assessing the potential risk that a substance poses to human health 
 or the environment involves a combined quantitative analysis of both 
 exposure and hazard , including the estimation of associated uncertainties . 
 for decades
, the evaluation of toxicological hazards in the context 
 of regulatory risk assessment has relied heavily on animal experimentation , 
 often described in internationally recognized test guidelines ( e.g. , 
 oecd ) .
animal experiments are typically designed either to indicate 
 the dose at which substance - induced pathological effects , other than 
 cancer , are first observed after a defined treatment duration ( e.g. , 
 lowest observed adverse effect level , loael ) or to infer the dose 
 below which no effect is expected to occur ( e.g. , no observed adverse 
 effect level , noael ) .
then , through a process of extrapolation using 
 empirically derived uncertainty factors , the assessor can arrive at 
 various hazard reference values ( e.g. , a derived no effect level , 
 dnel ) useful for an ecological or human health risk assessment . throughout 
 this process
, information about the mechanistic basis of the observed 
 adverse effects is often considered nice to have rather than necessary 
 to have . despite its shortcomings , over the years this traditional 
 approach to toxicity assessment has been generally considered as offering 
 an acceptable level of protection to our health and the environment . 
 however , we are arguably positioned to shift toward a new paradigm 
 for toxicity assessment because of the limitations of this process 
 in addressing increasing demands imposed by modern society together 
 with the opportunities offered by considerable developments in biomedical 
 research tools to do better .
the possibilities of improved safety 
 from applying systems toxicology ( figure 1 ) 
 approaches provide a scientific impetus to bring about change .
systems toxicology is aimed at decoding 
 the toxicological blueprint of active substances that interact with 
 living systems .
it integrates classic toxicology approaches 
 with network models and quantitative measurements of molecular and 
 functional changes occurring across multiple levels of biological 
 organization .
the multidisciplinary systems toxicology approach combines 
 principles of chemistry , computer science , engineering , mathematics , 
 and physics with high - content experimental data obtained at the molecular , 
 cellular , organ , organism , and population levels to characterize and 
 evaluate interactions between potential hazards and the components 
 of a biological system .
it is aimed at developing a detailed mechanistic 
 as well as quantitative and dynamic understanding of toxicological 
 processes , permitting prediction and accurate simulation of complex 
 ( emergent ) adverse outcomes .
thereby , the approach provides a basis 
 for translation between model systems ( in vivo and in vitro ) and study systems ( e.g. , human , ecosystem ) .
systems 
 toxicology , therefore , has an ultimate potential for extrapolating 
 from early and highly sensitive quantifiable molecular and cellular 
 events to medium- and long - term outcomes at the organism level , and 
 its application could be part of a new paradigm for risk assessment . 

there are numerous regulatory drivers promoting the development 
 and application of innovative methodology for risk assessment , many 
 of which have particular emphasis on alternative in vitro and computational methods that avoid or limit the use of animals .
since 1986 , the european union ( eu ) has had legislation 
 in place covering the use of animals for scientific purposes , in particular 
 directive 2010/63/eu , which came into 
 full effect in january 2013 , restricting the use of animals for assessing 
 the toxicological properties of substances for either regulatory or 
 research purposes to cases where it can be clearly demonstrated that 
 no alternative method is available .
the eu cosmetics regulation bans the marketing of cosmetics products in the 
 eu that have had any ingredient tested on an animal since 11 march 
 2013 , irrespective of the fact that full replacement methods are not 
 yet available for complex health effects such as chronic systemic 
 toxicity , reproductive toxicity , and cancer .
legislation dealing with 
 risk management in other sectors , such as the registration , evaluation , 
 authorization and restriction of chemicals ( reach ) , the plant protection products regulation , and the biocides regulation , still allows the use of animal testing to satisfy information requirements ; 
 however , the use of alternative methods is strongly encouraged and , 
 in some cases , obligatory if available and accepted by regulatory 
 authorities .
thus , never before has there been such demand for animal - free 
 solutions to toxicological testing and assessments that are fit for 
 the purpose of regulatory decision making . considering the diversity 
 of chemical exposures under real - world 
 conditions , there is currently no mechanism within the framework of 
 eu legislation to facilitate the systematic , comprehensive , and integrated 
 assessment of the combined effects of chemicals resulting from different 
 routes and times of exposure .
although risk assessment for mixtures 
 is typically driven by exposure considerations , a key question is 
 whether certain combinations of chemicals have independent , additive , 
 or even synergistic toxic effects .
however , mechanism - based mixture 
 toxicology is severely hindered because the mechanism(s ) or mode(s ) 
 of action of most chemicals is unknown or poorly described , there 
 are no established criteria for characterizing mechanisms or modes 
 of action , and there is no agreed terminology or formal description 
 thereof .
in addition , although assessing 
 the combined action of co - acting chemicals may be practically possible 
 when they have the same basic mechanism of action ( e.g. , act on the 
 same molecular target ) , the problem becomes much more difficult when 
 considering chemicals that impact the same biological system but act 
 through different mechanisms .
these issues have emerged prominently , 
 for example , in the contemporary dialogue regarding endocrine - disrupting 
 chemicals ( edcs ) because there is much debate regarding what constitutes 
 the endocrine system and criteria to identify edcs .
tackling issues 
 such as non - monotonic dose  response and the existence of thresholds 
 is severely hampered by an over - reliance on empirical evidence derived 
 primarily from animal experiments , a lack of emphasis on understanding 
 the fundamentals of endocrine system dynamics , and the limitations 
 of analytical and computational tools suitable to characterize and 
 model the problem at hand . in the context of chemical risk assessment
, 
 the application of 
 the new tools of experimental and computational systems toxicology 
 is anticipated to occur first for the large numbers of environmental 
 chemicals that have little or no toxicological data in the public 
 domain . in this manner
, there can be a prioritization of chemicals 
 to evaluate for endocrine - disrupting activity ( see http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf ) .
toxic substances control act ( tsca ) ( see http://www.epa.gov/agriculture/lsca.html ) once the methodologies are capable of deciphering modes of action 
 beyond those related to the endocrine system . for drug development , 

motivators for the application of systems tools include identification 
 and characterization of disease - related therapeutic targets and uncovering 
 chemical liabilities early in the drug - discovery pipeline , thus avoiding 
 costly failures once a candidate molecule reaches clinical trials .
drug safety objectives are being addressed , 
 for example , by a large public 
private partnership , the etox 
 project in the european union ( see http://www.etoxproject.eu ) , under the innovative medicines 
 initiative ( see http://www.imi.europa.eu/ ) .
the aim is to develop a drug safety database for the pharmaceutical 
 industry and novel tools to predict better the toxicological profiles 
 of molecules in early stages of the drug - development pipeline .
another 
 application on the horizon is the assessment of alternative chemicals 
 designed and produced within the framework of green or sustainable 
 chemistry .
the aim in this case is to minimize the use and generation 
 of hazardous substances . without an understanding of the toxicological 
 liability of proposed alternatives ,
the push for greener chemicals 
 is limited to using chemical engineering tools to reduce the use of 
 known hazardous precursors , maximize atom economy in synthetic processes , 
 and increase the energy efficiency of reactions .
national 
 academy of science has recently undertaken a project to provide a 
 framework for evaluating safer substitutes ( see studies in progress 
 at http://www8.nationalacademies.org/cp/projectview.aspx?key=49569 ) , including , in particular , systems toxicology .
together with regulatory 
 drivers , an overarching expectation is that systems toxicology approaches , 
 applied in a systematic and rigorous manner , will provide more sound 
 information on which to judge how chemicals cause biological perturbations , 
 moving knowledge beyond knowing only what phenotypes are altered . 

