Pain-related and psychological factors mediate the effect of personality on health-related quality of life. A study in breast cancer survivors with persistent pain.

Introduction: Personality characteristics affect the long-term well-being and health-related quality of life (HrQoL) of breast cancer (BC) survivors. Persistent pain significantly affects psychosocial well-being and HrQoL in this patient group. We studied the effects of temperament and character via pain-related and psychological factors on dimensions of HrQoL in BC survivors. Methods: We studied 273 patients who had been treated for BC and who reported persistent pain at any site of the body in Brief Pain Inventory. The patients were recruited from a longitudinal cohort of patients 4-9 years after surgery for BC. Short-Form-36 inventory was used to assess physical and mental dimensions of HrQoL and Temperament and Character Inventory to assess dimensions of temperament and character. We used parallel mediation modeling for studying effects of temperament and character on physical and mental HrQoL. Results: A significant total effect was found for harm avoidance (HA) temperament (ßtotal = -0.665, p < 0.001) and character dimensions self-directedness (SD) (ßtotal = 0.609, p = 0.001) and cooperativeness (CO) (ßtotal = 0.584, p = 0.028) on physical and mental HrQoL. Additionally, different combinations of pain-related and psychological variables fully mediated the indirect effects of HA, SD, and CO on physical and mental HrQoL. Discussion: HA temperament is a potential emotional vulnerability factor for psychological burden and impaired HrQoL in BC survivors. Character dimensions SD and CO may protect from the negative effect of mood on HrQoL. The results provide new insights about the risk-and target-factors for clinical interventions and effective pain management to improve psychosocial well-being and HrQoL in BC survivors.

Temperament and character dimensions differ in chronic post-surgical neuropathic pain and cold pressure pain.

OBJECTIVES: Psychobiological temperament and cognitive-evaluative character link to coping with chronic pain. The aim was to study possible independent role of temperament and character dimensions both in chronic and experimental pain in chronic post-surgical pain patients. This is a substudy of a previously published larger cohort of patients with intercostobrachial nerve injury after breast cancer surgery. METHODS: We recruited 241 women who had been treated for breast cancer 4-9 years before. They had a surgeon-verified intercostobrachial nerve injury with or without chronic post-surgical neuropathic pain (CPSNP). The patients filled in the Temperament and Character Inventory (TCI), Pain Catastrophizing Scale (PCS), Hospital Anxiety and Depression Scale (HADS), and Brief Pain Inventory (BPI), and underwent the cold pressor test (CPT). RESULTS: 201 (83%) patients reported chronic pain and 135 (56%) met the criteria for CPSNP. Patients with CPSNP showed higher levels of Harm Avoidance (HA) temperament than non-CPSNP patients, which was associated with lower cold pain tolerance and greater increase of pain intensity during CPT. HA subscales Fear of Uncertainty and Fatigability contributed to a stronger pain experience. For character dimensions, CPSNP patients reported higher levels of Self-Transcendence (ST) and lower levels of Self-Directedness (SD) and Cooperativeness (CO) than non-CPSNP patients. Cold pain tolerance, intensity, or unpleasantness did not associate with character dimensions. CONCLUSIONS: Psychobiological temperament, but not character, is independently from other psychological factors associated with primary pain processing in an experimental pain setting. Patients with and without CPSNP showed different profiles on both temperament and character dimensions suggesting a combination of heightened emotional vulnerability and lowered personality adaptability in CPSNP patients. Character dimensions associated with clinical but not experimental pain. ETHICAL COMMITTEE NUMBER: The study protocol was approved by the Ethics Committee of the Helsinki and Uusimaa Hospital District (reference number: 149/13/03/00/14). TRIAL REGISTRY NUMBER: The study is registered in ClinicalTrials.gov (NCT02487524).

Static mechanical allodynia in post-surgical neuropathic pain after breast cancer treatments.

