Education & Certifications
Doctor of Philosophy, Stanford University, BIOE-PHD (2014)
Bachelor of Arts, University of California Berkeley, Physics (2006)
Bachelor of Science, University of California Berkeley, Bioengineering (2006)
Master of Science, Stanford University, BIOE-MS (2010)
A dynamically assembled cell wall synthesis machinery buffers cell growth
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (12): 4554-4559
Assembly of protein complexes is a key mechanism for achieving spatial and temporal coordination in processes involving many enzymes. Growth of rod-shaped bacteria is a well-studied example requiring such coordination; expansion of the cell wall is thought to involve coordination of the activity of synthetic enzymes with the cytoskeleton via a stable complex. Here, we use single-molecule tracking to demonstrate that the bacterial actin homolog MreB and the essential cell wall enzyme PBP2 move on timescales orders of magnitude apart, with drastically different characteristic motions. Our observations suggest that PBP2 interacts with the rest of the synthesis machinery through a dynamic cycle of transient association. Consistent with this model, growth is robust to large fluctuations in PBP2 abundance. In contrast to stable complex formation, dynamic association of PBP2 is less dependent on the function of other components of the synthesis machinery, and buffers spatially distributed growth against fluctuations in pathway component concentrations and the presence of defective components. Dynamic association could generally represent an efficient strategy for spatiotemporal coordination of protein activities, especially when excess concentrations of system components are inhibitory to the overall process or deleterious to the cell.
View details for DOI 10.1073/pnas.1313826111
View details for Web of Science ID 000333341100053
Single-cell NF-kappa B dynamics reveal digital activation and analogue information processing
2010; 466 (7303): 267-U149
Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-alpha, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-kappaB. We measured NF-kappaB activity in thousands of live cells under TNF-alpha doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-kappaB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-alpha-induced NF-kappaB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.
View details for DOI 10.1038/nature09145
View details for Web of Science ID 000279580800043
View details for PubMedID 20581820
A Noisy Paracrine Signal Determines the Cellular NF-kappa B Response to Lipopolysaccharide
2009; 2 (93)
Nearly identical cells can exhibit substantially different responses to the same stimulus. We monitored the nuclear localization dynamics of nuclear factor kappaB (NF-kappaB) in single cells stimulated with tumor necrosis factor-alpha (TNF-alpha) and lipopolysaccharide (LPS). Cells stimulated with TNF-alpha have quantitative differences in NF-kappaB nuclear localization, whereas LPS-stimulated cells can be clustered into transient or persistent responders, representing two qualitatively different groups based on the NF-kappaB response. These distinct behaviors can be linked to a secondary paracrine signal secreted at low concentrations, such that not all cells undergo a second round of NF-kappaB activation. From our single-cell data, we built a computational model that captures cell variability, as well as population behaviors. Our findings show that mammalian cells can create "noisy" environments to produce diversified responses to stimuli.
View details for DOI 10.1126/scisignal.2000599
View details for Web of Science ID 000275604000003
View details for PubMedID 19843957
- Principles of Bacterial Cell-Size Determination Revealed by Cell-Wall Synthesis Perturbations CELL REPORTS 2014; 9 (4): 1520-1527
- Systematic Perturbation of Cytoskeletal Function Reveals a Linear Scaling Relationship between Cell Geometry and Fitness CELL REPORTS 2014; 9 (4): 1528-1537
The role of hydrolases in bacterial cell-wall growth
CURRENT OPINION IN MICROBIOLOGY
2013; 16 (6): 760-766
Although hydrolysis is known to be as important as synthesis in the growth and development of the bacterial cell wall, the coupling between these processes is not well understood. Bond cleavage can generate deleterious pores, but may also be required for the incorporation of new material and for the expansion of the wall, highlighting the importance of mechanical forces in interpreting the consequences of hydrolysis in models of growth. Critically, minimal essential subsets of hydrolases have now been identified in several model organisms, enabling the reduction of genetic complexity. Recent studies in Bacillus subtilis have provided evidence for both the presence and absence of coupling between synthesis and hydrolysis during sporulation and elongation, respectively. In this review, we discuss strategies for dissecting the relationship between synthesis and hydrolysis using time-lapse imaging, biophysical measurements of cell-wall architecture, and computational modeling.
View details for DOI 10.1016/j.mib.2013.08.005
View details for Web of Science ID 000328095900016
High-throughput, single-cell NF-kappa B dynamics
CURRENT OPINION IN GENETICS & DEVELOPMENT
2010; 20 (6): 677-683
Single cells in a population often respond differently to perturbations in the environment. Live-cell microscopy has enabled scientists to observe these differences at the single-cell level. Some advantages of live-cell imaging over population-based methods include better time resolution, higher sensitivity, automation, and richer datasets. One specific area where live-cell microscopy has made a significant impact is the field of NF-?B signaling dynamics, and recent efforts have focused on making live-cell imaging of these dynamics more high-throughput. We highlight the major aspects of increasing throughput and describe a current system that can monitor, image and analyze the NF-?B activation of thousands of single cells in parallel.
View details for DOI 10.1016/j.gde.2010.08.005
View details for Web of Science ID 000285229000016
View details for PubMedID 20846851
Computational modeling of mammalian signaling networks
WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE
2010; 2 (2): 194-209
One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. In this study, we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of 'model-driven discovery': cases in which computational modeling of a signaling network has led to new insights that have been verified experimentally.
View details for DOI 10.1002/wsbm.52
View details for Web of Science ID 000283711700007
View details for PubMedID 20836022
AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
2007; 24 (6): 1580-1600
We describe an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. Included in AIDA is a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. We validated AIDA using synthetic data spanning a broad range of signal-to-noise ratios and image types and demonstrated the algorithm to be effective for experimental data from adaptive optics-equipped telescope systems and wide-field microscopy.
View details for Web of Science ID 000246575400010
View details for PubMedID 17491626