the resulting understanding of biological responses to chemical exposure 
 and biomolecular interactions is expected to reduce uncertainties 
 in species extrapolations , in high - to - low dose extrapolation , and 
 to shed light on life stage and other susceptibility factors such 
 as genetics and pre - existing diseases . when paired with advances 
 in hazard detection and biomonitoring 
 strategies , systems toxicology holds 
 the long - term promise , at least in part , to address these challenges . 

it will enable the gradual shift from toxicological assessment using 
 solely apical end points toward understanding the biological pathways 
 perturbed by active substances .
systems toxicology is expected , therefore , 
 to create knowledge regarding both the dynamic interactions between 
 biomolecular components of a complex biological system and how perturbing 
 these interactions with active substances alters homeostasis and leads 
 to adverse reactions and disease .
this knowledge , represented as adverse 
 outcome pathways ( aops ) , is then expected 
 to enable our system - level understanding of exposure to active substances 
 and to lead to more effective risk assessment
. systems toxicology 
 is rooted in the premise that morphological 
 and functional changes in cellular , tissue , organ , and organism levels 
 are caused by , and cause , changes at the genomic , proteomic , and metabolomic 
 level .
a molecular system can be represented initially by high - level 
 static biological network ( bn ) models that employ basic graph representations 
 to map the molecular entities ( nodes ) and their interactions ( edges ) . 
 although these basic bn models serve to map the underlying molecular 
 wiring of a biological system , they are too simplistic in nature to 
 describe a toxicological pathway and its dynamic nature .
the description 
 of a toxicological pathway requires the development of more sophisticated 
 computable bns by incorporating mechanistic information through the 
 encoding of causal relationships between the nodes .
such models enable 
 the computational analysis and contextualization of experimental data , 
 for instance , for the quantification of network perturbation , which 
 is further described in this perspective .
ultimately , the description 
 of dynamic toxicological processes requires the development of even 
 more sophisticated executable bn models by incorporating the mathematical 
 description of the dynamic behavior of these causal relationships . 

such bns are capable of simulating changes in state of the molecular 
 network caused by external perturbation or modification of boundary 
 conditions . to be useful in current risk - assessment frameworks , however , 
 mathematical models need to be multiscale or able to traverse the 
 different levels of biological organization ultimately to relate the 
 dynamics at the bn level to apical adverse outcomes on which regulations 
 are based .
the most direct causal path , traversing multiple levels 
 of organization and linking exposure , binding of the toxicants to 
 biomolecules , intermediate key events , and apical adverse outcome , 
 is what characterizes aops ( figure 2 ) .
steps that define the systems toxicology paradigm , from 
 biological 
 network models to dynamic adverse outcome pathway ( aop ) models .
the 
 development of dynamic aop models enabling the simulation of the population - level 
 effects of an exposure is the ultimate goal of systems toxicology . 

the first level consists of the development of causal computable 
 biological network models that link the system s interaction 
 of a toxicant with the organ - level responses .
such models can be used 
 to quantify the biological impact of an exposure in the context of 
 quantifiable end points such as histology or physiological measurements . 
 in a second step ,
as more mechanistic knowledge derived from quantitative 
 measurements accumulates , dynamic models linking the exposure with 
 the organ - level responses can be developed .
ultimately , the third 
 level of maturity is reached when the link between the exposure and 
 the population outcome can be represented by mathematical models that 
 enable the simulation of population - level effects of an exposure . 

aops are tools for describing 
 the entire toxicological process 
 from the molecular level , triggered by an active substance , to an 
 adverse outcome of regulatory concern , typically observed at a tissue , 
 organ , organism , and potentially population level . although not traditionally 
 associated with systems toxicology , an aop is , in fact , a model that 
 discretizes a process into key events ( ke ) ( nodes ) and the causal 
 relationship between them ( edges ) .
an aop is developed first on the basis of a qualitative model that captures 
 kes and their high - level relationships .
gradually , as detailed mechanistic 
 knowledge accumulates , they will capture the causal relationships 
 between the kes and the underlying molecular events .
for example , such aops 
 can be used to map quantitative information for visualization purposes 
 or to compare and identify common features and intersections between 
 aops .
as aops are developed to include more detailed causal and quantitative 
 information , they are expected to become computable and eventually 
 executable , making them quantitative , sensitive , and predictive of 
 outcome for risk assessment .
the implementation and practice 
 of systems toxicology requires 
 several experimental approaches to generate data relevant to the question 
 at hand .
first , the exposure needs to be characterized both in terms 
 of composition , dose , and duration and in terms of biomarkers of exposure 
 at the subject level .
the next step is to examine the effect of these 
 exposures in biological model systems such as cellular and organotypic 
 cultures relevant to the exposure and the observed toxicological effects . 

the results obtained from these in vitro experiments 
 can then further guide the selection of relevant models of human diseases 
 and environmental health .
all of these studies will , however , yield 
 quantitative phenotypic readouts in the context of quantitative system - wide 
 molecular measurements .
the third step is to integrate the data from 
 these large - scale experiments , to analyze them to decipher the biological 
 mechanisms by which adverse effects arise from exposure , and to build 
 a functional model thereof . a new framework for risk assessment should 
 therefore integrate this mechanistic knowledge .
this perspective , 
 derived from the presentations and discussions held at a recent symposium 
 on systems toxicology , offers an integrated 
 view on this trajectory from experimentation to computational analysis 
 and risk assessment .
xenobiotic exposures are acknowledged to play an 
 overwhelmingly 
 important role in common chronic diseases such as cancer , diabetes , 
 cardiovascular , and neurodegenerative diseases , which constitute the 
 major health burden in economically developed countries .
it has been estimated that 90% of cancer deaths 
 and half of heart disease mortality can not be explained by genetic 
 factors , suggesting an environmental origin .
sequencing the human genome has permitted identification of individual 
 susceptibility to disease through genome - wide association studies , 
 but so far these have explained comparatively little of the variability 
 in chronic disease prevalence .
in contrast , 
 there has been a relative paucity of comparable tools for exposure 
 assessment and exposure estimates ( e.g. , 
 in epidemiology studies ) have relied primarily on questionnaires , 
 geographic information , and a few targeted measurements . in environmental 
 toxicology ,
exposure assessment is commonly based on quantifying chemical 
 concentrations in the exposure medium ( e.g. , water or soil ) , although 
 there is increasing recognition that quantification of intraorganism 
 concentrations provides a better means to link exposure to toxicological 
 effect .
remarkable advances in 
 analytical sensitivity and capacity , including 
 omics technologies , have now made environment - wide association study 
 ( ewas ) feasible , permitting detection of associations between 
 biomarkers and human health outcomes on a much larger scale than previously 
 possible .
wild , therefore , proposed the 
 concept of the exposome , representing all environmental exposures 
 ( including diet , lifestyle , and infections ) from conception onward , 
 as a complement to the genome in studies of disease etiology . in this 
 concept ,
exposures are therefore not only 
 restricted to chemicals ( toxicants ) entering the body from air , water , 
 or food but also include endogenous chemicals produced by inflammation , 
 oxidative stress , lipid peroxidation , infections , gut flora , and other 
 natural processes .
this internal chemical environment continually 
 fluctuates during life because of changes in external and internal 
 sources , aging , infections , life - style , stress , psychosocial factors , 
 and pre - existing diseases .
a challenge 
 lies in choosing on which of the thousands of measurable 
 biologically active chemicals to focus . paraphrasing abraham lincoln , 

