Using measures of metabolic flux to align screening and clinical development: Avoiding pitfalls to enable translational studies.

Screening campaigns, especially those aimed at modulating enzyme activity, often rely on measuring substrate→product conversions. Unfortunately, the presence of endogenous substrates and/or products can limit one's ability to measure conversions. As well, coupled detection systems, often used to facilitate optical readouts, are subject to interference. Stable isotope labeled substrates can overcome background contamination and yield a direct readout of enzyme activity. Not only can isotope kinetic assays enable early screening, but they can also be used to follow hit progression in translational (pre)clinical studies. Herein, we consider a case study surrounding lipid biology to exemplify how metabolic flux analyses can connect stages of drug development, caveats are highlighted to ensure reliable data interpretations. For example, when measuring enzyme activity in early biochemical screening it may be enough to quantify the formation of a labeled product. In contrast, cell-based and in vivo studies must account for variable exposure to a labeled substrate (or precursor) which occurs via tracer dilution and/or isotopic exchange. Strategies are discussed to correct for these complications. We believe that measures of metabolic flux can help connect structure-activity relationships with pharmacodynamic mechanisms of action and determine whether mechanistically differentiated biophysical interactions lead to physiologically relevant outcomes. Adoption of this logic may allow research programs to (i) build a critical bridge between primary screening and (pre)clinical development, (ii) elucidate biology in parallel with screening and (iii) suggest a strategy aimed at in vivo biomarker development.

A Surrogate Matrix-Based Approach Toward Multiplexed Quantitation of an sGC Stimulator and cGMP in Ocular Tissue and Plasma.

A liquid chromatography-tandem mass spectrometry assay was developed and qualified for the multiplexed quantitation of a small molecule stimulator of soluble guanylate cyclase (sGC) and its target engagement biomarker, 3',5'-cyclic guanosine monophosphate (cGMP), in ocular tissues and plasma from a single surrogate matrix calibration curve. A surrogate matrix approach was used in this assay due to the limited quantities of blank ocular matrices in a discovery research setting. After optimization, the assay showed high accuracy, precision, and recovery as well as parallelism between the surrogate matrix and the biological matrices (rabbit plasma, vitreous, and retina-choroid). This assay provided pharmacokinetic and target engagement data after intravitreal administration of the sGC stimulator. The nitric oxide-sGC-cGMP pathway is a potential target to address glaucoma. Increasing sGC-mediated production of cGMP could improve aqueous humor outflow and ocular blood flow. The sGC stimulator showed dose-dependent exposure in rabbit vitreous, retina-choroid, and plasma. The cGMP exhibited a delayed yet sustained increase in vitreous humor but not retina-choroid. Multiplexed measurement of both pharmacokinetic and target engagement analytes reduced animal usage and provided improved context for interpreting PK and PD relationships.

Time-dependent behavioral data from zebrafish reveals novel signatures of chemical toxicity using point of departure analysis.

High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based "hits" and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD10 values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Analysis of BMD10 values revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast in vitro data and zebrafish in vivo behavioral data, however ToxPi analysis indicated that rankings based on in vitro data were not a reliable predictor of in vivo rankings for lower potency chemicals. This analysis method will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.

Pharmacokinetic Properties of Humanized IgG1 and IgG4 Antibodies in Preclinical Species: Translational Evaluation.

Assessment of the factors that regulate antibody exposure-response relationships in the relevant animal models is critical for the design of successful translational strategies from discovery to the clinic. Depending on the specific clinical indication, preclinical development paradigms may require that the efficacy or dosing-related attributes for the existing antibody be assessed in various species when cross-reactivity of the lead antibody to the intended species is justified. Additionally, with the success of monoclonal antibodies for management of various human conditions, a parallel interest in therapeutic use of these novel modalities in various veterinary species has followed. The protective role of neonatal Fc receptor (FcRn) in regulation of IgG homeostasis and clearance is now well recognized and the "nonspecific clearance" of antibodies through bone marrow-derived phagocytic and vascular endothelial cells (via lysosomal processes) is modulated by interactions with FcRn receptors. In this study, we have attempted to examine the PK properties of human IgG antibodies in dog and monkey. These studies establish a translational framework for evaluation of IgG antibody PK properties across species.

