3rd q-bio Summer School Themes

After the summer school, students will participate in the Third q-bio Conference in Santa Fe, NM, August 5-9, 2009; this facilitated the choice of the themes for the summer school. They are

Signal Transduction Systems

Lecture 1

Scope
An introduction to the cell signaling problem
Lecturer
Byron Goldstein, Los Alamos National Laboratory

Lecture 2

Scope
Spatiotemporal aspects of signal transduction: models and experiments
Lecturer
Bridget S. Wilson, University of New Mexico Cancer Center

Lecture 3

Scope
Rule-based modeling
Lecturer
William S. Hlavacek, Los Alamos National Laboratory

Lecture 4

Scope
Applications of rule-based modeling
Lecturer
Richard G. Posner, Translational Genomics Research Institute

Lecture 5

Scope
Advanced methods of rule-based modeling
Lecturer
James R. Faeder, University of Pittsburgh School of Medicine

 

Stochastic Signal Processing in Biomolecular Systems

In this theme, we will explore stochasticity in biochemical and systems biology modeling. As the subject is immense in its scope, we will be limited necessarily to exploring just a small section of the related topics. Specifically, we will review experimental manifestations of stochastic effects in biology, the methods used to treat them analytically and numerically, and effects of the stochasticity on behavior of biomolecular signal processing systems.

This section of the summer school is organized by Ilya Nemenman. Please address all questions about this section of the summer school to its organizer.

Lecture 1

Scope
Stochastic effects in systems biology: Theoretical Foundations and Experimental Results, Part I
Lecturer
Brian Munsky, Slides:File:Munsky Slides QBIO 09.pdf
Topics
Introduction to Stochasticity and the Master Equation.
The importance of stochasticity in gene regulatory networks. Discussion of a couple key examples.
The physics behind stochastic chemical kinetics.
Connection between deterministic reaction rates and propensity functions.
Derivation of the Master Equation for discrete stochastic processes.
Analysis of the master equation for a simple transcription process.
Discussion of the importance of stochasticity in small populations.

Lecture 2

Scope
Stochastic effects in systems biology: Theoretical Foundations and Experimental Results, Part II
Lecturer
Brian Munsky
Topics
Solving the Chemical Master Equation.
Solution of the master equation for systems with affine linear propensity functions.
Discussion of the effect of feedback.
Discussion of Kinetic Monte Carlo algorithms. Tau Leaping. Chemical Langevin equation. Time separation schemes. Hybrid methods.
Finite State projections techniques. Switch rate analysis.
Moment Closure techniques.
Homework
Homework 1: File:QBIO HWK1.pdf
Homework 2: Stochastic Analysis of a toggle Switch: File:Toggle HWK.pdf

Lecture 3

Scope
Methods of generating functional in stochastic kinetics: enzymatic reactions and molecular motors
Lecturer
Nikolai Sinitsyn

Lecture 4

Scope
Signal processing in biochemical networks: Fourier transforms, central limit heorem, linear feedback, and all that, Part I
Lecturer
Ilya Nemenman

Lecture 5

Hour I
Stochastic processes in population genetics and evolution
Lecturer
Anton Zilman
Topics
 
First passage problems in Master equation: importance of the outliers
Fisher model: genetic drift and fixation as a gambling problem
Hour II
Bioinfiormatics and evolution of HIV
Lecturer
Bette Korber
Topics
Introduction to phylogenetic inference
Applications to HIV infection

Lecture 6

Scope
Signal processing in biochemical networks: Information theory, noise suppression, form and function, and all that. Part II
Lectures
Ilya Nemenman

Lecture 7

Scope
Bacterial chemo- and thermo-taxis as examples of stochastic signal processing systems
Lecturer
Will Ryu

Lecture 8

Scope
Bacterial chemo- and thermo-taxis as examples of stochastic signal processing systems
Lecturer
Will Ryu

Gene Regulation

Lecture 1

Scope
Design principles
Lecturer
Michael A. Savageau, UC/Davis, Los Alamos Ulam Scholar

Lecture 2

Scope
Modeling mechanisms of bacterial gene regulation
Lecturer
Michael E. Wall, Los Alamos National Laboratory

Lecture 3

Scope
Gene circuit structure and function
Lecturers
Michael A. Savageau and Michael E. Wall

