Global screening of potential Candida albicans biofilm-related transcription factors via network comparison

Molecular Determinants Involved in Candida albicans Biofilm Formation and Regulation

Candida albicans is known for its pathogenicity, although it lives within the human body as a commensal member. The commensal nature of C. albicans is well controlled and regulated by the host's immune system as they live in the harmonized microenvironment. However, the development of certain unusual microhabitat conditions (change in pH, co-inhabiting microorganisms' population ratio, debilitated host-immune system) pokes this commensal fungus to transform into a pathogen in such a way that it starts to propagate very rapidly and tries to breach the epithelial barrier to enter the host's systemic circulations. In addition, Candida is infamous as a major nosocomial (hospital-acquired infection) agent because it enters the human body through venous catheters or medical prostheses. The hysterical mode of C. albicans growth builds its microcolony or biofilm, which is pathogenic for the host. Biofilms propose additional resistance mechanisms from host immunity or extracellular chemicals to aid their survival. Differential gene expressions and regulations within the biofilms cause altered morphology and metabolism. The genes associated with adhesiveness, hyphal/pseudo-hyphal growth, persister cell transformation, and biofilm formation by C. albicans are controlled by myriads of cell-signaling regulators. These genes' transcription is controlled by different molecular determinants like transcription factors and regulators. Therefore, this review has focused discussion on host-immune-sensing molecular determinants of Candida during biofilm formation, regulatory descriptors (secondary messengers, regulatory RNAs, transcription factors) of Candida involved in biofilm formation that could enable small-molecule drug discovery against these molecular determinants, and lead to disrupt the well-structured Candida biofilms effectively.

Associations of Rap1 with Cell Wall Integrity, Biofilm Formation, and Virulence in Candida albicans

Rap1 (repressor activator protein 1) is a multifunctional protein, playing important roles in telomeric and nontelomeric functions in many eukaryotes. Candida albicans Rap1 has been previously shown to be involved in telomeric regulation, but its other functions are still mostly unknown. In this study, we found that the deletion of the RAP1 gene altered cell wall properties, composition, and gene expression. In addition, deletion of RAP1 affected C. albicans biofilm formation and modulated phagocytosis and cytokine release by host immune cells. Finally, the RAP1 gene deletion mutant showed attenuation of C. albicans virulence in a Galleria mellonella infection model. Therefore, these findings provide new insights into Rap1 functions that are particularly relevant to pathogenesis and virulence of C. albicans. IMPORTANCE C. albicans is an important fungal pathogen of humans. The cell wall is the outermost layer of C. albicans and is important for commensalism and infection by this pathogen. Moreover, the cell wall is also an important target for antifungals. Studies of how C. albicans maintains its cell wall integrity are critical for a better understanding of fungal pathogenesis and virulence. This work focuses on exploring unknown functions of C. albicans Rap1 and reveals its contribution to cell wall integrity, biofilm formation, and virulence. Notably, these findings will also improve our general understanding of complex machinery to control pathogenesis and virulence of fungal pathogens.

Investigating Common Pathogenic Mechanisms between Homo sapiens and Different Strains of Candida albicans for Drug Design: Systems Biology Approach via Two-Sided NGS Data Identification

Candida albicans (C. albicans) is the most prevalent fungal species. Although it is a healthy microbiota, genetic and epigenetic alterations in host and pathogen, and microenvironment changes would lead to thrush, vaginal yeast infection, and even hematogenously disseminated infection. Despite the fact that cytotoxicity is well-characterized, few studies discuss the genome-wide genetic and epigenetic molecular mechanisms between host and C. albicans. The aim of this study is to identify drug targets and design a multiple-molecule drug to prevent the infection from C. albicans. To investigate the common and specific pathogenic mechanisms in human oral epithelial OKF6/TERT-2 cells during the C. albicans infection in different strains, systems modeling and big databases mining were used to construct candidate host–pathogen genetic and epigenetic interspecies network (GEIN). System identification and system order detection are applied on two-sided next generation sequencing (NGS) data to build real host–pathogen cross-talk GEINs. Core host–pathogen cross-talk networks (HPCNs) are extracted by principal network projection (PNP) method. By comparing with core HPCNs in different strains of C. albicans, common pathogenic mechanisms were investigated and several drug targets were suggested as follows: orf19.5034 (YBP1) with the ability of anti-ROS; orf19.939 (NAM7), orf19.2087 (SAS2), orf19.1093 (FLO8) and orf19.1854 (HHF22) with high correlation to the hyphae growth and pathogen protein interaction; orf19.5585 (SAP5), orf19.5542 (SAP6) and orf19.4519 (SUV3) with the cause of biofilm formation. Eventually, five corresponding compounds—Tunicamycin, Terbinafine, Cerulenin, Tetracycline and Tetrandrine—with three known drugs could be considered as a potential multiple-molecule drug for therapeutic treatment of C. albicans.