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints toward their mechanism of toxicity. Gene expression and high-content imaging-derived phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning. Imaging features capturing changes in cell morphology and nucleus texture along with mRNA levels of HMOX1 and SQSTM1 were identified as the most powerful predictors of toxicity. These biomarkers were validated by their ability to accurately predict kidney toxicity of four out of six candidate therapeutics that exhibited toxicity only in late stage preclinical/clinical studies. Network analysis of similarities in toxic phenotypes was performed based on live-cell high-content image analysis at seven time points. Using compounds with known mechanism as reference, we could infer potential mechanisms of toxicity of candidate therapeutics. In summary, we report an approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities.

Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing Schedules.

Gastrointestinal (GI) adverse events (AEs) are frequently dose limiting for oncology agents, requiring extensive clinical testing of alternative schedules to identify optimal dosing regimens. Here, we develop a translational mathematical model to predict these clinical AEs starting from preclinical GI toxicity data. The model structure incorporates known biology and includes stem cells, daughter cells, and enterocytes. Published data, including cellular numbers and division times, informed the system parameters for humans and rats. The drug-specific parameters were informed with preclinical histopathology data from rats treated with irinotecan. The model fit the rodent irinotecan-induced pathology changes well. The predicted time course of enterocyte loss in patients treated with weekly doses matched observed AE profiles. The model also correctly predicts a lower level of AEs for every 3 weeks (Q3W), as compared to the weekly schedule.

Multiscale Mathematical Model of Drug-Induced Proximal Tubule Injury: Linking Urinary Biomarkers to Epithelial Cell Injury and Renal Dysfunction.

Drug-induced nephrotoxicity is a major cause of acute kidney injury, and thus detecting the potential for nephrotoxicity early in the drug development process is critical. Various urinary biomarkers exhibit different patterns following drug-induced injury, which may provide greater information than traditional biomarkers like serum creatinine. In this study, we developed a multiscale quantitative systems pharmacology model relating drug exposure to proximal tubule (PT) epithelial cell injury and subsequently to expression of multiple urinary biomarkers and organ-level functional changes. We utilized urinary kidney injury molecule-1 (Kim-1), alpha glutathione S-transferase, albumin (αGST), glucose, and urine volume time profiles as well as serum creatinine and histopathology data obtained from rats treated with the nephrotoxicant cisplatin to develop the model. Although the model was developed using single-dose response to cisplatin, the model predicted the serum creatinine response to multidose cisplatin regimens. Further, using only the urinary Kim-1 response to gentamicin (a nephrotoxicant with a distinctly different injury time course than cisplatin), the model detected and predicted mild to moderate PT injury, as confirmed with histopathology, even when serum creatinine was unchanged. Thus, the model is generalizable, and can be used to deconvolute the underlying degree and time course of drug-induced PT injury and renal dysfunction from a small number of urinary biomarkers, and may provide a tool to determine optimal dosing regimens that minimize renal injury.

Investigating nephrotoxicity of polymyxin derivatives by mapping renal distribution using mass spectrometry imaging.

Colistin and polymyxin B are effective treatment options for Gram-negative resistant bacteria but are used as last-line therapy due to their dose-limiting nephrotoxicity. A critical factor in developing safer polymyxin analogues is understanding accumulation of the drugs and their metabolites, which is currently limited due to the lack of effective techniques for analysis of these challenging molecules. Mass spectrometry imaging (MSI) allows direct detection of targets (drugs, metabolites, and endogenous compounds) from tissue sections. The presented study exemplifies the utility of MSI by measuring the distribution of polymyxin B1, colistin, and polymyxin B nonapeptide (PMBN) within dosed rat kidney tissue sections. The label-free MSI analysis revealed that the nephrotoxic compounds (polymyxin B1 and colistin) preferentially accumulated in the renal cortical region. The less nephrotoxic analogue, polymyxin B nonapeptide, was more uniformly distributed throughout the kidney. In addition, metabolites of the dosed compounds were detected by MSI. Kidney homogenates were analyzed using LC/MS/MS to determine total drug exposure and for metabolite identification. To our knowledge, this is the first time such techniques have been utilized to measure the distribution of polymyxin drugs and their metabolites. By simultaneously detecting the distribution of drug and drug metabolites, MSI offers a powerful alternative to tissue homogenization analysis and label or antibody-based imaging.

