

  • Network Motifs
  • Design Priziples of Biological Networks
  • Evolution
  • Dynamic Proteomics

Harvard Medical School

Johan Paulsson

  • General mathematical theory for fluctuations in cells
  • Experimental methods to count molecules
  • Partitioning and degradation of molecules
  • Feedback control
  • Gene expression
  • Evolution and conflict

Many genes, RNAs and proteins are present in such low numbers that fluctuations in abundances between otherwise identical cells are inevitable. Such ‘noise’ has now been implicated in a wide range of processes in a variety of organisms. Numerous studies have shown how noise generates phenotypic heterogeneity in cell behavior, how cells can suppress deviations when fluctuations are selected against, and how they can functionally exploit variability for increased fitness. The sources and dynamics of the fluctuations have also been analyzed in great detail, for example focusing on intrinsic versus extrinsic contributions, ‘bursty’ transcription and translation, or the transmission of noise in cascades. The recent explosion of fluctuation studies may be the mere beginning of a sea change in experimental cell biology, as fluorescent reporters are making quantitative single cell experiments routine.

Many insights have been gained from these studies, but as we and others have repeatedly shown, the conventional approaches can also be very misleading. The lack of organizing physical laws combined with low-resolution data and model plasticity make retrodiction and story-telling too easy, while most ‘predictions’ simply interpolate known principles. In some ways the situation is not unlike that of early astronomy: even without any coherent theory, the Babylonian Astronomical Diaries predicted certain celestial events with higher accuracy than the first applications of Newton’s laws, by simply mimicking observed patterns.

Our lab is committed to addressing this problem through a combination of approaches. We study several specific mechanisms in detail, but also place a great emphasis on broad theorems and mathematical methods, and on new libraries and novel experimental counting assays.

Oxford Biological Physics

Mark Leake
Research: Single Molecule Cell Biophysics

  • nanometer length scale imaging with millisecond time resolution (FRET, TIRF, ...)
  • seeing how single molecule properties in a living organism scale up to bring about whole-organism functionality

Full understanding of processes in living organisms is only achievable if all molecular interactions are considered. Cell biology strives to cultivate a full insight into the mechanisms of living cells by investigating interactions that elicit and direct cellular events, though to date the shear complexity of biological systems has caused precise single-molecule experimentation to be far too demanding, instead focusing on studies of single systems using relatively crude bulk ensemble-average measurements. One way forward which we're currently pushing is to monitor several biological systems simultaneously in living, functioning cells using more powerful and precise single molecule techniques, in effect investigating systems level biology from a bottom-up molecular level, eradicating noise rife in systems biology data associated with cell population stochasticity.

Oxford Department of Biochemistry

Bela Novak

Research: Dynamics of molecular regulatory networks

The living cell is a dynamical system of molecular interactions. Most of the physiological properties of the cell (movement, growth and division etc.) are determined by molecular networks rather than by a single molecule. These molecular networks are intrinsically dynamic and they determine how a cell changes in space and time. The understanding of the physiological consequences of these regulatory molecular networks requires computational methods. Our group uses mathematical modelling to build links between cell physiology and the wiring diagram of regulatory networks. The main focus of our research is the eukaryotic cell cycle control system

Humbold University Berlin

Edda Klipp
Research: Mathematical modelling of dynamic biological phenomena

  • understand cellular organization
  • stress response
  • decision making
  • develop predictive models of various signalling pathways, metabolic pathways, cell cycle and the interaction of such pathways upon environmental changes

Virginia Tech

John Tyson

Research: computational cell biology

They study biological systems from a rigorous mathematical perspective, and build realistic models that help them gain a deeper understanding of the physiology. Most of their work is on the mechanism of cell division cycle control.

Université Libre de Bruxelles

Albert Goldbeter

Research: Theoretical Chronobiology

  • Modeling the molecular regulatory mechanisms of biological rhythms: Circadian rhythms and related disorders of the sleep-wake cycle, segmentation clock, cell cycle, metabolic oscillations, oscillations and waves of cyclic AMP in Dictyostelium cells, oscillations and waves of calcium, pulsatile hormone secretion (GnRH, insulin), frequency encoding of pulsatile signals, oscillatory shuttling of transcription factors, complex oscillatory phenomena, transitions between periodic and chaotic behavior.
  • Threshold phenomena in enzyme regulation through phosphorylation-dephosphorylation ("zero-order ultrasensitivity").
  • Coexistence of multiple steady states (bistability).

Rockefeller University

Stanislas Leibler

Research: Analysis of Biological Networks

In recent years molecular biology has moved away from the study of individual components towards the study of many interacting components. The "systemic" approach seeks an appropriate, and if possible, quantitative description of cells and organisms. Both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms.

We try to tackle some basic questions connected with the functioning and evolution of simple genetic and biochemical networks. Some problems studied recently both experimentally and theoretically in our laboratory are:

  • Robustness and sensitivity of networks with respect to biochemical modifications of their components. This problem was addressed for the chemotaxis network in Escherichia coli, where robustness of adaptation and "non-genetic individuality" in cellular responses coexist.
  • Resistance of genetic networks to molecular noise, such as the noise connected with fluctuations in the number of different components. For this we studied genetic networks such as the circadian clock in cyanobacteria. The problem of stochastic behavior on the cellular level was approached in the study of the switching of flagellar bacterial motors.
  • Precision and establishment of proportions (scaling) in spatial pattern formation. This problem was addressed in a quantitative experimental study of the early Drosophila development, where for the anterior-posterior axis both precision and scaling seem to appear in the very first steps of the genetic pattern formation cascade.
  • Design and construction of simple artificial networks with a desired functionality. We constructed and studied simple genetic networks in bacteria, which function as clocks, logical gates, etc.


Michael Elowitz

Genetic circuits, composed of interacting genes and proteins, enable individual cells to respond to signals and environmental conditions, make decisions, and communicate with one another. What are the key design principles of genetic circuits? And, how do these circuits function dynamically in individual cells and multicellular systems? To address these questions, we develop and use several experimental approaches. We build our own synthetic genetic circuits and study their functions in bacteria, yeast, and mammalian cells. These synthetic circuits are simpler counterparts to the complex circuits one finds in nature. This approach is often called "synthetic biology."


James E. Ferrel

We have been studying the system of regulatory proteins that drives the cell cycle, through a combination of quantitative experimental approaches, computational modeling, and the theory of nonlinear dynamics. Our goal is to understand the design principles of this system, and perhaps to gain insight into the systems that drive other biological oscillations (e.g. heart beats, calcium oscillations, circadian rhythms) as well.