Objectives Static mechanical allodynia (SMA), i. e., pain caused by normally non-painful static pressure, is a prevalent manifestation of neuropathic pain (NP). Although SMA may significantly affect the patient's daily life, it is less well studied in the clinical context. We aimed to characterize SMA in women with chronic post-surgical NP (CPSNP) after breast cancer surgery. Our objective was to improve understanding of the clinical picture of this prevalent pain condition. This is a substudy of a previously published larger cohort of patients with intercostobrachial nerve injury after breast cancer surgery (Mustonen et al. Pain. 2019;160:246-56). Methods We studied SMA in 132 patients with CPSNP after breast cancer surgery. The presence, location, and intensity of SMA were assessed at clinical sensory examination. The patients gave self-reports of pain with the Brief Pain Inventory (BPI). We studied the association of SMA to type of surgery, oncological treatments, BMI, other pains, and psychological factors. General pain sensitivity was assessed by the cold pressor test. Results SMA was prevalent (84%) in this cohort whereas other forms of allodynia were scarce (6%). Moderate-to-severe SMA was frequently observed even in patients who reported mild pain in BPI. Breast and the side of chest were the most common locations of SMA. SMA was associated with breast surgery type, but not with psychological factors. Severe SMA, but not self-reported pain, was associated with lower cold pain tolerance. Conclusions SMA is prevalent in post-surgical NP after breast cancer surgery and it may represent a distinct NP phenotype. High intensities of SMA may signal the presence of central sensitization. Implications SMA should be considered when examining and treating patients with post-surgical NP after breast cancer surgery.

Psychological resilience associates with pain experience in women treated for breast cancer.

Background and aims Psychological resilience refers to successful adaptation or a positive outcome in the context of significant life adversity, such as chronic pain. On the other hand, anxiety closely associates with pain. The aim of this study was to explore how anxiety and psychological resilience together associate with persistent and experimental pain. Methods In a cross-sectional design, we studied 160 patients who had previously been treated for breast cancer and who now reported at least moderate pain (NRS ≥ 4) in any area of the body. Psychological resilience was measured on the Resilience Scale-14, anxiety on the Hospital Anxiety and Depression Scale, and intensity and interference of persistent pain by means of the Brief Pain Inventory. The cold pressor test was conducted to assess sensitivity to experimental cold pain. Results The results showed that resilience associated with pain interference in persistent pain, and that anxiety moderated this effect. Higher psychological resilience was associated with lower pain interference and this association was stronger in patients with low anxiety than among patients with high anxiety. These effects were visible with regard to persistent pain but not in experimental cold pain. Conclusions These results indicate that chronic pain and experimental pain as well as pain severity and pain interference are psychologically different phenomena. Psychological resilience protects against pain interference but effectively only in patients with low anxiety. It is necessary also to consider protective factors in addition to vulnerability factors in cases of persistent pain. Implications Resilience has been considered a potential target for intervention in chronic pain. However, high levels of anxiety might diminish the protective effect of psychological resilience in clinical settings. Therefore, it is important to treat anxiety in addition to resilience enhancing interventions. Patients with low psychological distress might be more suitable for resilience enhancing interventions than patients with high anxiety.

Douleur Neuropathique 4 (DN4) stratifies possible and definite neuropathic pain after surgical peripheral nerve lesion.

BACKGROUND: Douleur Neuropathique 4 (DN4) is a screening questionnaire to help identify neuropathic pain (NP) in clinical practice and research. We tested the accuracy of the DN4 questionnaire in stratifying possible NP (pNP) and definite NP (dNP) in patients operated for breast cancer. METHODS: We studied 163 patients from a longitudinal cohort of breast cancer operated patients 4-9 years after surgery. pNP or dNP were classified according to the NP grading system. Surgeon-verified intercostobrachial nerve resection was used as a confirmatory test for dNP. A receiver-operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated to test the diagnostic accuracy (sensitivity, specificity, positive and negative predictive values) of the DN4. Additionally, we studied clinical factors that associated with a positive screening outcome in the interview part of the DN4 (DN4i). RESULTS: DN4i and DN4 showed significant accuracy in stratifying patients with pNP or dNP with cut-off scores 3 and 4 resulting to sensitivity of 66.2% and 79.4% and specificity of 77.8% and 92.6%, respectively. pNP and dNP patients showed differences in sensory descriptors of pain according to DN4i items. Screening positive on DN4i associated with dNP and younger age. CONCLUSIONS: Full DN4 could stratify pNP and dNP patients in a chronic postsurgical NP patient group operated for breast cancer. Additionally, DN4i showed significant accuracy in stratifying pNP and dNP, but an examination is necessary to obtain proper accuracy. Demographic factors may have an impact on the screening outcome of DN4i. SIGNIFICANCE: DN4 stratifies possible and definite postsurgical peripheral neuropathic pain. DN4i may also show this, but full DN4 is more accurate. We confirm DN4i as a valid screening tool for NP.

What makes surgical nerve injury painful? A 4-year to 9-year follow-up of patients with intercostobrachial nerve resection in women treated for breast cancer.