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells.

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38 MAPK, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, the first three members of HER family (HER1-3) can form homo- and hetero-dimers, and there is considerable evidence suggesting that the receptor dimers differentially activate intracellular signaling pathways. To better understand the interactions in this system, we pursued multi-factorial experiments where HER dimerization patterns and signaling pathways were rationally perturbed. We measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38 MAPK, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. We hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated this hypothesis using a combination of model-based analysis to quantify the HER dimerization patterns, and by clustering the activation data in multiple ways to confirm that the HER receptor dimer is a better predictor of the signaling through p38 MAPK, ERK and AKT pathways than the total HER receptor expression and activation levels. We then pursued combinatorial inhibition studies to identify the causal regulatory interactions between sentinel signaling proteins. Quantitative analysis of the collected data using the modular response analysis (MRA) and its Bayesian Variable Selection Algorithm (BVSA) version allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways in HME cells. Results of the BVSA/MRA and cluster analysis were in agreement with each other.

ERK oscillation-dependent gene expression patterns and deregulation by stress response.

Studies were undertaken to determine whether extracellular signal regulated kinase (ERK) oscillations regulate a unique subset of genes in human keratinocytes and subsequently whether the p38 stress response inhibits ERK oscillations. A DNA microarray identified many genes that were unique to ERK oscillations, and network reconstruction predicted an important role for the mediator complex subunit 1 (MED1) node in mediating ERK oscillation-dependent gene expression. Increased ERK-dependent phosphorylation of MED1 was observed in oscillating cells compared to nonoscillating counterparts as validation. Treatment of keratinocytes with a p38 inhibitor (SB203580) increased ERK oscillation amplitudes and MED1 and phospho-MED1 protein levels. Bromate is a probable human carcinogen that activates p38. Bromate inhibited ERK oscillations in human keratinocytes and JB6 cells and induced an increase in phospho-p38 and a decrease in phospho-MED1 protein levels. Treatment of normal rat kidney cells and primary salivary gland epithelial cells with bromate decreased phospho-MED1 levels in a reversible fashion upon treatment with p38 inhibitors (SB202190; SB203580). Our results indicate that oscillatory behavior in the ERK pathway alters homeostatic gene regulation patterns and that the cellular response to perturbation may manifest differently in oscillating vs nonoscillating cells.

Model-based analysis of HER activation in cells co-expressing EGFR, HER2 and HER3.

The HER/ErbB family of receptor tyrosine kinases drives critical responses in normal physiology and cancer, and the expression levels of the various HER receptors are critical determinants of clinical outcomes. HER activation is driven by the formation of various dimer complexes between members of this receptor family. The HER dimer types can have differential effects on downstream signaling and phenotypic outcomes. We constructed an integrated mathematical model of HER activation, and trafficking to quantitatively link receptor expression levels to dimerization and activation. We parameterized the model with a comprehensive set of HER phosphorylation and abundance data collected in a panel of human mammary epithelial cells expressing varying levels of EGFR/HER1, HER2 and HER3. Although parameter estimation yielded multiple solutions, predictions for dimer phosphorylation were in agreement with each other. We validated the model using experiments where pertuzumab was used to block HER2 dimerization. We used the model to predict HER dimerization and activation patterns in a panel of human mammary epithelial cells lines with known HER expression levels in response to stimulations with ligands EGF and HRG. Simulations over the range of expression levels seen in various cell lines indicate that: i) EGFR phosphorylation is driven by HER1-HER1 and HER1-HER2 dimers, and not HER1-HER3 dimers, ii) HER1-HER2 and HER2-HER3 dimers both contribute significantly to HER2 activation with the EGFR expression level determining the relative importance of these species, and iii) the HER2-HER3 dimer is largely responsible for HER3 activation. The model can be used to predict phosphorylated dimer levels for any given HER expression profile. This information in turn can be used to quantify the potencies of the various HER dimers, and can potentially inform personalized therapeutic approaches.

