Marc D. Riedel Associate Professor, ECE University of Minnesota EE 5393: Circuits, Computation and Biology ORAND.

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Presentation transcript:

Marc D. Riedel Associate Professor, ECE University of Minnesota EE 5393: Circuits, Computation and Biology ORAND

[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 Biological Process Molecular Inputs Molecular Products Known Unknown Known / Unknown Unknown Given

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

Biochemistry in a Nutshell DNA: string of n nucleotides ( n ≈ 10 9 )... ACCGTTGAATGACG... Nucleotides: Amino acid: coded by a sequence of 3 nucleotides. Proteins: produced from a sequence of m amino acids ( m ≈ 10 3 ) called a “gene”. … …

Biochemical Reactions: rules specifying how types of molecules combine a2a b c Design Abstraction

The rate at which a given reaction fires is proportional to: Its rate constant. The concentration of its reactants.` Mass Action Kinetics k1k1 k2k2 k3k3

DNA Strand Displacement X1X1 X2X2 X3X3 + D. Soloveichik et al: “DNA as a Universal Substrate for Chemical Kinetics.” PNAS, Mar 2010

DNA Strand Displacement X1X1 X3X3 X2X2 + D. Soloveichik et al: “DNA as a Universal Substrate for Chemical Kinetics.” PNAS, Mar 2010

Molecular Reactions [computational] Biochemistry y x quantities z Biochemical [computation] quantity

Inversion Produce a quantity of a type only in the absence of another type.

Duplication Produce a quantity of a type equal to the quantity of another type:

Multiplication pseudo-code biochemical code

Moving Average Filter (improved) Signal transfer Computation Absence indicator

Output obtained by ODE simulations at the DNA level. Simulation Results: Moving Average Filter input: X output: Y Time (Hours) Concentration (nM)

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

Bacteria invade the cancerous tissue: cancerous tissue Design Scenario

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

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

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

cancerous tissue Approach: elicit a fractional response. Design Scenario

produce drug triggering compound E. Coli Approach: engineer a probabilistic response in each bacterium. with Prob. 0.3 don’t produce drug with Prob. 0.7 Synthesizing Stochasticity

Linear Threshold Gates 1 x 2 x n x 1 w 2 w n w 0 w...

Linear Threshold Gates Useful Model?

Computing With Limited Memory Examine a specific input bit. Based on current state, lookup next state. Each instruction: ( log 2 m bits memory) m states n Boolean inputs Assume n much greater than m