Nerve injury during breast cancer surgery can cause neuropathic pain (NP). It is not known why some, but not all, patients develop chronic postsurgical neuropathic pain (CPSNP) after the same nerve injury. In this study, we examined 251 breast cancer survivors with surgeon-verified intercostobrachial nerve resection to identify factors that associate with CPSNP. The patients were recruited from a previous study of 1000 women treated for breast cancer in 2006 to 2010. This enabled us to analyze preoperative factors that associate with future CPSNP. The patients were re-examined in 2014 to 2016 to diagnose CPSNP using the revised NP diagnostic criteria. Preoperative assessments were pain in the area to be operated on, any chronic pain condition, depressive symptoms, anxiety, sleep, and experimental cold pain sensitivity using the cold pressor test (CPT). Follow-up assessments were CPT, psychological factors, sleep, any chronic pain, and basic laboratory tests. One hundred thirty-seven (55%) patients with intercostobrachial nerve resection fulfilled CPSNP diagnostic criteria after 4 to 9 years. Of them, 30 patients (22%) had moderate to severe pain in self-reports and 86 (63%) presented moderate to severe evoked pain at examination. Preoperative pain in the surgical area, other chronic pains, and breast-conserving surgery were associated with future CPSNP. Other chronic pains, increased psychological burden, and insomnia, both before surgery and at the follow-up, were associated with CPSNP. Preoperative CPT did not associate with future CPSNP. Patients with established CPSNP showed increased pain sensitivity in CPT and higher levels of inflammatory markers, suggesting that central sensitization and inflammation may associate with the maintenance of CPSNP.

Changes in global gene expression of Vibrio parahaemolyticus induced by cold- and heat-stress.

BACKGROUND: Vibrio (V.) parahaemolyticus causes seafood-borne gastro-intestinal bacterial infections in humans worldwide. It is widely found in marine environments and is isolated frequently from seawater, estuarine waters, sediments and raw or insufficiently cooked seafood. Throughout the food chain, V. parahaemolyticus encounters different temperature conditions that might alter metabolism and pathogenicity of the bacterium. In this study, we performed gene expression profiling of V. parahaemolyticus RIMD 2210633 after exposure to 4, 15, 20, 37 and 42 °C to describe the cold and heat shock response. METHODS: Gene expression profiles of V. parahaemolyticus RIMD 2210633 after exposure to 4, 15, 20, 37 and 42 °C were investigated via microarray. Gene expression values and RT-qPCR experiments were compared by plotting the log2 values. Moreover, volcano plots of microarray data were calculated to visualize the distribution of differentially expressed genes at individual temperatures and to assess hybridization qualities and comparability of data. Finally, enriched terms were searched in annotations as well as functional-related gene categories using the Database for Annotation, Visualization and Integrated Discovery. RESULTS: Analysis of 37 °C normalised transcriptomics data resulted in differential expression of 19 genes at 20 °C, 193 genes at 4 °C, 625 genes at 42 °C and 638 genes at 15 °C. Thus, the largest number of significantly expressed genes was observed at 15 and 42 °C with 13.3 and 13%, respectively. Genes of many functional categories were highly regulated even at lower temperatures. Virulence associated genes (tdh1, tdh2, toxR, toxS, vopC, T6SS-1, T6SS-2) remained mostly unaffected by heat or cold stress. CONCLUSION: Along with folding and temperature shock depending systems, an overall temperature-dependent regulation of expression could be shown. Particularly the energy metabolism was affected by changed temperatures. Whole-genome gene expression studies of food related pathogens such as V. parahaemolyticus reveal how these pathogens react to stress impacts to predict its behaviour under conditions like storage and transport.

Gene expression profiles of Vibrio parahaemolyticus in viable but non-culturable state.

Viable but non-culturable (VBNC) state is referred to as a dormant state of non-sporulating bacteria enhancing the survival in adverse environments. To our knowledge, only few studies have been conducted on whole genomic expression of Vibrio parahaemolyticus VBNC state. Since a degradation of nucleic acids in V. vulnificus non-culturable state has been detected, we hypothesize that gene regulation of VBNC cells is highly reduced, downregulation of gene expression is dominant and only metabolic functions crucial for survival are kept on a sustained basis. Hence, we performed the whole transcriptomic profiles of V. parahaemolyticus in three phases (exponential, early stationary phase and VBNC state). Compared with exponential and early stationary phase, in V. parahaemolyticus VBNC cells we found 509 induced genes and 309 repressed by more than 4-fold among 4820 investigated genes. Upregulation was dominant in most of non-metabolism functional categories, while five metabolism-related functional categories revealed downregulation in VBNC state. To our knowledge, this is the first study of comprehensive transcriptomic analyses of three phases of V. parahaemolyticus RIMD2210633. Although the mechanism of VBNC state is not yet clear, massive regulation of gene expression occurs in VBNC state compared with expression in other two phases, indicating VBNC cells are active.