Integrated experimental and computational approach to understand the effects of heavy ion radiation on skin homeostasis.

The effects of low dose high linear energy transfer (LET) radiation on human health are of concern for space, occupational, and clinical exposures. As epidemiological data for such radiation exposures are scarce for making relevant predictions, we need to understand the mechanism of response especially in normal tissues. Our objective here is to understand the effects of heavy ion radiation on tissue homeostasis in a realistic model system. Towards this end, we exposed an in vitro three dimensional skin equivalent to low fluences of neon (Ne) ions (300 MeV u(-1)), and determined the differentiation profile as a function of time following exposure using immunohistochemistry. We found that Ne ion exposures resulted in transient increases in the tissue regions expressing the differentiation markers keratin 10, and filaggrin, and more subtle time-dependent effects on the number of basal cells in the epidermis. We analyzed the data using a mathematical model of the skin equivalent, to quantify the effect of radiation on cell proliferation and differentiation. The agent-based mathematical model for the epidermal layer treats the epidermis as a collection of heterogeneous cell types with different proliferation-differentiation properties. We obtained model parameters from the literature where available, and calibrated the unknown parameters to match the observed properties in unirradiated skin. We then used the model to rigorously examine alternate hypotheses regarding the effects of high LET radiation on the tissue. Our analysis indicates that Ne ion exposures induce rapid, but transient, changes in cell division, differentiation and proliferation. We have validated the modeling results by histology and quantitative reverse transcription polymerase chain reaction (qRT-PCR). The integrated approach presented here can be used as a general framework to understand the responses of multicellular systems, and can be adapted to other epithelial tissues.

Physiologically-based pharmacokinetic model for Fentanyl in support of the development of Provisional Advisory Levels.

Provisional Advisory Levels (PALs) are tiered exposure limits for toxic chemicals in air and drinking water that are developed to assist in emergency responses. Physiologically-based pharmacokinetic (PBPK) modeling can support this process by enabling extrapolations across doses, and exposure routes, thereby addressing gaps in the available toxicity data. Here, we describe the development of a PBPK model for Fentanyl - a synthetic opioid used clinically for pain management - to support the establishment of PALs. Starting from an existing model for intravenous Fentanyl, we first optimized distribution and clearance parameters using several additional IV datasets. We then calibrated the model using pharmacokinetic data for various formulations, and determined the absorbed fraction, F, and time taken for the absorbed amount to reach 90% of its final value, t90. For aerosolized pulmonary Fentanyl, F=1 and t90<1 min indicating complete and rapid absorption. The F value ranged from 0.35 to 0.74 for oral and various transmucosal routes. Oral Fentanyl was absorbed the slowest (t90~300 min); the absorption of intranasal Fentanyl was relatively rapid (t90~20-40 min); and the various oral transmucosal routes had intermediate absorption rates (t90~160-300 min). Based on these results, for inhalation exposures, we assumed that all of the Fentanyl inhaled from the air during each breath directly, and instantaneously enters the arterial circulation. We present model predictions of Fentanyl blood concentrations in oral and inhalation scenarios relevant for PAL development, and provide an analytical expression that can be used to extrapolate between oral and inhalation routes for the derivation of PALs.

Modeling dynamic regulatory processes in stroke.

The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics.

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models to determine the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: (1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, (2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and (3) receptor phosphorylation kinetics at the cell surface and early endosomes are comparable. We validated the last finding by measuring the EGFR dephosphorylation rates at various times following ligand addition both in whole cells and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.

Using imaging methods to interrogate radiation-induced cell signaling.

