520 likes | 531 Views
If I had to start all over again. Conference Honoring Louis Bolliet May 2008 Jacques Cohen Brandeis University Waltham,MA. Environment at IMAG in the early sixties. Excitement about Computer Science Small cohesive research groups Freedom to engage in novel areas
E N D
If I had to start all over again... Conference Honoring Louis Bolliet May 2008 Jacques Cohen Brandeis University Waltham,MA
Environment at IMAG in the early sixties • Excitement about Computer Science • Small cohesive research groups • Freedom to engage in novel areas • Easiness of interaction with French and European researchers • Abundance of resources
Contents Research • Systems and Synthetic Biology • Social and Technological Networks Education • New Intro Course in CS (MIT)
Strong Interdisciplinary Components • Biology • Physics • Economics • Sociology • Emphasize Generalization over Specialization
The Crucial Role of Systems-and-Synthetic Biology in Computer Science DNA (Static)Bioinformatics Deals mostly with sequences and spatial structures (geometry) DNA (Dynamic) Systems Biology Deals with gene interactions: graphs and dynamic systems
Similarity between Computer Programs and Regulatory NetworksSource Y. Yemini (YY) Computer Science DepartmentColumbia University
Theoretical Foundations • Solution of systems of differential equations (These equations can be rendered discrete) • There is a fascinating theory linking genetic networks (graphs) to the behavior of the differential equations. • The theory was conjectured by Thomas (Brussels) and proved by Soule´ (Bures-sur-Yvette)
There are excellent teams in France doing research on Genetic Networks, Among them: • Hidde de Jong (INRIAlpes) • Hans Geiselmann (U Joseph Fourier) • François Fages (INRIA, Paris) • Denis Thieffry (Marseille) • Gilles Bernot (Nice) • Laurent Trilling (Grenoble) • Jacques Nicolas (Rennes)
How to analyze and design new regulatory networks • In Synthetic Biology we want to design new circuits (gene networks), insert them in a cell and use the cell´s “operating system” to carry out the execution, i.e. produce a substance or effect. • To do this let us to go back to sequencing and synthesis of DNA
Sequencing Costs • 2003- Human Genome Project ($3 billion) • 2007- James Watson Genome ($1 million) • 2008- Applied Bio-Systems ($60,000) • 2011- Estimated ($1,000) The $100 sequencing is achievable sooner than anticipated Complete Genomics and BioNanometrix (MIT Tech ReviewApril 17 2008)
Advantages of inexpensive human genome sequencing • Detection of Single Nucleotide Polymorphisms (SNP) done at doctor´s office • Selecting the best medication for a particular patient • Personalized medicine Dangers • Breaches in privacy • Ethical considerations
DNA Synthesis T C G A ACCGTA ... Computer-Synthesizer
Synthesis of DNA • Related to sequencing (to check for accuracy) • Design of minimal living artificial organisms • The 582,970 base pair M. genitalium bacterial genome is the largest chemically defined structure synthesized in Craig Venter´s lab. (Water-marks have been added)
The mint-banana experiment(a very informal description) Steps • Find somewhere (in a plant) a gene GM that generates a product that smells like mint • Also find a gene GB capable of producing a product that smells like bananas • Design a substance that can inhibit GM and activate GB • Make sure that the process is robust • Place the combined genes, activators and inhibitors in a cell and… • DEBUG!!!
Lofty Goals for Synthetic Biology • Detecting dangerous substances • Drug design • Cleaning oil spills • Eliminating CO2 in the atmosphere • Produce new fuels using e-coli or yeast Synthetic Biology has its Dangers • Bio-hacking • Harmful forms of life (introduce the suicide gene for safety)
France´s own synthetic-biology group • In 2007 a team of young French researchers won the first prize on basic research at the iGEM competition (MIT) • iGEM -The international Genetically Engineered Machine competition • Gregory Batt (formerdoctoral student of Hidde de Jong, INRIAlpes) and Aurelien Rizk (doctoral student of Francois Fages, INRIA) were key partcipants.