Metabolic engineering of Acinetobacter baylyi ADP1 for improved growth on gluconate and glucose.

A high growth rate in bacterial cultures is usually achieved by optimizing growth conditions, but metabolism of the bacterium limits the maximal growth rate attainable on the carbon source used. This limitation can be circumvented by engineering the metabolism of the bacterium. Acinetobacter baylyi has become a model organism for studies of bacterial metabolism and metabolic engineering due to its wide substrate spectrum and easy-to-engineer genome. It produces naturally storage lipids, such as wax esters, and has a unique gluconate catabolism as it lacks a gene for pyruvate kinase. We engineered the central metabolism of A. baylyi ADP1 more favorable for gluconate catabolism by expressing the pyruvate kinase gene (pykF) of Escherichia coli. This modification increased growth rate when cultivated on gluconate or glucose as a sole carbon source in a batch cultivation. The engineered cells reached stationary phase on these carbon sources approximately twice as fast as control cells carrying an empty plasmid and produced similar amount of biomass. Furthermore, when grown on either gluconate or glucose, pykF expression did not lead to significant accumulation of overflow metabolites and consumption of the substrate remained unaltered. Increased growth rate on glucose was not accompanied with decreased wax ester production, and the pykF-expressing cells accumulated significantly more of these storage lipids with respect to cultivation time.

Bioprocess data mining using regularized regression and random forests.

BACKGROUND: In bioprocess development, the needs of data analysis include (1) getting overview to existing data sets, (2) identifying primary control parameters, (3) determining a useful control direction, and (4) planning future experiments. In particular, the integration of multiple data sets causes that these needs cannot be properly addressed by regression models that assume linear input-output relationship or unimodality of the response function. Regularized regression and random forests, on the other hand, have several properties that may appear important in this context. They are capable, e.g., in handling small number of samples with respect to the number of variables, feature selection, and the visualization of response surfaces in order to present the prediction results in an illustrative way. RESULTS: In this work, the applicability of regularized regression (Lasso) and random forests (RF) in bioprocess data mining was examined, and their performance was benchmarked against multiple linear regression. As an example, we used data from a culture media optimization study for microbial hydrogen production. All the three methods were capable in providing a significant model when the five variables of the culture media optimization were linearly included in modeling. However, multiple linear regression failed when also the multiplications and squares of the variables were included in modeling. In this case, the modeling was still successful with Lasso (correlation between the observed and predicted yield was 0.69) and RF (0.91). CONCLUSION: We found that both regularized regression and random forests were able to produce feasible models, and the latter was efficient in capturing the non-linearity in the data. In this kind of a data mining task of bioprocess data, both methods outperform multiple linear regression.

Heat stress adaptation induces cross-protection against lethal acid stress conditions in Arcobacter butzleri but not in Campylobacter jejuni.

The ability of many bacteria to adapt to stressful conditions may later protect them against the same type of stress (specific adaptive response) or different types of stresses (multiple adaptive response, also termed cross-protection). Arcobacter butzleri and Campylobacter jejuni are close phylogenetic relatives that occur in many foods of animal origin and have been linked with human illness (mainly diarrhoea). In the present study, sublethal stress adaptation temperatures (48 °C and 10 °C) and mild and lethal acid conditions (pH 5.0 and pH 4.0) were determined for A. butzleri and C. jejuni. In addition, it was evaluated whether these sublethal stress adaptations cause specific adaptive responses or cross-protection against subsequent mild or lethal acid stresses in these bacteria. The studies were conducted in broth adjusted to the different conditions and the results were determined by the dilution series plating method. It was shown that heat stress adapted A. butzleri (incubated for 2 h at 48 °C) were significantly more resistant to subsequent lethal acid stress (pH 4.0) than non-adapted cells at the 1 h time-point (p < 0.01 in Wilcoxon rank sum test). No specific adaptive responses against the stresses in A. butzleri or C. jejuni and no cross-protection in C. jejuni were found. The ability of heat stressed A. butzleri to tolerate later lethal acid conditions should be taken into account when designing new food decontamination and processing strategies.