Other Topics in Biological Modeling

Lecture 1

Scope
Modeling Cancer Development, Part I Experimental models
Lecturer
James Freyer, LANL
Abstract
A solid tumor in a human is arguably one of the most unique, complex and chaotic biological systems in existence. Contributing greatly to this complexity is the highly heterogeneous tumor microenvironment, which has both spatial and temporal variations in an unaccountably large number of parameters (extracellular chemistry, cellular physiology, metabolism, gene expression and protein composition, to name a few). Unfortunately, this unique microenvironment has numerous adverse effects on the response of a tumor to essentially every therapy that has been devised to date. Thus, improving our understanding of this extremely complex biological system will have benefits for cancer therapy as well as for basic biology. An increasingly important tool in this field is the use of model systems, both experimental and theoretical. This lecture will start with a basic description of the tumor microenvironment, including mechanisms behind the heterogeneity, recent advances in assaying the microenvironment, and impacts on cancer therapy. This will be followed by a description of three-dimensional (3-D) experimental tumor models, focusing on the multicellular spheroid that we use in our laboratory. Two examples of our recent experimental work with spheroids will demonstrate how this system can be used to answer basic questions on the regulation of the cell cycle and protein expression. A new application for spheroids will be presented, along with a new experimental model system we have under development. The lecture will conclude with a description of theoretical models used to describe tumor growth and the tumor microenvironment, including an introduction to a multiscale model developed at Los Alamos that will be presented in much more detail in a subsequent lecture in this series.

Lecture 2

Scope
Protein dynamics
Lecturer
Hans Frauenfelder
Abstract
Proteins are involved in essentially every biological reaction. In texts they are usually shown just as X-ray diffraction reveals them, namely rigid, without hydration shell and bulk solvent. Proteins are, however, dynamic systems that continuously fluctuate and the fluctuations are essential for functions. The lecture will discuss two concepts that are the basis for understanding the dynamics, namely the existence of an energy landscape and the influence of the hydration shell and the bulk solvent.

Lecture 3

Scope
How to Model HIV Infection
Lecturer
Alan Perelson
Abstract

Mathematical modeling of HIV infection has lead to asignificant dvances in our knowledge about HIV and its treatment. This lecture will provide a tutorial about how one goes from data to developing models that have practical utility. Modeling will involve ordinary differential equations. The basic biology of HIV will be reviewed as well as the action of drugs used in therapy. If you have questions about HIV/AIDS this would be good venue to ask them.

References

Perelson, A. S. and Nelson, P. (1999). Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev. 41, 3-44.

Callaway, D. S. and Perelson, A. S. (2002). HIV-1 infection and low steady-state viral loads. Bull. Math. Biol. 64, 29–64.

Perelson, A. S. (2002). Modelling viral and immune system dynamics. Nature Rev. Immunol. 2, 28-36.

Rong, L., Feng, Z. and Perelson, A. S. (2007). Mathematical analysis of age-structured HIV-1 dynamics with combination therapy. SIAM J. Appl. Math. 67, 731-756.

Lecture 4

Scope
Modeling Cancer Development
Lecturer
Yi Jiang
Abstract

Lecture 5

Scope
Cell-Based Modeling Approach: GGH and CompuCell3D
Lecturer
James Glazier

Lecture 6

Scope
Cancer Systems Biology
Lecturer
[Vito Guaranta]
Abstract

Systems Biology (SB) can be viewed as a system of linked coordinates that slides alongBiological Scales. SB practitioners still tend to work primarily at one particular biological scale, but their distinctive trait is a worry about connecting, or integrating, with scale levels above and below. There are misconceptions about Systems Biology, e.g., that it isa mindless accumulation of data by some high-throughput means, no hypothesis necessary prior to experimentation, or, more dangerous, that large amounts of data automatically provide important answers. One truth about SB is that, for now, it can be comfortably ignored by “Conventional Biology”. If Cancer Systems Biology entails a system of linked coordinates that one can slide along the Biological Scales of Cancer, then what are the Biological Scales of Cancer? They span the whole of life, from genes to populations. In modern cancer research, there is a general disconnect between scales, even between disciplines that focus on one scale, e.g., cancer epidemiology andcancer biology. This has produced an enormous loss of information, because it dismisses the concept of emergent properties, e.g., cells perform functions like proliferation or motility that cannot be assigned to elements of a lower scale, molecules or genes, for instance. However, these functions are possible because of genes and molecules. In addition a fundamental misconception is that emergent properties are something mysterious and cannot be addressed quantitatively. There is an unmet need to define cancer progression in terms of emergent properties as one moves from one scale to the other. A key activity in Cancer SB is collecting large datasets. Major barriersto producing large datasets have been mostly removed for genetic or proteomic data. However, they still exist at higher scale. Particularly noteworthy is the dearth of data at the cell biology level, which hinders the mapping of genotype to phenotype. By necessity, large datasets require modeling for interpretation, from statistical to mathematical to computational, since they defy intuition. While large datasets are not a prerequisite to begin modeling cancer, eventually they are an absolute need. A major challenge in cancer SB remains data collection, interpretation, integration and incorporation into models at the cellular scale.

