120 Jadwin Hall
Lab (609) 258-0105
Quantitative approaches to systems and developmental biology
Interest and Focus
Traditionally, biological questions have been investigated with qualitative techniques that allow for interpretation classically in the context of evolution. This qualitative approach, however, struggles to adequately describe the dynamic nature of most of the essential biological processes upon which evolution is acting. Recent advances in molecular biology, optical microscopy, nanoscopic physics and computer science have opened up new avenues for interpreting biological phenomena, combining high-precision measurement of biological processes with theoretical predictions and models that are bound by physical principles and formulated in mathematical language. This allows for models to be numerically tested and validated by experiments and, conversely, for experiments to be designed and guided by theoretical models. My laboratory uses such an approach to understand a biological system holistically, within a framework of fundamental physical principles that dictate and constrain biological phenomena.
Methods and Models
Research in the lab is highly interdisciplinary. The interests and expertise of the lab's members range from physics to biology to computer science to engineering; we use a combination of computational and experimental approaches. We build microscopes and microfluidic devices to measure the concentrations dynamics of proteins and signaling molecules; we use tools from molecular biology and genetics to manipulate the organisms we study; and we use image analysis and modeling to analyze our data. Researchers are encouraged to move freely between the different disciplines and to learn a variety of techniques according to their specific needs and interests. We primarily address questions concerning the development of fruit fly embryos and emergent collective behavior via cell signaling in amoeba populations, but we are open to new ideas and collaborations addressing questions in other model systems.
Specific example research projects
1. Signaling and emergent collective behavior in cell populations
Cells often communicate via concentration oscillations of small molecules, such as ions in neurons or cAMP in stressed (starved) cells of the social amoeba Dictyostelium. However, the "code'' or the language that these cells use to change their behavior is largely unknown. Optical concentration measurements of the relevant oscillating constituents will be used to measure the spatiotemporal communication dynamics among a few Dictyostelium cells. Simultaneously, a combination of electro-physiological and optical measurements will be used to measure similar dynamics in a developing network of cultured neurons. In both cases, communication among cells via oscillations affects the resulting behavior, i.e., aggregation for amoebae, network rewiring for neurons. The goal is to seek common strategies and mechanisms these cells use to change their behavior to form an organism, ultimately revealing unifying principles of communication in different systems.
2. Spatio-temporal patterning and differentiation during early development
In many multicellular organisms, cells differentiate in a hierarchical cascade of gene expression in order to determine a well defined spatio-temporal pattern. Although the qualitative picture of this process is well laid out, quantitatively we know very little about the underlying mechanisms that account for a precise readout of small transcription factor concentration differences or for the exact positioning of gene expression boundaries. Using organisms that express fluorescently labeled proteins or mRNA molecules, the spatiotemporal dynamics of an entire branch of the differentiation cascade will be measured and analyzed. Particular focus will be given to generation and transmission of positional information in the organism, as well as to the precision of the readout mechanisms that ensures a proper handling of the intrinsic sources of noise associated with transcriptional processes. The main focus of this research will be to analyze the gene expression pattern of the segmentation network in early Drosophila embryos and the differentiation processes in starved, aggregating cells of the cellular slime mold Dictyostelium.
3. In utero imaging of early mammalian development
In a crucial moment of mammalian development, the maturing blastocyst implants itself in the uterus. The mother starts to transmit vital information to the embryo, with the formation of the body axes happening very soon thereafter. To date, this stage has only been visualized using fixation methods; it has never been observed dynamically. We are currently developing novel techniques to image, in utero, the embryos of anesthetized mice. As a first step, we will use fluorescent proteins to label the embryo ubiquitously. We will then label specific proteins to observe and eventually quantify patterning processes, such as axis formation.
Abouchar L, Petkova MD, Steinhardt CR, Gregor T. (2014) Fly wing vein patterns have spatial reproducibility of a single cell. J R Soc Interface. 11: 20140443. Pubmed
Bothma JP, Garcia HG, Esposito E, Schlissel G, Gregor T, Levine M. (2014) Dynamic regulation of eve stripe 2 expression reveals transcriptional bursts in living Drosophila embryos. Proc Natl Acad Sci. Jul 3. [Epub ahead of print]
Petkova MD, Little SC, Liu F, Gregor T. (2014) Maternal origins of developmental reproducibility. Curr Biol. 24: 1283-88. Pubmed
Krotov D, Dubuis JO, Gregor T, Bialek W. (2014) Morphogenesis at criticality. Proc Natl Acad Sci. 111: 3683-88. Pubmed
Dubuis JO, Tkacik G, Wieschaus EF, Gregor T, Bialek W. (2013) Positional information, in bits. Proc Natl Acad Sci. 110: 16301-8. Pubmed
Little SC, Tikhonov M, Gregor T. (2013) Precise developmental gene expression arises from globally stochastic transcriptional activity. Cell. 154: 789-800. Pubmed
Little SC, Gregor T. (2013) Sorting sloppy sonic. Cell. 153: 509-10. Pubmed
Liu F, Morrison AH, Gregor T. (2013) Dynamic interpretation of maternal inputs by the Drosophila segmentation gene network. Proc Natl Acad Sci. 110: 6724-29. Pubmed
Dubuis JO, Samanta R, Gregor T. (2013) Accurate measurements of dynamics and reproducibility in small genetic networks. Mol Syst Biol. 9: 639. PubMed
Little S, Tkacik G, Kneeland T, Wieschaus E, Gregor T. (2011) The formation of the Bicoid morphogen gradient requires protein movement from anteriorly localized mRNA. PLoS Biol. 9: e1000596. PubMed
Gregor T, Fujimoto K, Masaki N, Sawai S. (2010) The onset of collective behavior in social amoebae. Science. 328: 1021-25. PubMed
Tkacik G, Gregor T, Bialek W. (2008) The role of input noise in transcriptional regulation. PLoS One. 3: e2774. PubMed
Gregor T, McGregor AP, Wieschaus EF. (2008) Shape and function of the Bicoid morphogen gradient in dipteran species with different sized embryos. Dev Biol. 316: 350-58. PubMed
Gregor T, Tank DW, Wieschaus EF, Bialek W. (2007) Probing the limits to positional information. Cell. 130: 153-64. PubMed
Gregor T, Wieschaus EF, McGregor AP, Bialek W, Tank DW. (2007) Stability and nuclear dynamics of the Bicoid morphogen gradient. Cell. 130: 141-52. PubMed
Gregor T, Bialek W, de Ruyter van Steveninck RR, Tank DW, Wieschaus EF. (2005) Diffusion and scaling during early embryonic pattern formation. Proc Natl Acad Sci. 102: 18403-07. PubMed