2 Background, Tasks, Modeling Methods …
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As for individual behavior, when exposed to irritating chemicals, a person would
certainly response to or evade from those harmful stressors. In 2016, a new field
termed “computational exposure science” has been coined to specifically simulate
the exposure of all types of stressors of concern to risk assessment regulators, where
nonlinear relationships between all objects under a context of exposome [40] and
exposure ontology have been emphasized [41]. Further incorporation of this computational exposure science might help computational toxicologists to have a more
realistic framework for modeling the exposure of chemicals.
2.3.3 Systems Toxicology Models
As mentioned above, during the absorption, distribution, metabolism, and excretion,
i.e., toxicokinetics by the organism, xenobiotic chemicals would also exert their influence on the organism after they reach certain targets, which results in toxicodynamics
of chemicals. In this sense, the target sites can be regarded as interfaces linking the
xenobiotic chemicals and the physiological functions of life systems.
Homeostasis, proliferation, differentiation, and apoptosis of cells, the basic functional units for life systems, are regulated by cellular signaling pathways/network.
Nowadays, bioinformatic and systems toxicology employ network models to map
the cellular biochemical components such as an upstream DNA sequences and its
downstream mRNA, by analyzing data from molecular biology technologies, especially genomics, transcriptomics, proteomics, etc. [42]. When xenobiotic chemicals
are tested within these -omics assays and certain toxic effects are focused on, the
xenobiotics could be thus anchored onto the biological network and linked with phenotypical in vitro or in vivo end points or diseases [43, 44]. The chemo-bioinformatics
and -omics assays would be an efficient strategy for selecting more relevant targets/marker interfaces for xenobiotics to exert their influences from a large in vitro
test battery [45, 46].
Qualitative signaling networks delineated by chemo-bioinformatics are indeed
informative. However, they still cannot quantitatively describe the toxicodynamics or
the dose–response curve of tested chemicals. Borrowing concepts from cybernetics,
general network motifs, e.g., negative/positive feedback loop, feed-forward loop, etc.,
have been extracted from the biochemical components of cells [47]. Furthermore,
these motifs can be composed as functional modules, e.g., hypersensitivity, periodical
oscillation, cellular memory, etc., which serve as in silico situational objects for
simulating the dynamics of the cellular signaling networks. These models termed as
computational systems biology pathway (CSBP) models, could give results that can
be compared with those of associated in vitro tests, becoming a promising tool for
chemicals risk assessment [47]. For example, a feed-forward loop can explain the
hormesis-shape dose–response curves of the phase I and phase II metabolism with
relatively low exposed doses of xenobiotic chemicals [48]. A CSBP model was also
applied successfully to describe the anti-oxidative stressors responses regulated by
nuclear factor erythroid 2-related factor 2 (Nrf2) [49].
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As for individual behavior, when exposed to irritating chemicals, a person would
certainly response to or evade from those harmful stressors. In 2016, a new field
termed “computational exposure science” has been coined to specifically simulate
the exposure of all types of stressors of concern to risk assessment regulators, where
nonlinear relationships between all objects under a context of exposome [40] and
exposure ontology have been emphasized [41]. Further incorporation of this computational exposure science might help computational toxicologists to have a more
realistic framework for modeling the exposure of chemicals.
2.3.3 Systems Toxicology Models
As mentioned above, during the absorption, distribution, metabolism, and excretion,
i.e., toxicokinetics by the organism, xenobiotic chemicals would also exert their influence on the organism after they reach certain targets, which results in toxicodynamics
of chemicals. In this sense, the target sites can be regarded as interfaces linking the
xenobiotic chemicals and the physiological functions of life systems.
Homeostasis, proliferation, differentiation, and apoptosis of cells, the basic functional units for life systems, are regulated by cellular signaling pathways/network.
Nowadays, bioinformatic and systems toxicology employ network models to map
the cellular biochemical components such as an upstream DNA sequences and its
downstream mRNA, by analyzing data from molecular biology technologies, especially genomics, transcriptomics, proteomics, etc. [42]. When xenobiotic chemicals
are tested within these -omics assays and certain toxic effects are focused on, the
xenobiotics could be thus anchored onto the biological network and linked with phenotypical in vitro or in vivo end points or diseases [43, 44]. The chemo-bioinformatics
and -omics assays would be an efficient strategy for selecting more relevant targets/marker interfaces for xenobiotics to exert their influences from a large in vitro
test battery [45, 46].
Qualitative signaling networks delineated by chemo-bioinformatics are indeed
informative. However, they still cannot quantitatively describe the toxicodynamics or
the dose–response curve of tested chemicals. Borrowing concepts from cybernetics,
general network motifs, e.g., negative/positive feedback loop, feed-forward loop, etc.,
have been extracted from the biochemical components of cells [47]. Furthermore,
these motifs can be composed as functional modules, e.g., hypersensitivity, periodical
oscillation, cellular memory, etc., which serve as in silico situational objects for
simulating the dynamics of the cellular signaling networks. These models termed as
computational systems biology pathway (CSBP) models, could give results that can
be compared with those of associated in vitro tests, becoming a promising tool for
chemicals risk assessment [47]. For example, a feed-forward loop can explain the
hormesis-shape dose–response curves of the phase I and phase II metabolism with
relatively low exposed doses of xenobiotic chemicals [48]. A CSBP model was also
applied successfully to describe the anti-oxidative stressors responses regulated by
nuclear factor erythroid 2-related factor 2 (Nrf2) [49].