The purpose of the Annual q-bio Summer School, founded in 2007 in Los Alamos, NM, is to advance predictive modeling of cellular regulatory systems by providing trtaining in mathematical, statistical, and computational techniques that are important in systems and synthetic biology. A secondary goal is to advance the careers of researchers new to quantitative biology. The school consists of courses in six subjects: 1) stochastic gene regulation, 2) cell signaling, 3) biomolecular simulations, 4) viral dynamics, 5) synthetic biology, and 6) computational neuroscience. Demand for training in quantitative biology is increasing rapidly-the number of qualified summer school applicants increased from fewer than 40 in 2010 to over 170 in 2012. In response to this demand, we expanded the school in 2012 to accommodate more students. The school now takes place at two campuses, in Santa Fe, NM (courses 1-4) and in San Diego, CA (courses 5-6). Approximately 30 students attend at each campus and are diverse in terms of educational background (mathematics, engineering, physical sciences, and biology), career level (~75% are graduate students, ~20% are postdocs, and ~5% are more advanced), and demographics (gender, race, ethnicity, and worldwide geographical origin). Students attend all core lectures in the courses offered at their campus, as well as specialized course-specific lectures, student get-to-know-me talks, and other talks (e.g., talks focused on career skills), and participate in hands-on computer labs and mentored projects. After two intensive weeks, all students gather in Santa Fe for a 2-day q- bio Student Symposium, which features student projects reports, student poster presentations, and external invited speakers. All students then attend the 4-day q-bio Conference, an international conference attended by >200 researchers. All q-bio Summer School participants can expect the following: a) broad exposure to mathematical/statistical/computational tools used in quantitative biology, b) in-depth training in techniques of special interest (i.e., in one of the six course subjects) through course-specific lectures, computer labs, and mentored projects;c) multiple opportunities to practice scientific communication through talks and poster presentations;d) exposure to cutting-edge research, and e) extensive networking opportunities with peers and thought leaders. Lecturers and speakers include more than 50 different researchers active in quantitative biology, including very distinguished scientists. For example, in 2013, confirmed lecturers include six academicians. By the time the students attend the q-bio Conference, they are equipped with a powerful social network that facilitates interactions, idea exchange, and initiation of collaborative research. The long-term goal of the school is to change the way biological research is conducted, making biology a more quantitative field, like physics and chemistry. In this effort, the organizers are supported by significant goodwill from the international quantitative biology community and a number of local institutions, including two national centers for systems biology. However, to maintain and improve the school, additional financial support is required.
Many future biomedical and biotechnological advances in synthetic and systems biology will require investigators who have the ability to carefully integrate quantitative experimentation with mathematical, statistical and computational modeling. The goal of the q-bio Summer School is to prepare a new generation of quantitative biologists who are adept at modeling and/or working with modelers to advance our predictive understanding of cellular regulatory systems. The complexity and importance of these systems, which govern cellular activities and fates, provides motivation for developing a scientific and engineering workforce equipped to deal with the complexity.
|Johnson, Rob; Munsky, Brian (2017) The finite state projection approach to analyze dynamics of heterogeneous populations. Phys Biol 14:035002|
|Fox, Zachary; Neuert, Gregor; Munsky, Brian (2016) Finite state projection based bounds to compare chemical master equation models using single-cell data. J Chem Phys 145:074101|
|Munsky, Brian; Fox, Zachary; Neuert, Gregor (2015) Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics. Methods 85:12-21|
|Chylek, Lily A; Harris, Leonard A; Faeder, James R et al. (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 12:045007|
|Munsky, Brian; Neuert, Gregor (2015) From analog to digital models of gene regulation. Phys Biol 12:045004|
|Hlavacek, William S; Gnanakaran, S; Munsky, Brian et al. (2015) The eighth q-bio conference: meeting report and special issue preface. Phys Biol 12:060401|
|Szyma?ska, Paulina; Gritti, Nicola; Keegstra, Johannes M et al. (2015) Using noise to control heterogeneity of isogenic populations in homogenous environments. Phys Biol 12:045003|
|Resnekov, Orna; Munsky, Brian; Hlavacek, William S (2014) Perspective on the q-bio Summer School and Conference: 2007 - 2014 and beyond. Quant Biol 2:54-58|
|Nemenman, Ilya; Faeder, James R; Gnanakaran, S et al. (2014) The Seventh q-bio Conference: meeting report and preface. Phys Biol 11:040301|
|Senecal, Adrien; Munsky, Brian; Proux, Florence et al. (2014) Transcription factors modulate c-Fos transcriptional bursts. Cell Rep 8:75-83|
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