stephen rappaport has suggested ,  we can not measure everything 
 all the time , but we can measure most things some of the time , and 
 we can measure some things most of the time .  thus , the primary purpose of biomarker - based exposure monitoring 
 should be to identify risk factors for use in epidemiological studies 
 rather than to provide exhaustive personalized exposomes that can not 
 be interpreted at an individual level .
even a partial exposome characterization 
 can lead to major public health benefits , for example , one or more 
 cross - sectional exposure measurements at different time points in 
 a prospective cohort study . because the 
 serum exposome is enormously complex
, an ewas focuses on measuring 
 network features , such as n - linked glycoproteins or reactive electrophiles 
 stabilized as adducts of human serum albumin . in this way
the issue of timing of exposure 
 assessment may have to be tailored to specific hypotheses by examining 
 exposure in cohorts of different ages .
the term biomarker can relate to any interaction between an 
 external 
 environmental agent and a biological system such as the human body . 
 in the context of risk assessment ,
biomarkers of interest have been 
 defined in terms of whether they relate quantitatively to a particular 
 exposure , its effect in a biological system , or to the way in which 
 the system reacts .
these biomarkers are 
 distinguished as follows:biomarker of exposure : an exogenous 
 substance or its metabolite or the product of an interaction between 
 a xenobiotic agent and some target molecule or cell that is measured 
 in a compartment within an organism;biomarker of effect : a measurable 
 biochemical , physiological , behavioral or other alteration within 
 an organism that , depending upon the magnitude , can be recognized 
 as associated with an established or possible health impairment or 
 disease;biomarker 
 of susceptibility : 
 an indicator of an inherent or acquired ability of an organism to 
 respond to the challenge of exposure to a specific xenobiotic substance .
biomarker of exposure : an exogenous 
 substance or its metabolite or the product of an interaction between 
 a xenobiotic agent and some target molecule or cell that is measured 
 in a compartment within an organism ; biomarker of effect : a measurable 
 biochemical , physiological , behavioral or other alteration within 
 an organism that , depending upon the magnitude , can be recognized 
 as associated with an established or possible health impairment or 
 disease ; biomarker 
 of susceptibility : 
 an indicator of an inherent or acquired ability of an organism to 
 respond to the challenge of exposure to a specific xenobiotic substance .
however , the boundaries between exposure 
 and effect are fluid and 
 it is therefore helpful to describe the measurement of xenobiotics 
 and their metabolites in biological systems as biological monitoring 
 and to restrict the term biomarker to consequences of functional or 
 structural change ( e.g. , protein and dna adducts , chromosome aberrations , 
 urinary proteins , and enzymes ) that represent an integration of exposure 
 and various biological processes .
for example , chemical - induced dna 
 adducts vary at the individual level because of carcinogen metabolism , 
 dna repair , and cell turnover , at least partially as a result of interindividual 
 genetic variation . therefore , such biomarkers may not strictly correlate 
 with exposure but are more likely to be associated with disease outcome , 
 for example , cancer risk , because they reflect the consequences of 
 exposure on a pathway relevant to carcinogenesis . in assessing biomarkers , particularly omics - based 
 biomarkers ,
it 
 is therefore important to separate those generated by environmental 
 exposures ultimately leading to disease ( causal pathway ) from those 
 that are manifestations of disease ( reactive pathway ) .
this process 
 of biomarker identification and validation can be separated into two 
 main phases . in the discovery phase
, 
 ewas is used in an untargeted , data - driven way with a focus on small 
 molecules and proteins .
the main objective is to identify discriminating 
 features that are consistent across studies , ideally those with a 
 prospective cohort design .
the exact chemical nature of the discriminating 
 feature has to be identified , a process that may involve searching 
 for matches in existing databases as well as additional experiments 
 using a battery of different analytical techniques . in the second 
 phase , candidate biomarkers are investigated in targeted , knowledge - driven 
 follow - up studies to establish causality , prevention , diagnosis , prognosis , 
 and treatment of diseases .
an example of such a stepwise approach 
 to the identification of biomarkers is the screening of several thousand 
 small nonpolar molecules in serum to discover a molecular biomarker 
 of susceptibility to colorectal cancer linked to low serum gta-446 
 anti - inflammatory fatty acid levels .
unraveling complex environmental and genetic disease etiologies 
 demands that both environmental exposures and genetic variation are 
 reliably measured . characterizing the exposome represents a technological 
 challenge at least as large as that for the human genome project , 
 which began when dna sequencing was in its infancy .
the data generated by application of omics technologies , 
 potentially in a longitudinal fashion , poses enormous bioinformatics 
 and biostatistics challenges , requiring substantive interdisciplinary 
 collaboration .
however , exposomics methods and goals are not as standardized 
 as they were for dna sequencing , and , consequently , the exposome does 
 not translate as easily to scale - up as did sequencing the human genome .
nevertheless , systems biology and systems toxicology , 
 when linked to epidemiology , offer the prospect of examining gene 
environment 
 interactions and identifying biomarkers for causative pathways of 
 diseases , monitoring their progression , and guiding treatments .
a fundamental building 
 block of systems toxicology is that exposures 
 lead to changes at the molecular level , some of which may induce morphological 
 or functional changes at the cellular and organism level that contribute 
 to toxic outcomes . over the past decade
, there has been unprecedented 
 development in technologies that enable the collection of very large - scale 
 data sets composed of quantitative information regarding molecular 
 responses on the basis of changes in all major classes of biomolecules 
 in living organisms .
furthermore , advanced imaging methods enable 
 the quantitative analysis of cell - level changes . hereafter
, we will 
 highlight a few of the recent developments in transcriptomic , proteomic , 
 metabolomic , and lipidomic measurement technologies as well as high - content 
 screening as the main emerging molecular methods that are critical 
 enablers of systems toxicology
. a broader range of technologies , including 
 epigenetics , and their applications to toxicology have been described 
 recently .
transcriptomics or 
 gene - expression profiling is perhaps the most widely used measurement 
 technology in systems toxicology and is used to study the changes 
 in expression of all mrnas in a cell population , organ , or organism . 

