Limits...
Use of the University of Minnesota Biocatalysis/Biodegradation Database for study of microbial degradation.

Ellis LB, Wackett LP - Microb Inform Exp (2012)

Bottom Line: UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking.The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism.The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA. lynda@umn.edu.

ABSTRACT
Microorganisms are ubiquitous on earth and have diverse metabolic transformative capabilities important for environmental biodegradation of chemicals that helps maintain ecosystem and human health. Microbial biodegradative metabolism is the main focus of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD). UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking. The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism. The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

No MeSH data available.


Different features comprising metabolic logic in the UM-PPS: A) Each biotransformation rule has an associated, color-coded aerobic likelihood assigned by content experts; B) Relative reasoning is applied when a biotransformation rule take precedence over another; C) Super rules combine metabolic steps when warranted, based on existing biodegradation knowledge; D) Variable aerobic likelihood is used to distinguish the likelihood of rules based on structural characteristics of the compound. For more information on the UM-PPS, see http://umbbd.msi.umn.edu/predict/aboutPPS.html.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3351732&req=5

Figure 3: Different features comprising metabolic logic in the UM-PPS: A) Each biotransformation rule has an associated, color-coded aerobic likelihood assigned by content experts; B) Relative reasoning is applied when a biotransformation rule take precedence over another; C) Super rules combine metabolic steps when warranted, based on existing biodegradation knowledge; D) Variable aerobic likelihood is used to distinguish the likelihood of rules based on structural characteristics of the compound. For more information on the UM-PPS, see http://umbbd.msi.umn.edu/predict/aboutPPS.html.

Mentions: Each rule is ranked with respect to its likelihood for occurring in aerobic biodegradation (Figure 3A). In this ranking system, the reactions governed by rules bt003 and bt0026 are considered to be "likely" or "very likely," respectively. During a prediction cycle, each compound submitted to the UM-PPS is examined for the organic functional groups that it contains, and these functional groups are matched to the appropriate UM-PPS rules. There are presently 250 btrules in the system; this number and the individual rules are periodically updated.


Use of the University of Minnesota Biocatalysis/Biodegradation Database for study of microbial degradation.

Ellis LB, Wackett LP - Microb Inform Exp (2012)

Different features comprising metabolic logic in the UM-PPS: A) Each biotransformation rule has an associated, color-coded aerobic likelihood assigned by content experts; B) Relative reasoning is applied when a biotransformation rule take precedence over another; C) Super rules combine metabolic steps when warranted, based on existing biodegradation knowledge; D) Variable aerobic likelihood is used to distinguish the likelihood of rules based on structural characteristics of the compound. For more information on the UM-PPS, see http://umbbd.msi.umn.edu/predict/aboutPPS.html.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351732&req=5

Figure 3: Different features comprising metabolic logic in the UM-PPS: A) Each biotransformation rule has an associated, color-coded aerobic likelihood assigned by content experts; B) Relative reasoning is applied when a biotransformation rule take precedence over another; C) Super rules combine metabolic steps when warranted, based on existing biodegradation knowledge; D) Variable aerobic likelihood is used to distinguish the likelihood of rules based on structural characteristics of the compound. For more information on the UM-PPS, see http://umbbd.msi.umn.edu/predict/aboutPPS.html.
Mentions: Each rule is ranked with respect to its likelihood for occurring in aerobic biodegradation (Figure 3A). In this ranking system, the reactions governed by rules bt003 and bt0026 are considered to be "likely" or "very likely," respectively. During a prediction cycle, each compound submitted to the UM-PPS is examined for the organic functional groups that it contains, and these functional groups are matched to the appropriate UM-PPS rules. There are presently 250 btrules in the system; this number and the individual rules are periodically updated.

Bottom Line: UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking.The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism.The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA. lynda@umn.edu.

ABSTRACT
Microorganisms are ubiquitous on earth and have diverse metabolic transformative capabilities important for environmental biodegradation of chemicals that helps maintain ecosystem and human health. Microbial biodegradative metabolism is the main focus of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD). UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking. The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism. The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

No MeSH data available.