Lecture 7

Scope
Integration of Experimentation with Modeling
Lecturer
Alissa Weaver
Abstract

This lecture gives an introduction to integration of experimentation with mathematical and computational modeling at all stages. The lecture will include the following parts: 1) Rationale for integration; 2) Stages of integration: i: premodel: framing the question; ii: generation of assumptions; iii: parameterization; iv: validation/testing of model predictions, both general and specific; v: model development/modification; 3) Philosophy on best use of data-model integration. Concrete examples of types of experimental integration (by stage) and the usefulness will be discussed.

Lecture 8

Scope
Mathematical Modeling of Epidemiology / Writing Custom Term Papers
Lecturer
Mac Hyman

3rd q-bio Summer School Travel Information and Lodging

Flying in

You can fly either to Albuquerque airport (ABQ) or to Santa Fe airport (SAF). Service to Santa Fe is very limited and expensive, though the airport is closer. From our personal experience, you may want to avoid the last flight of the day to either of the airports, and especially to Santa Fe. Both airports are quite small by big-city standards, and rebooking may be a problem if your flight is cancelled or delayed due to weather or other problems.

Getting to Los Alamos from the airports

Shuttles to Los Alamos from both airports are about $80 or more, and they need to be reserved in advance. Currently, the shuttle companies are

  • Roadrunner Shuttle (1-505-424-3367)
  • LA Shuttle (1-505-695-9407)

A viable alternative is a one-way car rental from either airport to Los Alamos. You should be able to get LANL contracted rate if you rent from Hertz and supply the LANL Hertz corporate discount number 72120 at the time of reservation and, more importantly, rental. This might be cheaper than other rental options since you will be able to decline those insurance coverages that are built into the contract rates. The Hertz car rental dropoff at Los Alamos (at Los Alamos County Airport, which has no commercial air service) is within a walking distance from the school hotel. Hertz rents only to those 21 years old or older.

Driving time to Los Alamos from Santa Fe and Albuquerque airports is about one and two hours, respectively. Traveling on a shuttle will take longer.

Transportation in Los Alamos

Atomic City Transit (the Blue route and the Trinity Shuttle) provides free scheduled service between the school hotel (County Offices stop) and the LA Research Park (TA-3 Park and Ride Stop, and then 5 minutes walk), where the lectures will be held.

Weekend Transportation

There is no bus service on weekends. If you want to explore the Los Alamos vicinity, go to Santa Fe, Bandelier National Monument, or elsewhere, you will need to rent a car. You won't be able to use LANL contract rates for these personal rentals.

Parking

If you are bringing your own car, The school hotel has ample parking. Parking near the LA Research Park, where the lectures will be held, is by permit only. The permits will be available for all needing them in the school booklet, which you will receive on the first day of classes.

Transportation to the q-bio Conference

We anticipate that many of you will able to share car rides to the q-bio Conference at St. John's College in Santa Fe in the morning of Aug 5, 2009. Those who cannot get a ride this way, will be able to get to Santa Fe using public transit and/or a van provided by the organizers.

Meals

Continental breakfasts will be served at the hotel. Lunches will be catered at the school classrom during weekdays. For the rest of the meals, you should plan for $7-15 for lunches, and $15-25 for dinners at Los Alamos town restaurants. Note that very few places in town remain open late (and late means past 7pm in Los Alamos).

Entertainment

For things to do during the weekends, see What to do in the vicinity of Los Alamos. Also, keep in mind that we will organize some weekend trips for interested students, like rafting, hiking, etc.

School Hotel

Summer school students will be housed at the Best Western Hilltop House Hotel in Los Alamos. The hotel features high-speed internet access, microwaves, and refrigerators in every room.

Rooms will be double occupancy for students who are receiving travel awards. If you have roommate preferences, please contact the organizers.