transcriptomic analysis is also the best established approach for 
 identifying perturbed biological networks ( see below ) and thereby 
 gaining mechanistic insight into the system s response to an 
 exposure .
the current technological basis of transcriptomics 
 measurements for systems toxicology studies has involved oligonucleotide 
 microarrays and , more recently , next - generation sequencing ( ngs ) .
oligonucleotide microarrays represented the major technological advance 
 in transcriptomics of the past decade and were introduced and generalized 
 with the invention of high - density array printing by affymetrix . today
, 
 it is possible to design an array of oligomer probes that covers the 
 whole transcriptome of any organism for which the genome sequence 
 is known and the possible open reading frames and gene models have 
 been identified using well - established bioinformatics analysis pipelines . 
 these microarrays enable high - throughput parallel analysis of many 
 samples derived from model systems as well as clinical or environmental 
 samples . with the concomitant progress in full cdna sequencing and 
 rna sequencing by next - generation sequencing technologies
, current 
 microarrays can cover each gene or its exons ; such exon arrays are 
 now available for human , mouse , and rat .
the use of exon arrays has 
 improved the analysis of alternatively spliced rna transcripts as 
 well as the accuracy of the overall gene - expression measurements .
ngs technologies are emerging as the measurement 
 methods that may supersede microarray technologies in the near future 
 on the basis of greater accuracy , providing exact transcript counts 
 and results that closely approach quantitative pcr .
furthermore , ngs 
 methods are more flexible as they enable gene - expression studies in 
 organisms for which microarrays are not available , such as model systems 
 used in environmental toxicology . finally , they are likely to offer 
 a higher throughput than microarrays as new developments will likely 
 allow for the analysis of thousands of transcriptome samples in a 
 single sequencing run .
the 
 proteome represents 
 the full complement of the proteins in a cell , organ , or organism , 
 and proteomics is a systematic approach to characterizing all or an 
 enriched subset of proteins therein .
measuring changes in levels and 
 modifications of proteins is applied to diagnostics , drug discovery , 
 and investigating toxic events .
proteomic data are of particular value 
 in systems toxicology because the proteome is an important mediator 
 of altered biological responses as a consequence of exposure to active 
 substances .
increases or decreases in protein levels may be a direct 
 consequence of corresponding mrna - expression changes , but increases 
 or decreases in protein function may also be influenced by post - translational 
 modifications .
for example , protein phosphorylation , which can be 
 further addressed by high - throughput phosphoproteomics , enables the 
 characterization of molecular events proximal to disease - related signaling 
 mechanisms .
mass spectrometry ( ms ) 
 is widely considered to be the central technology for modern proteomics , 
 mainly because of its unsurpassed sensitivity and throughput .
thanks 
 to the high accuracy of ms , peptides in the subfemtomolar range can 
 be detected in biological samples with a mass accuracy of less than 
 10 ppm .
this level of accuracy is necessary to compare proteins between 
 samples derived from exposed and control systems .
isotope tagging 
 for relative and absolute quantification ( itraq ) is used in comparative 
 proteomics for systems toxicology because it enables the relative 
 quantification of protein species between samples in a nontargeted 
 manner .
this method can be further complemented with a targeted method 
 of even higher accuracy : selected reaction monitoring ( srm ) .
it leverages a 
 unique property of ms to quantify peptides , produced by a controlled 
 enzymatic digestion of the proteome , as a way to quantify their corresponding 
 proteins .
because it is a targeted approach , any proteomics analysis 
 by srm requires the a priori selection of the proteins 
 to quantify .
this is generally achieved using the results of previous 
 experiments using nontargeted approaches such as itraq combined with 
 a review of the scientific literature to leverage prior knowledge . 

the list of selected proteins is then processed with bioinformatics 
 tools to identify at least two proteolytic peptides that optimally 
 represent the protein and distinguish it from all others .
this step 
 is followed by the selection of srm transitions for each of those 
 peptides and several optimization and validation steps to ensure unique 
 identification and accurate quantification .
this method enables a 
 multiplexed approach by which hundreds of proteins can be quantified 
 in a single ms run .
targeted approaches for protein quantification 
 have been developed 
 to produce accurate and high - sensitivity data for defined subproteomes . 

an emerging example is the development of surface - capture probes to 
 interrogate the surface - reactive subproteome , a critical response 
 gateway for active substances , as recently demonstrated for hepatocytes .
antibody - based approaches are also important 
 in targeted proteomics . for instance , reverse protein arrays ( rpas ) 
 have evolved from classical enzyme - linked immunosorbent assays ( elisa ) 
 and enable high - throughput analysis of up to 192 different lysates , 
 including post - translationally modified proteins , on a zeptochip with 
 over 200 validated antibodies . other antibody - based targeted methods , 
 such as luminex , provide alternative measurement platforms for multiplexed , 
 high - sensitivity and -specificity targeted proteomic data generation .
these data can lead to the refinement of mechanistic 
 details of toxicology pathways that were mainly based on transcriptomic 
 data .
metabolomics involves 
 a comprehensive and quantitative analysis of all metabolites or low 
 molecular weight organic or inorganic chemicals that are products 
 or substrates of enzyme - mediated processes . in the context of systems toxicology , metabolomics is unique because 
 it can be used to define amounts of internalized xenobiotic chemicals 
 and their biotransformation products as well as 
 the perturbed endogenous metabolome , which represents the ultimate 
 change in the levels of chemical species resulting from molecular 
 perturbations at the genomic and proteomic levels . in the first 
 case ,
an understanding of the kinetic behavior of xenobiotic toxicants 
 and their metabolites ( as well as related biomolecular adducts ) is necessary to identify candidate biomarkers 
 of exposure for human and environmental monitoring .
the second is 
 a more conventional systems biology perspective of metabolomics , which 
 involves both identification of metabolites and quantification of 
 changes in their abundance and rates of production caused by an exposure . from a technical perspective , metabolomics most commonly involves 
 nmr spectroscopy and/or ms analysis techniques in untargeted profiling 
 or targeted analysis strategies . profiling 
 of a metabolome may entail global detection and relative quantification 
 of a large number of metabolites without a priori knowledge .
however , targeted experiments involve the absolute quantification 
 of a small number of metabolites ( around 20 or less ) to test a defined 
 hypothesis . in systems toxicology , metabolomics may be accompanied 
 by concomitant transcriptomic and proteomic measurements to provide 
 the full context of the exposure .
integrated analysis of these diverse 
 data types is necessary to enable a full understanding of the mechanistic 
 events driving metabolomic changes .
finally , emerging application 
 areas include the characterization of effects of mixtures on chemical 
 responses in microfluidic organ model systems .
the lipidome is the complete 
 lipid profile of a biological system and is an example of a specialized 
 subset of the metabolome . by extension , lipidomics is a systematic 
 approach to characterize and quantify lipids in biological samples 
 using , as with metabolomics , analytical methods based on nmr and ms . 

