Scouting enzyme behavior

Matsumaru, Naoki; Colombano, Silvano; Zauner, Klaus-Peter

doi:10.1109/cec.2002.1006203

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…Therefore to address this problem, we examine how human scientists may capture this tradeoff between exploration and exploitation. In related work the notion of experimenters reacting to surprising observations has been described [45], [7]. Where surprising observations should be investigated further to understand the surprise, then continue to explore the parameter space after the surprise is sufficiently understood.…”

Section: B Experiments Selectionmentioning

confidence: 99%

“…As discussed in Section II, NADH is an important part of enzymatic reactions and has been considered within previous work investigating enzymatic computation [2], [7]. Additionally NADH has the nice property that it has a nonlinear behaviour where part of the behaviour can be predicted using the Beer-Lambert law [47].…”

Section: Evaluating the Techniquementioning

confidence: 99%

“…1). Currently there are few autonomous experimentation systems available [6], [7], [8], however within the machine learning community there is increased research activity in developing algorithms capable of efficient active experimental design [9], [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enabling the discovery of computational characteristics of enzyme dynamics

Jones

Lovell

Gunn

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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Abstract-Biology demonstrates powerful information processing capabilities. Of particular interest are enzymes, which process information in highly complex dynamic environments. Exploring the information processing characteristics of an enzyme by selectively altering its environment may lead to the discovery of new modes of computation. The physical experiments required to perform such exploration are combinatorial in nature. Thus resource consumption, both time and money, poses major limiting factors on any exploratory work. New tools are required to mitigate these factors. One such tool is lab-on-chip based autonomous experimentation system, where a microfluidic experimentation platform is driven by machine learning algorithms. The lab-onchip approach provides an automated platform that can perform complex protocols, which is also capable of reducing the resource cost of experimentation. The machine learning algorithms provide intelligent experiment selection that reduces the number of experiments required for discovery. Here we discuss development of the experimentation platform and machine learning software that will lead to fully autonomous characterisation of enzymes.

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Section: B Experiments Selectionmentioning

confidence: 99%

Section: Evaluating the Techniquementioning

confidence: 99%

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Enabling the discovery of computational characteristics of enzyme dynamics

Jones

Lovell

Gunn

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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“…See text for details the next generation of the population in the evolutionary process will focus its attention on parameter combinations in the vicinity of the conditions that gave rise to the surprising experimental result. The scouting method has been applied in wet-lab experiments in conjunction with a computer controlled fluidics system to autonomously investigate the response of the enzyme malate dehydrogenase to ion signals [16]. In this domain the scouting algorithm focuses measurements on areas of the parameter space that show unusual phenomena.…”

Section: Autonomous Experimentationmentioning

confidence: 99%

Artificial Life as an Aid to Astrobiology: Testing Life Seeking Techniques

Centler

Dittrich

et al. 2003

Lecture Notes in Computer Science

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Abstract. Searching for signatures of fossil or present life in our solar system requires autonomous devices capable of investigating remote locations with limited assistance from earth. Here, we use an artificial chemistry model to create spatially complex chemical environments. An autonomous experimentation technique based on evolutionary computation is then employed to explore these environments with the aim of discovering the chemical signature of small patches of biota present in the simulation space. In the highly abstracted environment considered, autonomous experimentation achieves fair to good predictions for locations with biological activity. We believe that artificially generated biospheres will be an important tool for developing the algorithms key to the search for life on Mars.

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“…This work demonstrated that an algorithm can successfully navigate an immense search space by emulating the interplay of adjusting hypotheses and modifying experiments, which is characteristic of human experimenters [6]. Recently, evolutionary computation has been applied to autonomous experimentation [7] and was employed in conjunction with computer-controlled fluidics to characterize protein response with regard to chemical signals [8]. This method, named scouting, has also been suggested for detecting and localizing unusual chemical signatures [9].…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Scouting—Autonomous Experimentation for Systems Biology

Matsumaru

Centler

Zauner

et al. 2004

Lecture Notes in Computer Science

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Abstract. We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIVimmune system interaction. Finally, the difference between scouting and optimization is discussed.

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Scouting enzyme behavior

Cited by 8 publications

References 23 publications

Enabling the discovery of computational characteristics of enzyme dynamics

Enabling the discovery of computational characteristics of enzyme dynamics

Artificial Life as an Aid to Astrobiology: Testing Life Seeking Techniques

Self-Adaptive Scouting—Autonomous Experimentation for Systems Biology

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