The possible molecular mechanisms of farnesol on the antifungal resistance of C. albicans biofilms: the regulation of CYR1 and PDE2

Background

Farnesol has potential antifungal activity against Candida albicans biofilms, but the molecular mechanism of this activity is still unclear. Farnesol inhibits hyphal growth by regulating the cyclic AMP (cAMP) signalling pathway in C. albicans, and CYR1 and PDE2 regulate a pair of enzymes that are directly responsible for cAMP synthesis and degradation. Here, we hypothesize that farnesol enhances the antifungal susceptibility of C. albicans biofilms by regulating CYR1 and PDE2.

Results

The resistance of the CYR1- and PDE2-overexpressing strains to caspofungin, itraconazole and terbinafine was increased in planktonic cells, and that to amphotericin B was increased in biofilms. Meanwhile, the biofilms of the CYR1- and PDE2-overexpressing strains were thicker (all p < 0.05) and consisted of more hyphae than that of the wild strain. The intracellular cAMP levels were higher in the biofilms of the CYR1-overexpressing strain than that in the biofilms of the wild strain (all p < 0.01), while no changes were found in the PDE2-overexpressing strain. Exogenous farnesol decreased the resistance of the CYR1- and PDE2-overexpressing strains to these four antifungals, repressed the hyphal and biofilm formation of the strains, and decreased the intracellular cAMP level in the biofilms (all p < 0.05) compared to the untreated controls. In addition, farnesol decreased the expression of the gene CYR1 and the protein CYR1 in biofilms of the CYR1-overexpressing strain (all p < 0.05) but increased the expression of the gene PDE2 and the protein PDE2 in biofilms of the PDE2-overexpressing strain (all p < 0.01).

Conclusions

The results indicate that CYR1 and PDE2 regulate the resistance of C. albicans biofilms to antifungals. Farnesol suppresses the resistance of C. albicans biofilms to antifungals by regulating the expression of the gene CYR1 and PDE2, while PDE2 regulation was subordinate to CYR1 regulation.

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains

BACKGROUND

Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies.

RESULTS

By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns.

AVAILABILITY

The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Role of SFP1 in the Regulation of Candida albicans Biofilm Formation

Candida albicans is a major human fungal pathogen. One of the important features of C. albicans pathogenicity is the ability to form biofilms on mucosal surfaces and indwelling medical devices. Biofilm formation involves complex processes in C. albicans, including cell adhesion, filamentous growth, extracellular matrix secretion and cell dispersion. In this work, we characterized the role of the transcription factor Sfp1, particularly with respect to its function in the regulation of biofilm formation. The deletion of the SFP1 gene enhanced cell adhesion and biofilm formation in comparison to the wild-type strain. Interestingly, the sfp1-deleted mutant also exhibited an increase in the expression of the ALS1, ALS3 and HWP1 genes, which encode adhesin proteins. In addition, Sfp1 was demonstrated to function downstream of the Rhb1-TOR signaling pathway. Bcr1 and Efg1 are transcription factors that are critical for controlling biofilm formation, and Efg1 is also required for hyphal growth. Deleting either the BCR1 or EFG1 gene in the sfp1-null background led to reduced adhesin gene expression. As a result, the bcr1/sfp1 or efg1/sfp1 double deletion mutants exhibited dramatically reduced biofilm formation. The results indicated that Sfp1 negatively regulates the ALS1, ALS3 and HWP1 adhesin genes and that the repression of these genes is mediated by the inhibition of Bcr1 and Efg1.