it has been recognized recently that alterations of lipid homeostasis 
 contribute to several pathophysiological conditions .
lipids are not 
 only an important energy store but are also essential constituents 
 of all cellular membranes and exert a number of signaling functions . 
 indeed , profound changes in the cellular and tissue lipid composition 
 occur in response to exposure to active substances and environmental 
 factors .
lipidomics is thus a powerful method to monitor the overall 
 lipid composition of biological matrixes and has been shown to have 
 great potential to identify and detect candidate biomarker signatures 
 indicative of toxicity .
there have been rapid recent advances in ms - based 
 techniques capable of identifying and quantifying hundreds of molecular 
 lipid species in a high - throughput manner .
the lipid extracted 
 from various biological matrices can then be analyzed by multiple 
 ms platforms , either by detecting the lipids by shotgun lipidomics 
 or after separation by liquid chromatography to detect and quantify 
 lipids of lower abundance . performing concomitant transcriptomics 
 analysis in samples subjected to lipidomic profiling enables the integrated 
 analysis of these complementary data sets to derive mechanistic information 
 regarding the toxicological meaning of lipid alterations .
molecular 
 changes measured in systems toxicology studies should , as often as 
 possible , be correlated with cellular or tissue - level changes measured 
 under the same conditions .
although histopathology is the main approach 
 to gather such data , phenotypic assessment of cells in culture can 
 be performed using high - content screening ( hcs ) methods .
this technique is based on computer - aided visual 
 detection of a panel of functional biomarker measurements providing 
 precise temporal , spatial , and contextual information that define 
 the biological status of the cells or organs and structure of small 
 organisms ( e.g. , early life stages in zebrafish ) .
the measurement 
 of the different biomarkers can be achieved on either a fixed specimen 
 labeled with fluorescent reagents or directly on a living specimen 
 during the time of the exposure .
a broad panel of biomarkers can be 
 analyzed in a multiplexed manner to provide quantitative measurements 
 of the level of abundance and localization of proteins ( e.g. , phosphorylated 
 protein ) and/or changes in the morphology of the cell .
each biomarker 
 can be adapted to the cell type relevant to a particular exposure 
 modality to quantify key cellular events such as ( 1 ) apoptosis and 
 autophagy , ( 2 ) cell proliferation , ( 3 ) cell viability , ( 4 ) dna damage , 
 ( 5 ) mitochondrial health , ( 6 ) mitotic index , ( 7 ) cytotoxicity and 
 oxidative stress , ( 8) nascent protein synthesis , and ( 9 ) phospholipidosis 
 and steatosis .
hcs is enabled by computer - aided automated digital 
 microscopy ( e.g. , arrayscan ) or flow cytometry for data analysis and 
 storage .
advanced experimental technologies for systems toxicology produce 
 unprecedented amounts of data , the management of which is a major 
 challenge for scientists .
it is expected that this trend will accelerate 
 because of ongoing technology developments and concomitant cost decreases . 

therefore , we are witnessing many efforts globally to develop methods 
 and tools to manage , integrate , and process this data . as a consequence , 

a wide range of open - source software solutions ( e.g. , emma and mimas ) are 
 continuously being developed and made available to store and manage 
 data along with the information to describe accurately the experimental 
 setup and the conditions that led to the data .
this latter point is 
 of crucial importance to data integration , experimental reproducibility , 
 and proper data analysis and interpretation .
national academy of sciences has recently specified experimental 
 transparency as one of the major requirements for translational omics .
minimum information about microarray experiment 
 ( miame ) -based exchange format mage - tab provides a solution for microarray data , which 
 has also become the standard for data sets deposited in public databases 
 such as arrayexpress ( http://www.ebi.ac.uk/arrayexpress/ ) or gene expression omnibus 
 ( geo ) ( http://www.ncbi.nlm.nih.gov/geo/ ) . a similar standard exists for proteomics experiments , namely , 
 the minimum information about a proteomics experiment ( miape ) .
the analysis of complex data sets is greatly 
 facilitated by computational 
 pipelines or workflows that automate a sequence of computational tasks , 
 often performed by independent software components . in this context , 

computational biology workflow management systems ( e.g. , galaxy and taverna ) have 
 been developed to implement reproducible data analysis processes . 

caarray and cagrid from the national 
 cancer institute biomedical informatics grid ( nci cabig ) are prominent 
 examples of a systems biology data - integration environment that includes 
 knowledge management as well as workflow systems . for instance , this 
 environment allows integration with commercial data analysis and biological 
 pathway analysis systems .
as outlined above , the core objective 
 of systems toxicology is 
 to elucidate the mechanisms that causally link a well - characterized 
 exposure to active substances with quantifiable adverse events and 
 disease .
it requires collection of experimental data reflecting molecular 
 changes in the context of quantifiable cellular , tissue - level , or 
 physiological changes that are linked to disease phenotypes or adverse 
 events at the organism level .
thus , systems toxicology heavily relies 
 on computational approaches to manage , analyze , and interpret the 
 data generated by the large - scale experimental approaches described 
 earlier .
the ultimate goal is to develop predictive in silico models that can be used in risk assessment . to reach this ambitious 
 goal
, computational systems toxicology has four major areas of focus:analyzing the 
 massive amounts 
 of systems - wide data generated by high - throughput methods and their 
 integration with the structural description of the substances involved 
 in the exposure.representing the relevant mechanisms 
 leading to adverse outcome as biological network models that adequately 
 describe both the normal state and the causal effect of their perturbations 
 upon exposure to active substances ( figure 3).quantifying 
 the dose - dependent 
 and time - resolved perturbations of these biological networks upon 
 exposure and assessing their overall biological impact.building and validating adequate 
 computational models with predictive power that can be applied to 
 risk assessment ( figure 3 ) . analyzing the 
 massive amounts 
 of systems - wide data generated by high - throughput methods and their 
 integration with the structural description of the substances involved 
 in the exposure .
representing the relevant mechanisms 
 leading to adverse outcome as biological network models that adequately 
 describe both the normal state and the causal effect of their perturbations 
 upon exposure to active substances ( figure 3 ) .
quantifying 
 the dose - dependent 
 and time - resolved perturbations of these biological networks upon 
 exposure and assessing their overall biological impact .
building and validating adequate 
 computational models with predictive power that can be applied to 
 risk assessment ( figure 3 ) . biological network model - development process . 

initial static bn 
 models can be constructed using biological facts derived from the 
 literature .
these initial 
 models can serve as the basis to guide the development of computable 
 bn models .
these models rely on biological facts ( key events ) derived 
 from both the literature and new experimental data and are expressed 
 in a computable format such as the biological expression language 
 ( bel ) .
computational methods such as reverse causal reasoning and 
 reverse engineering are used to support the model - building process . 

such networks can then serve as the foundation to build executable 
 bn models in which edges are expressed with equations such as ordinary 
 differential equations .
the main applications of these broad classes 
 of bn models are shown in light blue boxes .
hereafter , we highlight just a 
 few applications of computational 
 systems toxicology covering computational toxicology , biological network 
 model building , biological impact quantification , and predictive modeling 
 to exemplify some recent developments in these four areas . computational 
 toxicology , recently reviewed by kavlock and colleagues , is a growing area of research that integrates 
 molecular and cell biology with chemistry and computational approaches . 

the objective is to increase the predictive power of toxicology by 
 more efficiently and effectively ranking chemicals based on risk . 
 with the advent of high - throughput experimental setups
, relevant cell - based 
 assay systems can be exposed to a large number of substances in a 
 dose - dependent manner , and many phenotypic end points can be measured 
 quantitatively by high - content ( or visual ) screening methods .
furthermore , 
 cell - free enzymatic and ligand - binding assays , including g - protein - coupled 
 and nuclear receptors , kinases , phosphatases , cytochrome p450s , histone 
 deacetylases , ion channels , and transporters , can be used in a high - throughput 
 screening mode to profile the mechanism(s ) of action of chemicals . 