Highly Recommended URLs Craig Venter´s videohttp://biosingularity.wordpress.com/category/synthetic-biology/ Article about Venter´s effort in Scientific American http://www.sciam.com/article.cfm?id=longest-piece-of-dna-yet
Contents Research • Systems and Synthetic Biology • Social and Technological Networks Education • New Intro Course in CS (MIT)
Trends in Obesity in the US • Animation New England Journal of Medicine • http://content.nejm.org/cgi/content/full/357/4/370/DC2
Some key researchers (It may all have started with Paul Erdos) • Jon Kleinberg (Computer Scientist, Cornell) • Mark Newman (Physicist, Santa Fe Institute) • Albert-László Barabási (Physicist, biologist, visiting Harvard) • Duncan Watts (Sociologist, Columbia University) • Steven Strogartz (Applied Math, Cornell) • Mark Granovetter (Sociologist, Stanford)
Laws that may govern networks • Small world paradigm • Power law • Law of weak ties
Liben-Lowell Kleinberg Tracing Information flow on a global scale Chain Letters PNAS March 2008
Liben-Lowell Kleinberg (cont 3)Chain letters Final Result • It is possible to determine the parameters for generating trees that mimic very well the behavior of the spread of chain letters. • It is not a small-world behavior
Highly Recommended URLs • Course in Finland (freely downloadable) http://www.cis.hut.fi/Opinnot/T-61.184/ • Cornell Course by Kleinberg and Easley http://www.infosci.cornell.edu/courses/info204/2007sp/ • In France LaBRI (Bordeaux) has a group working on social networks
Contents Research • Systems and Synthetic Biology • Social and Technological Networks Education • New Intro Course in CS (MIT)
MIT’s curriculum revision in EE and CS Hal Abelson http://courses.csail.mit.edu/6.01/ research.google.com/university/relations/eduSummit2007/HalAbelson.pdf
Percentage of applicants indicating interest in EE or CS Number of MIT Domestic Applicants
New EECS Educational Initiatives • Increase integration of life sciences and quantum concepts into EECS Similar to introduction of math in early ‘50s and solid-state physics in early ‘60s • Restructure/renovate the undergraduate curriculum • Reduce the size of the common core so as to increase depth and add flexibility • Current structure is 30 years old • Two core subjects rather than four
New Pedagogical Style • Drastic decrease in lecture time in favor of supervised labs • Labs are more open ended • Most labs involve mobile robots • Small student/staff ratio – we’re aiming for 4:1 • In steady state, a substantial fraction of undergrads will have had teaching experience. This is a design goal of the curriculum reform.
Labs • Programming in Python • Applications to mobile robots • Circuits on breadboards, connected to robot
Concepts discrete probability and state estimation abstracting continuous probability models search, dynamic programming Labs state estimation in simulated discrete worlds robot localization based on noisy sonar robot path planning and execution Dealing with uncertainty Grand finale: locate lights in a maze using head, light sensors, analog and digital control, state estimation, and planning
Final Exam: Question 1 Imagine you have a simple robot with two wheels, each of which has its own motor, and two photo-resistors. • Describe a strategy for driving the robot up to a light, by controlling the wheel velocities depending on the photo-resistor values. • Assume the motors and photo-resistors are connected to your computer, so that you can read and control them in software. Write a step function in Python that implements your strategy. • Design an analog circuit that does the whole job by itself. Explain your strategy in English. • Describe the relative advantages and disadvantages of these two solution strategies.
Conclusion • The described areas share the following: • Huge data sets • Data integration is a must • Work is inherently interdisciplinary • Generality gains over Specialization • Return to continuous models • Important role of probability and statistics
Good News! • Plenty of exciting new work for young researchers. • Exploring new areas is easier and (for many of us) more fulfilling than deepening into well known paths. • Thank you!