7th q-bio Summer School Sponsors

The Seventh Annual q-bio Summer School is supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R25GM105608. The school content is solely the responsibility of the organizers and does not necessarily represent the official views of the National Institutes of Health.

7th q-bio Summer School Organizers

7th q-bio Summer School Students

 

Viral Dynamics

  • Boelen, Lies, Imperial College London
  • Earley, Michelle, Los Alamos National Laboratory
  • Holechek, Susan, Arizona State University
  • Lalanne, Jean-Benot, McGill University
  • Oyler, Jennifer, Memorial Sloan Kettering Cancer Center
  • Jordan, Sheldon Keith, University of New Mexico
  • Soltani, Madjid, University of Waterloo
  • Yaniv, Alon, Ben Gurion University of the Negev

Stochastic Gene Regulation

  • Boddington, Christopher, University of Manchester
  • Donovan, Rory, University of Pittsburgh
  • Li,Yi, University of Texas at Dallas
  • Nguyen, Truong Rice University
  • O'Neill,Patrick, UMBC
  • Phillips, Nick, University of Manchester
  • Takei, Yodai, RIKEN Quantitative Biology Center

Cell Signaling

  • Jaruszewicz, Joanna, IPPT
  • Kuehn, Axel, Systems Biology Ireland
  • Robin, Xavier, Center for Biological Sequence Analysis (CBS)
  • Salazar-Cavazos, Emanuel, STMC at the University of New Mexico
  • Sawicka, Maria, University of Leeds
  • Sheehan, Robert, University of Pittsburgh

Cancer Dynamics

  • Elias, Jan, Universite Pierre et Marie Curie, France
  • Jilkine, Alexandra, University of Arizona
  • Imkeller, Katharina, Humboldt University Berlin
  • Iwanaszko, Marta, Silesian University of Technology
  • Kerketta, Romica, University of New Mexico
  • Li, Biao, Rice University
  • Wojtczak, Dominik, University of Liverpool

Biomolecular Simulations

  • Elias Wolff, Federico, Stockholm University
  • Erasmus, M Frank, UNM Medical School
  • He, Xiuxiu, Georgia State University
  • Nouri, Mariam, Rice University
  • Quezada, Ana, University of California Riverside
  • Sulc, Petr, University of Oxford
  • Whaley, Meagan, Rice University

7th q-bio Summer School Locations

Lectures will take place on two different campuses on weekdays from July 22 through August 2. There will be joint sessions on August 5 and 6 in Santa Fe. All students will then attend The Seventh q-bio Conference in Santa Fe (August 7-10, 2013).

St. John's College, Santa Fe, New Mexico

St. John's College in Santa Fe will host the NM branch of the 7th annual q-bio Summer School as well as the q-bio Conference. The courses offered in Santa Fe are indicated below. Enrollment is limited.

Campus Description. Nestled at 7,300 feet above sea level in the Sangre de Cristo Mountains, the 250-acre Santa Fe campus offers both spectacular scenery and cultural attractions. Santa Fe is the capitol city of the state of New Mexico with a population of 65,000. It has been a Spanish colonial town, a frontier town, and was and still is a center for indigenous culture. Santa Fe is 57 miles from Albuquerque, the nearest metropolitan area. Nearby airports are SAF and ABQ.

University of California, San Diego, California

The school expanded to two campuses in 2011. The course offered in San Diego are indicated below. Enrollment is limited.

7th q-bio Summer School Courses

Course lectures will take place on weekdays during the weeks of July 22 and 29. All students are encouraged to participate in the q-bio Student Symposium (August 5 and 6, 2013, Santa Fe, NM) and The Seventh q-bio Conference (August 7-10, 2013, Santa Fe, NM).

QB1, Stochastic Gene Regulation (Santa Fe, NM)

In this theme, we will explore stochasticity and cell-to-cell variability in the measurement and modeling of biochemical systems. In particular, we will concentrate on the effects that small numbers of important molecules (i.e. genes, RNA, and protein) have on the dynamics of living cells. We will review experimental manifestations of stochastic effects in molecular biology, as can be measured using single-cell and single-molecule techniques. We will discuss the most recent analytical and numerical methods that are used to model these systems and show how these methods can improve interpretation of experimental data. We will study how different cellular mechanisms control and/or exploit randomness in order to survive in uncertain environments. Similarly, we will explore how single-cell measurements of cell-to-cell variability can reveal more information about underlying cellular mechanisms. This section of the summer school will include a number of instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models for gene regulatory systems. Students will then apply these tools to model experimental flow cytometry or other single-cell data. Access and knowledge of Matlab will be helpful, but is not strictly necessary. This section of the summer school is organized by Brian Munsky. Please address all questions about this section of the summer school to its organizer.