these approaches enable the screening of numerous substances for their 
 impact on toxicity pathways .
this will eventually lead to the creation 
 of a comprehensive landscape of adverse cellular effects induced by 
 biologically active substances .
the integration of the biological 
 end points with the chemical structures of the tested substances enables 
 computational mapping ( interpolating ) of untested chemical analogues 
 into the toxicity data , thereby providing a prediction of their relative 
 toxicological hazard potential .
toxcast aims 
 to understand the potential health risks of a large number of diverse 
 chemicals including failed pharmaceuticals , commodity chemicals , and 
 pesticides by employing a large array of high - throughput screening 
 methodologies .
the first phase , or proof of concept , involved the 
 use of almost 300 chemicals that had been well - characterized for their 
 toxicological potential in animal models that were studied in more 
 than 600 high - throughput screening assays .
the results of this phase included a large number of expected perturbed 
 biological pathways and the development of several predictive models 
 based on a multifactorial data analysis .
the second phase of the toxcast initiative ( http://www.epa.gov/ncct/toxcast/ ) is currently screening over 1000 such compounds against many of 
 the same targets employed in phase i , and the data for this component 
 has been released ( see toxcast website ) . a key element of the toxcast 
 program involves the interpretation of the in vivo relevance of in vitro activity concentrations through 
 a process called reverse toxicokinetics . combining this information with high - throughput exposure assessments , 
 as introduced by wambaugh and colleagues , provides for a risk - based process of prioritization of chemicals 
 for more intensive study of their potential to impact human health . by necessity , all descriptions of complex systems need to be simpler 
 than the system itself .
indeed , bn models are represented as diagrams 
 of nodes ( entities ) and edges ( relationships ) .
initial models can 
 be constructed using existing knowledge drawn from both pathway databases 
 and literature - derived information ( figure 3 ) .
although publicly accessible databases of manually curated biological 
 networks and pathways , such as the kyoto encyclopedia of genes and 
 genomes ( kegg ) and wikipathways , are available , they generally provide a high - level 
 static representation of our understanding of biological processes 
 and offer only a limited representations of the adverse event mechanisms 
 in the context of a particular exposure .
therefore , they need to be 
 further supplemented with new nodes and edges derived from experiments 
 adequately designed to link observed molecular changes with phenotypic 
 changes in the context of a given exposure . to extract the key molecular 
 entities and their relationships from these data sets , sophisticated 
 reverse - engineering algorithms , such as gns healthcare s reverse - engineer 
 and forward simulation predictive framework or those reviewed by lecca and priami can be applied .
second , these high - level pathway maps have limited 
 computational value for the analysis of systems toxicology data sets . 
 even though recent network informatics tools allow us to  paint  
 pathway maps according to the differential abundance of molecular 
 entities ( such as gene - expression data ) , they generally fall short 
 of providing a computational infrastructure for causal reasoning and 
 hence a more complete interpretation of high - throughput data sets . 

the transition from static to computable bn models is thus a necessary 
 step to analyze experimental data fully and to build our collective 
 knowledge of the toxicological effects of exposure to biologically 
 active substances .
this transition requires a formal language to describe 
 the causal nature of the interactions between nodes to complement 
 the gene ontology that already provides 
 a coherent framework for the description of the nodes themselves . 

the recent development of the biological expression language ( bel ) , 
 for instance , enables this transition because it allows the semantic 
 representation of life - science relationships in a computable format and thus the development of computable bn models 
 composed of cause - and - effect relationships .
bel was designed to capture 
 biological cause - and - effect relationships with associated experimental 
 context from disparate sources .
bel thereby facilitates the encoding 
 of directional relationships within computable biological network 
 models .
the bel framework is an open - source technology for managing , 
 publishing , and using structured life - science knowledge ( http://www.openbel.org/ ) .
this is unlike the biological pathway exchange 
 ( biopax ) , which focuses on the integration and exchange of biological 
 pathway data across a large array of existing pathway resources .
building causal computable bn models is 
 a process that has been previously described in several publications 
 and is briefly summarized below .
these models consist of qualitative causal relationships 
 to represent biological processes and are coded in bel .
the model - building 
 process starts with the definition of its contents and boundaries 
 by including all necessary mechanistic building blocks relevant to 
 the bn under consideration .
this step is guided by literature investigations 
 of the signaling pathways relevant to the bn model under construction . 

these network boundaries are then used to screen the literature and 
 to derive a collection of cause - and - effect relationship statements 
 describing the relevant and known biology linked to the bn model in 
 a given tissue context . from this
the model consists of nodes , representing biological entities , such 
 as mrna expression or protein abundance , and edges , representing causal 
 relationships between the nodes . during this model - building process , 

reverse causal reasoning ( rcr ) analysis 
 of relevant gene - expression data sets is used to guide the selection 
 of nodes for model building .
rcr interrogates the experimental data 
 against a knowledgebase of possible causal relationships for all of 
 the context - specific nodes and edges in the literature model to identify 
 upstream controllers of the mrna state changes ( gene - expression changes ) 
 observed in the data set .
the potential upstream controllers identified 
 by rcr are statistically significant potential explanations for the 
 observed mrna state changes .
these potential upstream controllers 
 are then included as new nodes in the bn model if they have literature 
 support for a mechanistic role in the biological process and context 
 of interest .
this allows uncovering relevant nodes and edges that 
 were not identified during the construction of the literature - based 
 network .
rcr - based augmentation of the network model can be performed 
 using proprietary or publicly available gene - expression data sets 
 ( e.g. , geo or arrayexpress ) . as a final step in the construction of 
 the network model , the nodes and edges are manually reviewed and refined 
 to include relevant context and pathways ( e.g. , by additional literature 
 review ) , producing the final network model .
the review process 
 should be conducted with each model periodically 
 to ensure that newly available data sets and new literature are taken 
 into account and thereby ensure that each model is kept up to date . 

furthermore , bn models need to be verified to prove that they capture 
 context - specific nodes and edges that are supported by experimental 
 observations . this verification can be achieved on three levels .
first , 
 existing data sets not considered during model building can be used 
 to verify specific areas of the model .
this approach is , however , 
 constrained by the availability of relevant publicly accessible data 
 sets that fulfill the context - specific boundary criteria .
second , 
 the structure of the model can lead to the design and conduct of new 
 experiments designed for verification purposes , as exemplified by 
 recent work on the computable cellular proliferation network model 
 for normal lung cells .
finally , research 
 communities can be engaged to curate network models as implemented in wikipathways or through collaborative competition leveraging the wisdom of the 
 crowd . by systematically investigating 
 and describing the major mechanisms 
 involved in toxicology
, the scientific community will gradually build 
 a compendium of computable bn models covering the crucial mechanisms 
 involved in toxicity .
three layers of information and knowledge need 
 to be addressed to tackle this daunting task .
first , the molecular 
 entities ( i.e. , the nodes in the bn models ) will need to be further 
 characterized , and their often diverse biological functions need to 
 be captured in curated knowledge bases such as uniprot .
second , bn model construction will need to focus 
 on the identification of new nodes and connections in a context - sensitive 
 manner , where cell type and tissue environment are taken into account . 

finally , identified mutations and their contributions to adverse manifestations 
 need be the catalogued and mapped into the bn models in a consistent 
 manner .
this is essential to systems biology in general and systems 
 toxicology in particular and will require the coordination of efforts that can easily dwarf the human genome project . the ability to predict
the potential health risk of long - term exposure 
 to biologically active substances is an ultimate goal in systems toxicology . 