Improved triacylglycerol production in Acinetobacter baylyi ADP1 by metabolic engineering.

BACKGROUND: Triacylglycerols are used in various purposes including food applications, cosmetics, oleochemicals and biofuels. Currently the main sources for triacylglycerol are vegetable oils, and microbial triacylglycerol has been suggested as an alternative for these. Due to the low production rates and yields of microbial processes, the role of metabolic engineering has become more significant. As a robust model organism for genetic and metabolic studies, and for the natural capability to produce triacylglycerol, Acinetobacter baylyi ADP1 serves as an excellent organism for modelling the effects of metabolic engineering for energy molecule biosynthesis. RESULTS: Beneficial gene deletions regarding triacylglycerol production were screened by computational means exploiting the metabolic model of ADP1. Four deletions, acr1, poxB, dgkA, and a triacylglycerol lipase were chosen to be studied experimentally both separately and concurrently by constructing a knock-out strain (MT) with three of the deletions. Improvements in triacylglycerol production were observed: the strain MT produced 5.6 fold more triacylglycerol (mg/g cell dry weight) compared to the wild type strain, and the proportion of triacylglycerol in total lipids was increased by 8-fold. CONCLUSIONS: In silico predictions of beneficial gene deletions were verified experimentally. The chosen single and multiple gene deletions affected beneficially the natural triacylglycerol metabolism of A. baylyi ADP1. This study demonstrates the importance of single gene deletions in triacylglycerol metabolism, and proposes Acinetobacter sp. ADP1 as a model system for bioenergetic studies regarding metabolic engineering.

Storage of environmental samples for guaranteeing nucleic acid yields for molecular microbiological studies.

The purpose of this study is to evaluate whether sample preservation can affect the yield of nucleic acid extracts from environmental samples. Storage of microbial samples was studied using three sediment types of varying carbon contents (10-57% carbon of dry weight). Four different storage solutions were tested at three temperatures. Freezing of samples at -20 °C or -80 °C, either without preservative or in phenol-chloroform solution, retained nucleic acid quantities very efficiently. Storage of samples in phenol-chloroform solution at +4 °C also gave good yields except for sediment with extremely high-carbon content. Ethanol and RNAlater preservation decreased nucleic acid yields drastically at all temperatures. To study how sample preservation may affect the result of microbial community analysis, one type of sediment was selected for length heterogeneity-PCR analysis and PCR cloning of the 16S rRNA genes. Ethanol and RNAlater preservation caused a slight bias towards certain microbial types in the community analyses shown by underrepresentation of Bacteroidetes, Betaproteobacteria and Gammaproteobacteria-affiliated peak sizes and overrepresentation of Actinobacteria, Chloroflexi and Alphaproteobacteria-affiliated peak sizes. Based on the results of this study, preservation in phenol-chloroform solution can be recommended as an alternative storage method when freezing is not possible such as during extended field sampling; however, ethanol and RNAlater may cause serious problems when used as preservatives for environmental samples containing humic acids.

Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.

Critical networks exhibit maximal information diversity in structure-dynamics relationships.

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and show that these relationships are most diverse in the critical regime.

RMBNToolbox: random models for biochemical networks.

BACKGROUND: There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. RESULTS: We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. CONCLUSION: While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Computational methods for estimation of cell cycle phase distributions of yeast cells.

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/http://www.cs.tut.fi/sgn/csb/yeastdistrib/.

Simulation of microarray data with realistic characteristics.

BACKGROUND: Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed. RESULTS: We present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples. CONCLUSION: The proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.

Simulation tools for biochemical networks: evaluation of performance and usability.

MOTIVATION: Simulation of dynamic biochemical systems is receiving considerable attention due to increasing availability of experimental data of complex cellular functions. Numerous simulation tools have been developed for numerical simulation of the behavior of a system described in mathematical form. However, there exist only a few evaluation studies of these tools. Knowledge of the properties and capabilities of the simulation tools would help bioscientists in building models based on experimental data. RESULTS: We examine selected simulation tools that are intended for the simulation of biochemical systems. We choose four of them for more detailed study and perform time series simulations using a specific pathway describing the concentration of the active form of protein kinase C. We conclude that the simulation results are convergent between the chosen simulation tools. However, the tools differ in their usability, support for data transfer to other programs and support for automatic parameter estimation. From the experimentalists' point of view, all these are properties that need to be emphasized in the future.