Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
EE 5393: Circuits, Computation and Biology Marc D. Riedel Assistant Professor, ECE University of Minnesota x1 x2 AND x3 OR f1 AND f2 f3 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 is alien! “Minnesota Farmer” Who is this guy? He’s a human-bacteria hybrid: [like all of us] • 100 trillion bacterial cells of at least 500 different types inhabit his body. vs. • only 1 trillion human cells of 210 different types. “Minnesota Farmer” Who What’s is in this hisguy? gut? He’s a human-bacteria hybrid: [like all of us] • 100 trillion bacterial cells of at least 500 different types inhabit his body. vs. • only 1 trillion human cells of 210 different types. “Minnesota Farmer” 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 Bacterial Motor Bacterial Motor Electron Microscopic Image We should put these critters to work… “Stimulus, response! Stimulus response! Don’t you ever think!” 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 … Building Bridges "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 • Quantitative modeling. • Mathematical analysis. • Incremental and iterative design changes. Building Digital Circuits Intel 4004 (1971) ~2000 gates Intel “Nehalem” (2008) ~2 billion gates Building Digital Circuits inputs outputs x1 a f1 ( x1 , … , xm ) x2 … , xm ) a f 2 ( x1 , .. . xm digital circuit a f n ( x1 , … , xm ) • Design is driven by the input/output specification. • CAD tools are not part of the design process; they are the design process. 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. From ad hoc to Systematic… “A Symbolic Analysis of Relay and Switching Circuits,” “A Mathematical Theory of M.S. Thesis, MIT, 1937 Communication,” Bell System Technical Claude E. Shannon 1916 –2001 Journal, 1948. of all digital computation. BasisBasis of information theory, coding theory and all communication systems. [computational] [computational] Synthetic Analysis Biology “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2004 Molecular Inputs Known / Known Unknown Biological Process Molecular Products Given Unknown Unknown Known 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 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. Biochemistry in a Nutshell Nucleotides: { A, C , T , G} DNA: string of n nucleotides (n ≈ 109) ... ACCGTTGAATGACG... Amino acid: coded by a sequence of 3 nucleotides. { A, C , T , G }3 {a1 , , a20 } Proteins: produced from a sequence of m amino acids (m ≈ 103). {a1 , , a20 } m protein 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… Jargon vs.Terminology “Now this end is called the thagomizer, after the late Thag Simmons.” 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? 1 transistor (1960’s) 2000 transistors (Intel 4004, 1971) 800 million transistors (Intel Penryn, 2007) 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? • Abutting true physical limits. • Cost and complexity are starting to overwhelm. The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 Potential Solutions: • Multiple cores? • Parallel Computing? The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 Potential Solutions: • Novel Materials? • Novel Function? c a b ? The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 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 RNAp repressor protein gene Biological computation? nada Research Activities in my Lab 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: • • • • 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. Research Activities in my Lab Circuits • We’re studying the mathematical functions for digital circuits. • We’re writing computer programs to automatically design such circuits. Biology • We’re studying the concepts, mechanisms, and dynamics of intracellular biochemistry. • We’re writing computer programs for analyzing and synthesizing these dynamics. Two Made-Up Facts [well, abstractions, really…] Logic Gates x1 g x2 Biochemical Reactions + Logic Gates “AND” gate x1 g x2 x1 x2 g 0 0 0 0 1 0 1 0 0 1 1 1 Logic Gates “XOR” gate x1 g x2 x1 x2 g 0 0 0 0 1 1 1 0 1 1 1 0 Digital Circuit inputs outputs x1 a f1 ( x1 , , xm ) x2 a f 2 ( x1 , , xm ) a f n ( x1 , , xm ) xm circuit Digital Circuit inputs outputs x1 a f1 ( x1 , , xm ) x2 a f 2 ( x1 , , xm ) a f ( x1 , , xm ) a f n ( x1 , , xm ) xm gate circuit Digital Circuit x11 1 x02 NAND 1 x03 OR 0 x14 0 AND AND 0 x15 NOR 1 x16 AND My PhD Dissertation [yes, in one slide…] x3 x2 x1 x1 x1 x1 x2 x3 It’s not a bug, it’s a feature. Current Research Model defects, variations, uncertainty, etc.: inputs outputs 0 circuit 1 Characterize probability of outcomes. Current Research Model defects, variations, uncertainty, etc.: inputs outputs p1 = Prob(one) 0,1,1,0,1,0,1,1,0,1,… circuit 1,0,0,0,1,0,0,0,0,0,… p2 = Prob(one) Current Research Model defects, variations, uncertainty, etc.: inputs outputs 2 circuit 1 5 5 Biochemical Reactions + protein count 9 8 6 5 7 9 cell Biochemical Reactions slow + medium + fast + Design Scenario Bacteria are engineered to produce an anti-cancer drug: triggering compound drug E. Coli Design Scenario Bacteria invade the cancerous tissue: cancerous tissue Design Scenario The trigger Bacteria elicits invade the bacteria the cancerous to produce tissue: the drug: cancerous tissue Design Scenario The trigger the bacteria Problem: patientelicits receives too high produce of a dosethe of drug: the drug. cancerous tissue Design Scenario Conceptual design problem. Constraints: • Bacteria are all identical. • Population density is fixed. • Exposure to triggering compound is uniform. Requirement: • Control quantity of drug that is produced. Design Scenario Approach: elicit a fractional response. cancerous tissue Synthesizing Stochasticity Approach: engineer a probabilistic response in each bacterium. produce drug with Prob. 0.3 triggering compound E. Coli don’t produce drug with Prob. 0.7 Engineering vs. Biology vs. Mathematics Dilbert Beaker Papa Communicating Ideas Domains of Expertise • • • • Vision Language Abstract Reasoning Farming Circuit Human • Number Crunching • Mining Data • Iterative Calculations Astonishing Hypothesis “A person's mental activities are entirely due to the behavior of nerve cells, glial cells, and the atoms, ions, and molecules that make them up and influence them.” – Francis Crick, 1982 The Astonishing Part “That the astonishing hypothesis is astonishing.” – Christophe Koch, 1995 Circuits & Computers as a Window into our Linguistic Brains Brain Circuit Conceives of circuits and computation by “applying” language. ? Lousy at all the tasks that the brain that designed it is good at (including language). If You Don’t Know the Answer…