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Reverse Engineering And Automatic Synthesis Of Metabolic Pathways From Observed Data Using Genetic Programming
 Paci Symposium on Biocomputing , 6
, 2001
"... This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed timedomain data. Genetic programming starts with observed timedomain concentrations of input substances and automatically creates both the topology of the networ ..."
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Cited by 20 (4 self)
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This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed timedomain data. Genetic programming starts with observed timedomain concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the concentration of the final product of the automatically created network matches the observed timedomain data. This paper describes how genetic programming automatically created a metabolic pathway involving four chemical reactions that takes in glycerol and fatty acid as input, uses ATP as a cofactor, and produces diacylglycerol as its final product. In addition, this paper describes how genetic programming similarly created a metabolic pathway involving three chemical reactions for the synthesis and degradation of ketone bodies. Both automatically created metabolic pathways contain at least one instance of three noteworthy topological features, namely an internal feedback loop, a bifurcation point where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources
Automated synthesis of computational circuits using genetic programming
 Proceedings of the 1997 IEEE Conference on Evolutionary Computation. Piscataway, NJ
, 1997
"... Abstract: Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into ..."
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Cited by 11 (4 self)
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Abstract: Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into a digital signal, performing the mathematical function in the digital domain, and then converting the result back to the analog domain. The design of computational circuits is difficult even for mundane mathematical functions and often relies on the clever exploitation of some aspect of the underlying device physics of the components. Moreover, implementation of each different mathematical function typically requires an entirely different clever insight. This paper demonstrates that computational circuits can be designed without such problemspecific insights using a single uniform approach involving genetic programming. Both the circuit topology and the sizing of all circuit components are created by genetic programming. This uniform approach to the automated synthesis of computational circuits is illustrated by evolving circuits that perform the cube root function (for which no circuit was found in the published literature) as well as for the square root, square, and cube functions. 1.
Iterative Refinement of Computational Circuits Using Genetic Programming
 IEEE POSIX. IEEE POSIX 1003.1c Threads API
, 2002
"... Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits ..."
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Cited by 3 (0 self)
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Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits whose output is an approximation to the error function associated with an existing computational circuit (created by means of genetic programming or some other method). The output of the evolved circuit can then be added to the output of the existing circuit to produce a circuit that computes the desired function with greater accuracy. This process can be performed iteratively. We present a set of results showing the effectiveness of this approach over multiple iterations for generating squaring, square root, and cubing computational circuits. We also perform iterative refinement on a recently patented cubic signal generator circuit, obtaining a refined circuit that is 7.2 times more accurate than the original patented circuit. The iterative refinement process described herein can be viewed as a method for using previous knowledge (i.e. the existing circuit) to obtain an improved result.
Use of TimeDomain Simulations in Automatic Synthesis of Computational Circuits Using Genetic Programming
 Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference. Las Vegas, NV
, 2000
"... Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does ..."
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Cited by 1 (1 self)
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Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does not necessarily lead to robust circuits that correctly perform the desired mathematical function over time. This paper addresses the problem of automatically synthesizing computational circuits using multiple timedomain simulations and presents results involving the synthesis of both the topology and sizing for a squaring, square root, and multiplier computational circuit and a lag circuit (from the field of control).