however , disease or population - level responses 
 might take decades to manifest , at which point changes in therapeutic 
 regime , life style , or exposure would not prevent disease onset .
it 
 is therefore necessary to develop methods that are predictive and 
 quantitative for risk assessment .
it is well - understood that exposure 
 to biologically active substances leads to modifications in the abundance 
 of molecular entities within a living system , and , in some cases , 
 modifications in the very nature of these entities , such as splice 
 variants and post - translational modifications of proteins , lead to 
 bn perturbations , which in turn can later lead to tissue - level changes 
 and adverse events or disease . as described earlier ,
causal computable 
 bn models are designed to describe these causal links between biological 
 entities and thus provide a possible quantitative framework for biological 
 impact assessment .
indeed , these models can be used to derive a data - driven 
 quantitative assessment of the perturbation of a given bn .
recent 
 toxicogenomics approaches have proven 
 the potential of mechanism - based assessment methods by using a simple 
 scoring method to evaluate the overall gene set - level expression changes 
 to profile biologically active substances .
although the use of gene 
 sets provides a first assessment based on gene lists derived from , 
 for example , kegg and other pathway databases , bn models will provide 
 a more accurate and detailed analysis of the mechanistic effects of 
 the exposure as well as a quantitative evaluation of the perturbation 
 of each model under consideration and an overall pan - model biological 
 impact . by way of example
, we have published a five - step approach 
 to derive such a biological impact factor ( bif ) for a given exposure or stimulus .
this approach employs 
 causal computable bn models as the substrate to analyze high - throughput 
 data sets derived from in vitro and in vivo dose  response and time - course experiments .
briefly , quality - controlled 
 measurements generated in vitro or in vivo constitute systems response profiles ( srps ) for each given exposure 
 in a given experimental system .
these srps express the degree to which 
 each individual molecular entity is changed as a consequence of the 
 exposure of the system .
these srps are then analyzed in the context 
 of a collection of bn models designed to cover the molecular mechanisms 
 perturbed by the exposure .
this analysis is designed to answer questions 
 about the completeness of the models ( i.e. , whether additional nodes 
 and edges need to be added to the model to cover all of the exposure 
 effects and to identify any new mechanisms not yet reported in the 
 literature ) .
this analysis employs rcr , by which downstream measurements 
 ( e.g. , gene expression ) are causally mapped as effects of an activity 
 of individual elements in the model .
once this step is completed , 
 we apply specific algorithms to derive the specific network perturbation 
 amplitude ( npa ) scores .
specifically , 
 the npa algorithm translates gene fold - changes ( or other downstream 
 measurements ) into the perturbation of individual elements in the 
 model and finally aggregates these perturbations into a bn - specific 
 score . by providing a measure of biological network 
 perturbation , npa scores allow correlation of molecular events with 
 phenotypes that characterize the network at the cell , tissue , or organ 
 level .
finally , the overall biological impact of a perturbing agent can be estimated by aggregating the npa scores 
 from several bn models into one holistic value , the bif , that expresses 
 the overall impact on the entire biological system .
this bif can be 
 readily correlated with other measurable end points ( e.g. , collected 
 at a higher level of biological organization through phenotypic changes ) 
 or used to generate testable hypotheses that could provide new insight 
 into mechanisms of disease onset or progression .
the bn models described above provide 
 either a static or a computable environment to contextualize experimental 
 data in the framework of current knowledge .
although these approaches 
 enable a detailed understanding of molecular processes and the identification 
 of new nodes and edges to supplement existing bn in a context - sensitive 
 manner ( e.g. , particular cell type in a specific tissue environment ) , 
 they do not exhibit the simulation capabilities necessary to understand 
 the dynamic response of a molecular system to an exposure or more 
 importantly to predict the effect of new exposures a priori .
key to this transition from descriptive to executable biology is 
 the development of mathematical models that represent the dynamic 
 properties of biological processes and their corresponding computational 
 models that can be executed in silico .
therefore , 
 the ultimate objective of computational systems toxicology is to develop 
 dynamic ( executable ) mathematical models of these bns in such a way 
 that their simulation correctly represents the observed behavior of 
 the biological process ( figure 3 ) . such models 
 will eventually permit true predictions , such as the emergence of 
 new network behaviors following in silico perturbations . 

the development of such models is currently still in its infancy and 
 represents a major frontier in systems biology and systems toxicology . 

epa and aims 
 to build a virtual embryo modelin silico to predict the potential for environmental chemicals 
 to affect embryonic development .
the predictive power of the model 
 will be tested with a selection of well - characterized chemicals with 
 known health effects in animal studies .
another example of predictive 
 modeling is provided by the physiolab platform developed by entelos 
 ( foster city , ca , usa ) .
several large - scale dynamic mathematical models 
 were developed and include both human and mouse models of diseases : 
 ( 1 ) virtual nod mice ( type 1 diabetes physiolab ) , ( 2 ) virtual apoe mice ( apoe mouse physiolab ) , ( 3 ) virtual humans in both rheumatoid arthritis and ( 4 ) the skin sensitization physiolab .
all of these physiolab platforms are large multiscale biological 
 models where molecular and cellular interactions are generally represented 
 by ordinary differential equations , the numerical solution of which 
 simulates the dynamic response of the biological system across several 
 layers of organization .
although most of these computational systems 
 are primarily designed to plan and understand , for example , clinical 
 studies , two of them are sample applications for systems toxicology . 
 for instance , the apoe physiolab was designed 
 to understand the toxicological effects of cigarette smoke and cessation 
 on atherosclerotic plaque growth .
the 
 objective was to build a framework to translating experimental apoe mouse results to the human setting despite 
 the uncertainty around the mechanisms underlying disease etiology , 
 the relative importance of these mechanisms as drivers of progression , 
 and how these roles change in response to lifestyle changes .
this 
 model may be used to optimize in vivo experiments 
 and to pave the way for a similar modeling approach for human disease . 

the second example is the dilisym modeling software , a multiscale 
 representation of drug - induced liver injury ( dili ) that is associated with simpops ( see http://dilisym.com/products-services/simpops.html ) , a collection of simulated individuals that match a particular 
 range and distribution of parameters and patient characteristics , 
 allowing the exploration of the interindividual variability in response 
 to potential dili - causing drugs .
the third example is driven by recent 
 changes in european union legislation , prohibiting the use of in vivo experimental animal models for testing cosmetics 
 ( eu cosmetics regulation ) . as a consequence , 

nonanimal approaches to provide the data for skin sensitization risk 
 assessment need to be further developed . in this context
, maxwell 
 and mackay have developed an in silico model of the induction of skin sensitization to 
 characterize and quantify the contribution of each pathway to the 
 overall biological process .
the resulting skin sensitization physiolab 
 is thus an important attempt to integrate data derived from different 
 forms of nonanimal hazard data to inform new skin sensitization risk - assessment 
 decisions and demonstrates how computational systems toxicology can 
 contribute to hazard evaluation in a new framework for consumer safety 
 risk assessment .
predictive model development 
 has also been started in nonhumans , namely , fish , and focuses on the 
 reproductive effects resulting from disturbance of the endocrine axis 
 by environmental chemicals in a research program led by the u.s .
the development of dynamic ( executable ) 
 mathematical models of aops linking exposure to individual and population - wide 
 adverse outcomes is the grand challenge of computational systems toxicology . 

although new computational methods and mathematical tools are certainly 
 needed , the major challenge in building such models is our limited 
 understanding of how bn perturbations are linked to the kes in the 
 aops at a higher level of biological organization .
although this obviously 
 requires a deep understanding of the dynamic behavior of the causal 
 link between bn perturbations and kes , it also requires a far deeper 
 understanding of the causal link between these kes and the adverse 
 outcome for the individual and how these translate to the population . 

