Understanding Science Through the Lens of Computation

1. Understanding Science Through the Lens of Computation Richard Karp Visit Day 2008

2. The Computational Lens • In many sciences, the natural processes being studied are computational in nature. Viewing natural or engineered systems through the lens of their computational requirements or capabilities, made rigorous through the theory of algorithms and computational complexity, provides important new insights and ways of thinking.

3. Computational Processes in the Sciences • Regulation of protein production, metabolism and embryonic development • Phase transitions of physical systems • Mechanisms of learning • Molecular self-assembly • Strategic behavior of companies • Evolution of Web-based social networks

4. The Computational Lens at Berkeley • The Web, the Internet and Computational Game Theory (Christos Papadimitriou) • Quantum Computing (Umesh Vazirani) • Statistical Physics (Alistair Sinclair, Elchanan Mossel) • Computational Molecular Biology (Michael Jordan, Richard Karp, Elchanan Mossel, Christos Papadimitriou, Satish Rao, Yun Song)

5. A Computational View of Quantum Physics • Quantum physics is the right setting for studying computation at subatomic levels. • Theory of Computation (ToC) is fundamental for understanding the power of quantum computation. • Quantum computation and hence ToC will test of the foundations of quantum physics.

6. Testing the Foundations “Quantum computing is as much about testing quantum physics as it is about building powerful computers.” Umesh Vazirani

7. Highlights • Construction of a universal quantum Turing machine (Bernstein, Vazirani) • Definition of BQP, the class of problems efficiently solvable on a quantum Turing machine (Bernstein, Vazirani) • Quantum Fourier Transform algorithm, a tool for Shor’s polynomial-time factoring algorithm(Hales, Hallgren, Vazirani)

8. Links Between Statistical Physics and Computer Science • Both fields study how macroscopic properties of large systems arise from local interactions. • Statistical physics: properties of water and magnetic materials • Computer science: global properties of World Wide Web, structure of complex combinatorial problems

9. Similarities of Models and Methods • Probabilistic models capture statistical behavior of large, complex, heterogeneous and incompletely known systems. • Phase transitions in statistical physics have close parallels with sharp thresholds in computer science.

10. Areas of Convergence • Constraint satisfaction problems • Belief propagation and error-correcting codes • Markov Chain Monte Carlo • Percolation and sensor networks

11. Highlights • Randomized polynomial-time algorithm for computing the permanent of a nonnegative matrix (Jerrum, Sinclair, Vigoda) • Survey propagation, the best known method for solving random satisfiability problems, combines ideas from statistical physics and computational learning theory.

12. Computational Models of the Web and the Internet • “For the first time, we had to approach an artifact with the same puzzlement with which the pioneers of other sciences had to approach the universe, the cell, the brain, the market” Christos Papadimitriou

13. Computational Models of the Web • The Internet and the Web are simultaneously computational, social and economic. They support new modes of interaction. • Novel algorithmic problems: ranking methods of search engines, reputation systems, recommendation systems, design of auctions and other economic mechanisms, optimal placement of on-line advertisements.

14. Highlight • “Computing a Nash Equilibrium is PPAD Complete” Daskalakis, Goldberg, Papadimitriou

15. Social Sciences and the Web • The Web is a powerful laboratory for studying social and economic systems as computational processes. • Insights from algorithmic game theory are indispensable for understanding the new markets and economic mechanisms that the Internet has spawned.

16. Computational Processes in Biology • Learning in neural networks • Response of immune system to an invading microbe • Specialization of cells during embryonic development • Collective behavior of animal communities: flocking of birds, self-organization of ant colonies • Design of sensor-actuator control systems for regulation of biological processes • Evolution of species

17. Highlights • “Optimal Phylogenetic Reconstruction” (Daskalakis, Mossel, Roch) determines the minimum length of DNA sequences needed to reconstruct the evolutionary history of a set of species. • “Identification of Protein Complexes Conserved in Yeast, Worm and Fly” (Karp et al) infers molecular machines using cross-species analysis of protein interaction data.

18. A Challenge for the Future “We can approach understanding how the whole genome works by breaking it down into groups of genes that interact strongly with each other. Once researchers identify and understand these network modules, the next step will be to figure out the interactions within networks of networks, and so on until we eventually understand how the whole genome works, many years from now. ” Gary Odell

19. And so … • The algorithmic worldview is changing the sciences: mathematical, natural, life, social. • CS is placing itself at the center of scientific discourse and exchange of ideas. • And this is only the beginning …

20. The Power of the Computational Perspective • Exposes the computational nature of natural processes and provides a language for their description. • Brings to bear fundamental algorithmic concepts: adversarial and probabilistic models, asymptotic analysis, intractability, computational learning theory, threshold behavior, fault tolerance, … • Alters the worldviews of many scientific fields.

21. Algorithmic Challenges in Computational Molecular Biology

22. Revolution in Biology • Advances in computation and instrumentation enable a quantitative characterization of biological systems. • Opportunity to advance understanding of molecular processes of life and change the ways we diagnose and treat disease. • Multidisciplinary field: involves the biological, physical, engineering and mathematical sciences.

23. Biological background The eukaryotic cell

24. Goals of Computational Molecular Biology • Sequence and compare the genomes of many organisms. • Identify the genes and determine the functions of the proteins they encode. • Understand how genes, proteins and other molecules act in concert to control cellular processes.

25. Goals of Computational Molecular Biology • Trace the evolutionary history and evolutionary relationships of existing species. • Understand the structure, function and evolutionary history of proteins. • Identify the associations between genetic mutations and disease.

26. Regulation of Gene Expression • Animals can be viewed as highly complex, precisely regulated spatial and temporal arrays of differential gene expression. • Gene expression is regulated by a complex network of interactions among proteins, genomic DNA, RNA and chemicals within the cell.

27. Levels of Regulation • Genome: spells the names of the proteins. • Transcription of genes to mRNA: regulated by binding of transcription factors to DNA in control regions of genes. • Translation of mRNA into functioning proteins, regulated by complex networks of protein-protein and protein-RNA interactions, and by post-translational modifications of proteins.

28. Levels of Regulation (Cont.) • Regulation of metabolic processes: complex network of chemical reactions catalyzed by enzymes. • Global phenotype such as disease: regulated by interaction of many metabolic processes.

29. Key Research Areas • Analysis of protein-DNA interactions: breaking the cis-regulatory code. “ Regulatory interactions mandated by circuitry encoded in the genome determine whether each gene is expressed in each cell, throughout developmental space and time, and, if so, at what amplitude.” Eric Davidson • Analysis of protein-protein interactions: identification of molecular machines and signal transduction cascades.

30. Tools for Analysis • Measurement of protein-DNA and protein-protein interactions, and of mRNA production under perturbed conditions. • DNA sequence analysis to identify genes, their regulatory regions and the transcription factor binding sites within them. • Phylogenetic analysis to identify regulatory structures conserved across species. • Classification of proteins according to structure and function.

31. The Ultimate Goal • ``Portions of the endo16 cis-regulatory system of Strongylocentrotus are to date the most extensively explored of any, with respect to the functional meaning of each interaction that takes place within them. What emerges is almost astounding: a network of logic interactions programmed into the DNA sequence that amounts essentially to a hardwired biological computational device.”