Check in is on Sunday, July 19. When checking in, you will need to provide your name and valid ID (e.g., a passport) to the hotel staff.

Atomic City Transit, Route 1 - Downtown Circulator, provides free bus service from the hotel to Los Alamos National Laboratory (LANL). The closest bus stop to the hotel is stop D (4th St & Central Ave). The closest bus stop to LANL is the Transit Center. The bus runs every 20 min between 6 AM and 7 PM, Monday through Friday.

Classrooms

The site of the summer school is Room 203A/B in the Los Alamos Research Park building at 4200 W Jemez Rd (map). This room is around the corner from Hot Rocks Java Cafe, a coffee and sandwich shop with free Wi-Fi. Across from the building is Technical Area 3 (TA-3) of Los Alamos National Laboratory (LANL). The building sits on the rim of Los Alamos Canyon (map).

About the surrounding area

The network of hiking trails throughout Los Alamos can be accessed from the research park. One can drive from the research park to the Pajarito Mountain ski/mountain biking area in about 10 minutes. By car, one can reach sites such as the Santa Fe National Forest, Bandelier National Monument, and the Valles Caldera National Preserve in less than 30 minutes.

  • Map - the Los Alamos Research Park is at the top of the map, a bit to the left from the center; the building number is 4200.
  • Parking permits will be available upon request.
  • Wireless and wired internet access will be available at the classroom. A printer will also be available.

What to do in the vicinity of Los Alamos

Albuquerque

Note that Albuquerque is at about 6,000 ft elevation, and the nearby Sandia Crest is about 10,500 ft (+- a hundred feet or so, depending if you go to the Crest or to the Tram). This is 1,800-3,200 m for the Europeans among you. Unless you are one of those lucky ones who lives in the Alps most of the year, hiking up the Sandia Crest in your first day in New Mexico is probably not the best idea.

  • Sandia Mountain is a nice hike (La Luz trail) and/or cable car ride (half a day to a day).
  • Old Town area will take you a few hours to explore (but don't forget that Santa Fe offers a lot more antiquities and Indian artifacts).
  • Petroglyphs National Monument offers some nice hikes -- better be here early in the day, or the sun will burn you into ashes.

Santa Fe

Santa Fe is a remarkably cultural city for its size. It's the second biggest art market in the USA and the second oldest surviving city in the USA. The best way to enjoy the city is strolling in the downtown, visiting galleries (there are over three hundred of them in the city), and eating at nice restaurants (again, over three hundred, some rated as exceptional and to eat at before you die by Zagat or New York Times).

Los Alamos

Back to The First q-bio School.

Back to The First q-bio Conference.

Back to The Second q-bio Conference.

Back to The Second q-bio School.

3rd q-bio Summer School Students

  1. Roberto Bertolusso, Rice University
  2. Fiona A. Chandra, California Institute of Technology
  3. Lily A. Chylek, University of New Mexico
  4. Scott C. Clark, Cornell University and LANL
  5. Bernie J. Daigle Jr., Stanford University
  6. Flor A. Espinoza-Hidalgo, University of New Mexico
  7. Pau Formosa-Jordan, Universitat de Barcelona
  8. Alex Greenfield, New York University
  9. Justin S. Hogg, University of Pittsburgh
  10. Albertas Janulevicius, Delft University of Technology
  11. Lili Jiang, IBM T.J. Watson Research Center
  12. Yuki Kimura, University of Illinois at Urbana-Champaign
  13. Amit Lakhanpal, California Institute of Technology
  14. Ganhui Lan, IBM T.J. Watson Research Center
  15. Chien-Chi Lo, Los Alamos National Laboratory
  16. Jason Lomnitz, University of California, Davis
  17. Aviv Madar, New York University
  18. Natasa Miskov-Zivanov, Carnegie Mellon University
  19. Jatin Narula, Rice University
  20. Tin Yau Pang, Stony Brook University
  21. Anil Raj, Columbia University
  22. Pablo Sartori-Velasco, IBM T.J. Watson Research Center
  23. John Sekar, University of Pittsburgh
  24. Thomas Sokolowski, AMOLF
  25. Ophelia Venturelli, California Institute of Technology
  26. Sydeaka Watson, Baylor University and LANL
  27. Steffen Werner, University of Toronto
  28. Yang Zhang, New Mexico State University

3rd q-bio Summer School Lecturers

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