although the dynamic models described above are a step toward understanding 
 the links between bn perturbations and individual adverse outcomes , 
 the last step could be achieved by creating large sets of mechanistically 
 distinct virtual humans that , upon simulation , statistically match 
 the prevalence of phenotypic variability reported in human studies . 

the recently reported algorithm termed mechanistic axes population 
 ensemble linkage ( mapel ) , which utilizes 
 a mechanistically based weighting method to match clinical trial statistics , 
 may be an approach enabling such population - based risk assessment 
 and expands on the concept of simpops .
similar model can be developed 
 for other organisms , such as fish or plants .
such dynamic models will 
 eventually allow for simulating the behavior of biological systems 
 at both the individual and population levels to generate new hypotheses 
 and insights into the potential effect of an exposure a priori .
the needs of risk assessment are context - dependent 
 and can vary 
 from simple classification of a substance for hazard ( e.g. , is it 
 genotoxic or not ) to prioritization by the nature and severity of 
 hazard for further investigation to quantitative estimates of risk 
 to determine the urgency and nature of any risk - management action . 

although existing approaches have served society well , there is increasing 
 concern , some real and some hypothetical , that there may be important 
 weaknesses in the assumptions used in current risk assessment .
these 
 include the shape of the dose  response relationship under human - relevant 
 exposures , whether biological thresholds exist and for what end points , 
 the extent of population variability in toxicological response , and 
 the influence of factors such as life stage on response . despite a 
 substantial amount of research ,
some in large groups of animals , it 
 has not been possible to resolve these concerns .
this shortcoming 
 is due to the intrinsic limitation of all experimental observations 
 by the power of the study .
as discussed above , systems toxicology 
 has real potential to provide the data as well as the quantitative 
 mechanistic models to address these issues . in this sense ,
systems 
 toxicology can provide a deep mechanistic understanding of toxicological 
 effects , permitting prediction of responses to chemicals .
if adequately 
 described , a systems description should enable prediction of responses 
 for which experimental data were not available ( i.e. , the system will 
 exhibit emergent properties entailing novel patterns and properties 
 arising from the inherent structure of the system ) .
hence , it will be some time before they can be used in risk assessment . 

furthermore , risk assessors are not often well - trained in relevant 
 mathematical procedures and hence there is inherent reluctance to 
 adopt computationally intensive approaches with which they are poorly 
 familiar .
one way in 
 which this might be achieved is by adapting the more familiar mode 
 of action / adverse outcome pathway paradigm .
a moa / aop comprises a 
 series of quantifiable key events that are necessary but individually 
 usually not sufficient for a toxicological response to a chemical . 

it should be possible to use a modular approach to the development 
 of systems toxicology , where one starts with a systems - based model 
 of a single key event and incorporates that into a quantitative biological 
 model with dose 
response data for the other key events at an 
 operational level . in this way
, one could progressively add systems - based 
 modules describing each key event until a full systems - based description 
 of the response is achieved .
this will also 
 allow incorporation of systems toxicologically in a progressive fashion 
 into risk assessment , while providing risk assessors the opportunity 
 to become familiar and comfortable with evaluating such data .
major efforts are underway to develop nonanimal test methods , such 
 as the high - throughput toxcast program discussed earlier .
however , 
 in themselves , these platforms are unlikely to meet the needs of risk 
 assessment beyond hazard identification .
taking a further step will 
 require additional quantitative approaches , for which systems toxicology 
 would be ideally suited .
however , development of suitable models will 
 be a complex and time - consuming undertaking .
mode of action can provide 
 a translational bridge , enabling the incorporation of information 
 from in vitro and other approaches into a systems - based 
 description of key events , thereby enabling the stepwise development 
 of a full systems toxicology characterization of the organ and eventually 
 the organism
we stand 
 at the edge of an unprecedented transformation in the 
 conduct of toxicological evaluations .
a central tenant of the new 
 toxicology involves applying modern molecular analysis techniques 
 to elucidate mechanisms of toxicity and is being enabled by several 
 factors .
the first is the increasing power and availability of molecular 
 measurement tools able to probe the functioning of biological networks 
 inside organisms , organs , tissues , and cells .
the second is the increasing 
 affordability of high - throughput and high - content characterization 
 approaches that can be applied to thousands of chemicals in short 
 time periods rather than the chemical - by - chemical approach of the 
 past 4 decades that involves thousands of animals and perpetual high 
 costs and years of duration .
the third enabler is the increasing computational 
 power , data - storage capacity , and information - management tools now 
 available to the scientific community that has facilitated the ability 
 to employ complicated systems biology models .
the fourth enabler is 
 the acceleration in the development of adequate in vitro test systems to complement and gradually replace animal models .
the fifth enabler is that of significant resource 
 investment by governments throughout the world in funding efforts 
 to develop the scientific foundation of systems toxicology .
for example , 
 darpa ( the defense advanced research projects agency , usa ) recently 
 invested $ 70 million to develop up to 10  organs on chips  
 within the next 5 years .
likewise , the eu has invested several hundred 
 million euros in fp7 ( see http://www.axlr8.eu/ ) for various supporting projects such as seurat-1 ( safety assessment 
 ultimately replacing animal testing , see http://www.seurat-1.eu ) , with 
 prospects to continue the investment in the european funding framework , 
 horizon 2020 .
ultimately , these international efforts need to be combined 
 to produce a real shift in the risk - assessment paradigm that not only 
 assures the highest levels of protection of public health and the 
 environment but also enables economic growth and global trade .
finally , 
 a key requirement in addressing the challenge is that contributors 
 and stakeholders , whether from government , academia , industry , ngos , 
 regulatory bodies , or the public at large , reach a consensus that 
 new approaches based on systems toxicology reach the standards they 
 demand .
 systems 
 toxicology is the integration of classical toxicology with 
 quantitative analysis of large networks of molecular and functional 
 changes occurring across multiple levels of biological organization . 
 
 society demands increasingly close scrutiny of the potential health 
 risks associated with exposure to chemicals present in our everyday 
 life , leading to an increasing need for more predictive and accurate 
 risk - assessment approaches . 
 developing such approaches requires a 
 detailed mechanistic understanding of the ways in which xenobiotic 
 substances perturb biological systems and lead to adverse outcomes . 
 
 thus , systems toxicology approaches offer modern strategies for gaining 
 such mechanistic knowledge by combining advanced analytical and computational 
 tools . 
 furthermore , systems toxicology is a means for the identification 
 and application of biomarkers for improved safety assessments . in 
 systems toxicology , 
 quantitative systems - wide molecular changes in 
 the context of an exposure are measured , and a causal chain of molecular 
 events linking exposures with adverse outcomes ( i.e. , functional and 
 apical end points ) is deciphered . 
 mathematical models are then built 
 to describe these processes in a quantitative manner . 
 the integrated 
 data analysis leads to the identification of how biological networks 
 are perturbed by the exposure and enables the development of predictive 
 mathematical models of toxicological processes . 
 this perspective integrates 
 current knowledge regarding bioanalytical approaches , computational 
 analysis , and the potential for improved risk assessment . 
Introduction
The Exposome: A Systems Approach
to Understanding
Exposure
Large-Scale Molecular Measurement
Data and Information Management
Computational Platforms for
Systems Toxicology
Systems Toxicology and Risk
Assessment
Outlook