There is increasing emphasis on the use of systems biology approaches to define radiation-induced responses in cells and tissues. Such approaches frequently rely on global screening using various high throughput 'omics' platforms. Although these methods are ideal for obtaining an unbiased overview of cellular responses, they often cannot reflect the inherent heterogeneity of the system or provide detailed spatial information. Additionally, performing such studies with multiple sampling time points can be prohibitively expensive. Imaging provides a complementary method with high spatial and temporal resolution capable of following the dynamics of signaling processes. In this review, we utilize specific examples to illustrate how imaging approaches have furthered our understanding of radiation-induced cellular signaling. Particular emphasis is placed on protein colocalization, and oscillatory and transient signaling dynamics.

Cell type-dependent gene transcription profile in a three-dimensional human skin tissue model exposed to low doses of ionizing radiation: implications for medical exposures.

The concern over possible health risks from exposures to low doses of ionizing radiation has been driven largely by the increase in medical exposures, the routine implementation of X-ray backscatter devices for airport security screening, and, most recently, the nuclear incident in Japan. Because of a paucity of direct epidemiological data at very low doses, cancer risk must be estimated from high dose exposure scenarios. However, there is increasing evidence that low and high dose exposures result in different signaling events and may have different response mechanisms than higher doses. We have examined the radiation-induced temporal response after exposure to 10 cGy of an in vitro three dimensional (3D) human skin tissue model using microarray-based transcriptional profiling. Cell type-specific analysis showed significant changes in gene expression with the levels of >1,400 genes altered in the dermis and >400 genes regulated in the epidermis. The two cell layers rarely exhibited overlapping responses at the mRNA level. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) measurements validated the microarray data in both regulation direction and value. Key pathways identified relate to cell cycle regulation, immune responses, hypoxia, reactive oxygen signaling, and DNA damage repair. The proliferation status as well as the expression of PCNA was examined in histological samples. We discuss in particular the role of proliferation, emphasizing how the disregulation of cellular signaling in normal tissue may impact progression toward radiation-induced secondary diseases.

Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems.

BACKGROUND: Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades. RESULTS: In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms. CONCLUSIONS: This is the first demonstration of a global regulatory network modeling conserved host response between in vitro and in vivo models.

Inhibition of ERK oscillations by ionizing radiation and reactive oxygen species.

The shuttling of activated protein kinases between the cytoplasm and nucleus is an essential feature of normal growth factor signaling cascades. Here we demonstrate that transforming growth factor alpha (TGFα) induces oscillations in extracellular signal regulated kinase (ERK) cytoplasmic-nuclear translocations in human keratinocytes. TGFα-dependent ERK oscillations mediated through the epidermal growth factor receptor (EGFR) are inhibited by low dose X-irradiation (10 cGy) and low concentrations of hydrogen peroxide (0.32-3.26 µM H(2)O(2)) used as a model reactive oxygen species (ROS). A fluorescent indicator dye (H2-DCFDA) was used to measure cellular ROS levels following X-irradiation, 12-O-tetradecanoyl phorbol-13-acetate (TPA) and H(2)O(2). X-irradiation did not generate significant ROS production while 0.32 µM H(2)O(2) and TPA induced significant increases in ROS levels with H(2)O(2) > TPA. TPA alone induced transactivation of the EGFR but did not induce ERK oscillations. TPA as a cotreatment did not inhibit TGFα-stimulated ERK oscillations but qualitatively altered TGFα-dependent ERK oscillation characteristics (amplitude, time-period). Collectively, these observations demonstrate that TGFα-induced ERK oscillations are inhibited by ionizing radiation/ROS and perturbed by epigenetic carcinogen in human keratinocytes.

Spatial aspects in biological system simulations.

Mathematical models of the dynamical properties of biological systems aim to improve our understanding of the studied system with the ultimate goal of being able to predict system responses in the absence of experimentation. Despite the enormous advances that have been made in biological modeling and simulation, the inherently multiscale character of biological systems and the stochasticity of biological processes continue to present significant computational and conceptual challenges. Biological systems often consist of well-organized structural hierarchies, which inevitably lead to multiscale problems. This chapter introduces and discusses the advantages and shortcomings of several simulation methods that are being used by the scientific community to investigate the spatiotemporal properties of model biological systems. We first describe the foundations of the methods and then describe their relevance and possible application areas with illustrative examples from our own research. Possible ways to address the encountered computational difficulties are also discussed.