EE 5393: Circuits, Computation and Biology Marc D. Riedel Assistant Professor, ECE University of Minnesota Thanks to Bell Museum. Thanks to the Bryant-Lake Bowl. Thanks to all of you for coming out. Friendly faces. OR AND

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! Welcome to Cost-to-Coast AM with Art Bell. “Minnesota Farmer”

Who is this guy? vs. 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 is this guy? What’s in his gut? vs. He’s a human-bacteria hybrid: [like all of us] 100 trillion bacterial cells of at least 500 different types inhabit his body. vs. What specifically is in his gut? 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 – Pam Silver, Harvard 2007 Engineering Design "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 digital circuit … . In my line of work, we are not designing programs to design digital circuits; we are design programs to design the programs to design the circuits (without begin facetious). 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,” M.S. Thesis, MIT, 1937 “A Mathematical Theory of Communication,” Bell System Technical Journal, 1948. Claude E. Shannon 1916 –2001 Basis of all digital computation. Basis of information theory, coding theory and all communication systems.

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

Going from reading genetic codes to writing them. 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

Going from reading genetic codes to write them. 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: 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).

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… Heady Times. Novel Materials: Carbon nanotubes. Protein chemistry. Synthetic biology. (Yes, folks, synthetic life has arrived!) Novel biochemistry…

“Now this end is called the thagomizer, after the late Thag Simmons.” 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? Heady Times. 2000 transistors (Intel 4004, 1971) 800 million transistors (Intel Penryn, 2007) 1 transistor (1960’s)

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. Heady Times.

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? Intel blindsided the industry a few years ago with the announcement of multiple cores. The assumption: provide the hardware and the software will follow. Blue Gene: 64,000 processors. The history of computing is littered with sweeping predictions and attempts at implementing parallelization. Luminaries and startups (Thinking Machines, Cray Supercomputers). No one has really been able to crack the problem. Amhdals’ law.

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? ? c Provide the functionality and the “digital designers” will figure out how to use it. Novel function: apply computing in different ways. Novel Function?

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 output protein 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 nada repressor protein gene Biological computation?

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.

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

Logic Gates “AND” gate 1 Generally thought of as acyclic structures (often defined as a DAG). Once input values are applied, values propagate to the outputs. Outputs assume definite boolean values regardless of the initial values on the wires and independently of all timing assumptions. 1

Logic Gates “XOR” gate 1 1 1 1 Generally thought of as acyclic structures (often defined as a DAG). Once input values are applied, values propagate to the outputs. Outputs assume definite boolean values regardless of the initial values on the wires and independently of all timing assumptions. 1 1

Digital Circuit inputs outputs circuit Realm of digital design is mature: Combinational Circuit: Circuit does not have any memory, or any internal state. Performs a mapping from boolean inputs, to boolean outputs.

Digital Circuit inputs outputs gate circuit Combinational Circuit: Circuit does not have any memory, or any internal state. Performs a mapping from boolean inputs, to boolean outputs.

Digital Circuit 1 1 1 1 1 1 1 NAND OR AND NOR 1 1 An acyclic circuit is clearly combinational. If a circuit has no feedback paths resulting in cycles, that is to say if its topology is feed-forward, then its behavior is readily understood: <click> When input values are applied, these propagate forward to the outputs. The outcome is determine regardless of the prior values on the wires; clearly such a circuit does not preserve any state information so it is combinational. 1 1 1

My PhD Dissertation [yes, in one slide…] An acyclic circuit is clearly combinational. If a circuit has no feedback paths resulting in cycles, that is to say if its topology is feed-forward, then its behavior is readily understood: <click> When input values are applied, these propagate forward to the outputs. The outcome is determine regardless of the prior values on the wires; clearly such a circuit does not preserve any state information so it is combinational.

It’s not a bug, it’s a feature.

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

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

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

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

Biochemical Reactions slow + medium + fast +

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

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

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

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

Conceptual design problem. Design Scenario Conceptual design problem. Constraints: Bacteria are all identical. Population density is fixed. Exposure to triggering compound is uniform. No possible solution, it would seem. Bacteria are identical. Everything is fixed. Requirement: Control quantity of drug that is produced.

Approach: elicit a fractional response. Design Scenario Approach: elicit a fractional response. cancerous tissue

Synthesizing Stochasticity Approach: engineer a probabilistic response in each bacterium. E. Coli produce drug with Prob. 0.3 triggering compound Bacterial are identical by design. But this design could allow for a probabilistic response. don’t produce drug with Prob. 0.7

Engineering vs. Biology vs. Mathematics Papa Dilbert Beaker

Communicating Ideas

Domains of Expertise Vision Language Abstract Reasoning Farming Circuit Number Crunching Mining Data Iterative Calculations Human

“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 Astonishing Hypothesis “That the astonishing hypothesis is astonishing.” – Christophe Koch, 1995 The Astonishing Part

Circuits & Computers as a Window into our Linguistic Brains Conceives of circuits and computation by “applying” language. ? Lousy at all the tasks that the brain that designed it is good at (including language).

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