Computational systems biology and dose-response modeling in relation to new directions in toxicity testing

Qiang Zhang, Sudin Bhattacharya, Melvin E. Andersen, Rory B. Conolly

Research output: Research - peer-reviewArticle

  • 34 Citations

Abstract

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of network motifsthe basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

LanguageEnglish (US)
Pages253-276
Number of pages24
JournalJournal of Toxicology and Environmental Health - Part B: Critical Reviews
Volume13
Issue number2-4
DOIs
StatePublished - Feb 2010
Externally publishedYes

Profile

Systems Biology
Biological Science Disciplines
Computational Biology
Computer Simulation
Direction compound
Toxicity
Testing
Intuition
Systems Theory
Nonlinear Dynamics
Mathematics
Periodicity
Systems Analysis
Curriculum
Cell Biology
Homeostasis
Education
In Vitro Techniques
Dynamical systems
Throughput

ASJC Scopus subject areas

  • Health, Toxicology and Mutagenesis
  • Toxicology

Cite this

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing. / Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E.; Conolly, Rory B.

In: Journal of Toxicology and Environmental Health - Part B: Critical Reviews, Vol. 13, No. 2-4, 02.2010, p. 253-276.

Research output: Research - peer-reviewArticle

@article{7d28ebd1e9244dd797b78e5684dfc95d,
title = "Computational systems biology and dose-response modeling in relation to new directions in toxicity testing",
abstract = "The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of network motifsthe basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.",
author = "Qiang Zhang and Sudin Bhattacharya and Andersen, {Melvin E.} and Conolly, {Rory B.}",
year = "2010",
month = "2",
doi = "10.1080/10937404.2010.483943",
volume = "13",
pages = "253--276",
journal = "Journal of Toxicology and Environmental Health - Part B: Critical Reviews",
issn = "1093-7404",
publisher = "Taylor and Francis Ltd.",
number = "2-4",

}

TY - JOUR

T1 - Computational systems biology and dose-response modeling in relation to new directions in toxicity testing

AU - Zhang,Qiang

AU - Bhattacharya,Sudin

AU - Andersen,Melvin E.

AU - Conolly,Rory B.

PY - 2010/2

Y1 - 2010/2

N2 - The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of network motifsthe basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

AB - The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of network motifsthe basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

UR - http://www.scopus.com/inward/record.url?scp=77953964154&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953964154&partnerID=8YFLogxK

U2 - 10.1080/10937404.2010.483943

DO - 10.1080/10937404.2010.483943

M3 - Article

VL - 13

SP - 253

EP - 276

JO - Journal of Toxicology and Environmental Health - Part B: Critical Reviews

T2 - Journal of Toxicology and Environmental Health - Part B: Critical Reviews

JF - Journal of Toxicology and Environmental Health - Part B: Critical Reviews

SN - 1093-7404

IS - 2-4

ER -