QB2, Cell Signaling (Santa Fe, NM)

This series of lectures, which is offered at the Santa Fe campus, will be focused on modeling cell signaling. We will begin with an overview of the inherent features of cell signaling systems, including the modularity of proteins, the importance of post-translational modifications (e.g., multisite phosphorylation), and an overview of cell signaling motifs, such as kinetic proofreading, serial engagement, and regulated recruitment (co-localization of enzymes and substrates). We will then discuss how these features complicate efforts to develop predictive mechanistic models of cell signaling systems and possible solutions, in particular the rule-based modeling approach. We will cover methods for simulating a model, visualizing and annotating a model, and fitting procedures. We will make extensive use of software tools that are compatible with the BioNetGen language (BNGL) or the closely related Kappa language (http://kappalanguage.org/). An example of such a tool is BioNetGen (http://bionetgen.org). For additional information, contact Bill Hlavacek.

QB3, Biomolecular Simulations (Santa Fe, NM)

As system biology thrives to excel in providing cellular level behavior of complex biological systems, it has become imperative to integrate the molecular level events for better understanding at the system level. The objective of this theme is to provide training on computational methodologies to extract molecular level events at different resolutions. In addition we intend to provide a brief review on recent theoretical and computational methods and state-of the-art computing architectures. Finally, we will use examples from our own research to show that the real strength of these computational methodologies can only be materialized when combined with experimental studies. This section of the summer school is organized by "Gnana" S Gnanakaran. Please address all questions about this section of the summer school to its organizer.

QB4, Viral Dynamics (Santa Fe, NM)

This series of lectures focus on viral and immune systems dynamics at the cellular population level, that is the modeling of interactions of immune cells and viruses within an individual. There will be a basic introduction to the biology of these systems, followed by lectures on modeling specific issues/processes/immune systems. We will cover both modeling of the healthy immune system, and viral infections such as human immunodeficiency (HIV), hepatitis (B and C), and influenza. Particular attention will be given to the evolutionary processes shaping these viruses in vivo, including the topic of phylogenetics. The approach to all these subjects will include discussions of the mathematical framework, the types of available data and how to link models and data to generate new hypotheses, estimate parameters and gain the most biological insight from restricted datasets. Group projects will include journal club presentations, small simulations projects, and there will be scope for research-type projects if enough student interest is manifested. This section of the summer school is organized by Ruy Ribeiro. Please address all questions about this section of the summer school to its organizer.

QB5, Cancer (Santa Fe, NM)

In this theme we will address a number of biological and mathematical issues related to modeling of evolution of cancer (using the example of leukemias), organized in three core lectures, which will cover the fundamental issues of cell proliferation and mutation dynamics, molecular events affecting specific pathways in cells and the population genetics effects (see the abstracts further on). This section of the summer school will include a number of instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models of cancer evolution. Students will then apply these tools to model experimental and clinical data. This section of the summer school is organized by Marek Kimmel. Please address all questions about this section of the summer school to its organizer.

QB6, Synthetic Biology (San Diego, CA)

In this theme, students will learn how to use computational modeling tools for design and evaluation of synthetic gene networks. We will review basic modeling approaches including Boolean networks, mass action kinetics, stochastic simulation algorithms and provide hands-on training sessions on using corresponding software tools. Then we will apply these tools to the design and analysis of basic elements of synthetic circuits such as positive and feedback loops, oscillators, toggle switches, logical gates, quorum-sensing circuits, enzymatic machinery. Issues of stochasticity, cell-to-cell variability, robustness and parameter sensitivity will be addressed in depth in regards to evaluation of synthetic circuit performance.

QB7, Computational Neuroscience (San Diego, CA)

In this track, students will learn how to use computational modeling for analysis of biological neural networks. We will introduce basic models including biophysical conductance-based models, simplified rate equations, fast spiking models, synaptic transmission and plasticity and memory models. We will also cover information theory and spike train analysis and provide hands-on training sessions on popular software tools used in computational neuroscience. We will apply these tools to the analysis and simulations of specific neural circuits, synaptic dynamics, learning, and control of neural systems. The relevant topics of bifurcation theory, robustness, sensitivity analysis and information theory will be reviewed in regards to the dynamics of neural circuits. In addition, participants will have the opportunity to implement models of cortical neural dynamics in programmable and reconfigurable analog neuromorphic hardware. The course includes an introduction to neuromorphic engineering from a dynamical systems perspective, and live and interactive demonstrations.

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