EE 5393: Circuits, Computation and Biology

1. AND OR AND Marc D. Riedel Assistant Professor, ECE University of Minnesota EE 5393: Circuits, Computation and Biology

2. Who is this guy? • Most of the cells in his body are not his own! • Most of the cells in his body are not even human! • Most of the DNA in his body isalien! “Minnesota Farmer”

3. Who is this guy? He’s a human-bacteria hybrid: • 100 trillion bacterial cells of at least 500 different types inhabit his body. [like all of us] vs. • only 1 trillion human cells of 210 different types. “Minnesota Farmer”

4. What’s in his gut? Who is this guy? He’s a human-bacteria hybrid: • 100 trillion bacterial cells of at least 500 different types inhabit his body. [like all of us] vs. • only 1 trillion human cells of 210 different types. “Minnesota Farmer”

5. What’s in his gut? “E. coli, a self-replicating object only a thousandth of a millimeter in size, can swim 35 diameters a second, taste simple chemicals in its environment, and decide whether life is getting better or worse.” – Howard C. Berg About 3 pounds of bacteria! flagellum

6. Bacterial Motor

7. Bacterial Motor Electron Microscopic Image

8. We should put these critters to work… “Stimulus, response! Stimulus response! Don’t you ever think!”

9. Synthetic Biology • Positioned as an engineering discipline. • “Novel functionality through design”. • Repositories of standardized parts. • Driven by experimental expertise in particular domains of biology. • Gene-regulation, signaling, metabolism, protein structures …

10. Building Bridges • Quantitative modeling. • Mathematical analysis. • Incremental and iterative design changes. "Think of how engineers build bridges. They design quantitative models to help them understand what sorts of pressure and weight the bridge can withstand, and then use these equations to improve the actual physical model. [In our work on memory in yeast cells] we really did the same thing.” – Pam Silver, Harvard 2007 Engineering Design

11. Building Digital Circuits Intel 4004(1971) ~2000 gates Intel “Nehalem”(2008) ~2 billion gates

12. inputs outputs … … . . . digital circuit … Building Digital Circuits • Design is driven by the input/output specification. • CAD tools are not part of the design process; they are the design process.

13. Synthetic Biology Feats of synthetic bio-engineering: • Cellulosic ethanol (Nancy Ho, Purdue, ’04) • Anti-malarial drugs (Jay Keasling, UC Berkeley, ‘06) • Tumor detection (Chris Voigt, UCSF ‘06) Strategy: apply experimental expertise; formulate ad-hoc designs; perform extensive simulations.

14. From ad hoc to Systematic… “A Symbolic Analysis of Relay and Switching Circuits,”M.S. Thesis, MIT, 1937 “A Mathematical Theory of Communication,” Bell System Technical Journal,1948. Claude E. Shannon1916 –2001 Basis of all digital computation. Basis of information theory, coding theoryand all communication systems.

15. BiologicalProcess [computational]Synthetic Biology [computational] Analysis “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2004 Molecular Inputs Molecular Products Known /Unknown Given Known Unknown Unknown Known

16. Artificial Life Going from reading genetic codes to writing them. US Patent 20070122826 (pending):“The present invention relates to a minimal set of protein-coding genes which provides the information required for replication of a free-living organism in a rich bacterial culture medium.” – J. Craig Venter Institute

17. Artificial Life Going from reading genetic codes to write them. Moderator: “Some people have accused you of playing God.” J. Craig Venter:“Oh no, we’re not playing.

18. Biochemistry in a Nutshell Nucleotides: DNA: string of n nucleotides (n ≈ 109) ... ACCGTTGAATGACG... Amino acid: coded by a sequence of 3 nucleotides. Proteins: produced from a sequence of m amino acids (m ≈ 103).

19. The (nano) Structural Landscape “You see things; and you say ‘Why?’ But I dream things that never were; and I say ‘Why not?’" – George Bernard Shaw, 1925 Novel Materials… Novel biological functions… Novel biochemistry…

20. Jargon vs.Terminology “Now this end is called the thagomizer, after the late Thag Simmons.”

21. The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 Semiconductors:exponentially smaller, faster, cheaper – forever? 2000 transistors(Intel 4004, 1971) 800 million transistors(Intel Penryn, 2007) 1 transistor (1960’s)

22. The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 • Abutting true physical limits. • Cost and complexity are starting to overwhelm. Semiconductors:exponentially smaller, faster, cheaper – forever?

23. The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 • Multiple cores? • Parallel Computing? Potential Solutions:

24. c The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 • Novel Materials? Potential Solutions: ? • Novel Function?

25. output protein RNAp gene The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002

26. RNAp nada gene The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 repressor protein Biological computation?

27. Research Activities in my Lab • The concurrent logical and physical design of nanoscale digital circuitry. • The synthesis of stochastic logic for robust polynomial arithmetic. • Feedback in combinational circuits. • High-performance computing for the stochastic simulation of biochemical reactions. • The analysis and synthesis of stochasticity in biochemical systems. Our research activities encompass topics in logic synthesis and verification, as well as in synthetic and computational biology. A broad theme is the application of expertise from the realm of circuit design to the analysis and synthesis of biological systems. Current projects include: ?

29. Research Activities in my Lab • We’re studying the mathematical functions for digital circuits. • We’re writing computer programs to automatically design such circuits. Circuits Biology • We’re studying the concepts,mechanisms,anddynamicsof intracellular biochemistry. • We’re writing computer programs for analyzing and synthesizing these dynamics.

31. + Two Made-Up Facts [well, abstractions, really…] Logic Gates Biochemical Reactions

32. 0 0 0 1 1 0 1 1 Logic Gates “AND” gate 0 0 0 1

33. Logic Gates “XOR” gate 0 0 0 0 1 1 1 0 1 1 1 0

34. inputs outputs circuit Digital Circuit

35. inputs outputs circuit gate Digital Circuit

36. NAND OR AND AND NOR AND Digital Circuit 1 1 0 1 0 0 0 1 0 1 1 1

37. My PhD Dissertation [yes, in one slide…]

38. It’s not a bug, it’s a feature.

39. inputs outputs circuit Current Research Model defects, variations, uncertainty, etc.: 0 1 Characterize probability of outcomes.

40. inputs outputs circuit Current Research Model defects, variations, uncertainty, etc.: p1 = Prob(one) 0,1,1,0,1,0,1,1,0,1,… 1,0,0,0,1,0,0,0,0,0,… p2 = Prob(one)

41. inputs outputs circuit Current Research Model defects, variations, uncertainty, etc.:

43. + Biochemical Reactions cell protein count 9 8 6 5 7 9

44. + + + Biochemical Reactions slow medium fast

46. Design Scenario Bacteria are engineered to produce an anti-cancer drug: triggering compound drug E. Coli

47. Design Scenario Bacteria invade the cancerous tissue: cancerous tissue

48. Design Scenario The trigger elicits the bacteria to produce the drug: Bacteria invade the cancerous tissue: cancerous tissue

49. Design Scenario The trigger elicits the bacteria produce the drug: Problem: patient receives too high of a dose of the drug. cancerous tissue

50. Design Scenario Conceptual design problem. • Bacteria are all identical. • Population density is fixed. • Exposure to triggering compound is uniform. Constraints: Requirement: • Control quantity of drug that is produced.