Secretomes are a potential source of molecular targets for cancer therapies and indicate that APOE is a candidate biomarker for lung adenocarcinoma metastasis

Identifying patients at high risk of metastasis is a major challenge in lung adenocarcinoma (ADC) therapy, therefore discovery of noninvasive biomarkers and therapeutic targets is urgent. We found significant differences between the secretomes of differentially expressed proteins in lung ADC cell lines, clinical tissue samples and serum plasma samples with high and low metastatic potential. In particular, Apolipoprotein E (APOE) levels were three-times greater in cells with lymph node metastases (LNM) than those without. Our study indicates that APOE is a potential indicator of metastatic lung ADC and that secretomes may offer a valuable resource for biomarkers of lung ADC with LNM.

The role of Mss11 in Candida albicans biofilm formation

Candida albicans is an opportunistic human pathogen that can form a biofilm on biotic or inert surfaces such as epithelia and clinical devices. In this study, we examine the formation of C. albicans biofilm by establishing a key gene-centered network based on protein-protein interaction (PPI) and gene expression datasets. Starting from C. albicans Cph1 and Efg1, transcription factors associated with morphogenesis of biofilm formation, a network elucidates the complex cellular process and predicts potential unknown components related to biofilm formation. Subsequently, we analyzed the functions of Mss11 among these identified proteins to test the efficiency of the proposed computational approach. MSS11-deleted mutants were compared with a wild-type strain, indicating that the mutant is defective in forming a mature biofilm and partially attenuates the virulence of C. albicans in an infected mouse model. Finally, a DNA microarray analysis was conducted to identify the potential target genes of C. albicans Mss11. The findings of this study clarify complex gene or protein interaction during the biofilm formation process of C. albicans, supporting the application of a systems biology approach to study fungal pathogenesis.

Essential functional modules for pathogenic and defensive mechanisms in Candida albicans infections

The clinical and biological significance of the study of fungal pathogen Candida albicans (C. albicans) has markedly increased. However, the explicit pathogenic and invasive mechanisms of such host-pathogen interactions have not yet been fully elucidated. Therefore, the essential functional modules involved in C. albicans-zebrafish interactions were investigated in this study. Adopting a systems biology approach, the early-stage and late-stage protein-protein interaction (PPI) networks for both C. albicans and zebrafish were constructed. By comparing PPI networks at the early and late stages of the infection process, several critical functional modules were identified in both pathogenic and defensive mechanisms. Functional modules in C. albicans, like those involved in hyphal morphogenesis, ion and small molecule transport, protein secretion, and shifts in carbon utilization, were seen to play important roles in pathogen invasion and damage caused to host cells. Moreover, the functional modules in zebrafish, such as those involved in immune response, apoptosis mechanisms, ion transport, protein secretion, and hemostasis-related processes, were found to be significant as defensive mechanisms during C. albicans infection. The essential functional modules thus determined could provide insights into the molecular mechanisms of host-pathogen interactions during the infection process and thereby devise potential therapeutic strategies to treat C. albicans infection.

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study

Background

Despite clinical research and development in the last decades, infectious diseases remain a top global problem in public health today, being responsible for millions of morbidities and mortalities each year. Therefore, many studies have sought to investigate host-pathogen interactions from various viewpoints in attempts to understand pathogenic and defensive mechanisms, which could help control pathogenic infections. However, most of these efforts have focused predominately on the host or the pathogen individually rather than on a simultaneous analysis of both interaction partners.

Results

In this study, with the help of simultaneously quantified time-course Candida albicans-zebrafish interaction transcriptomics and other omics data, a computational framework was developed to construct the interspecies protein-protein interaction (PPI) network for C. albicans-zebrafish interactions based on the inference of ortholog-based PPIs and the dynamic modeling of regulatory responses. The identified C. albicans-zebrafish interspecies PPI network highlights the association between C. albicans pathogenesis and the zebrafish redox process, indicating that redox status is critical in the battle between the host and pathogen.

Conclusions

Advancing from the single-species network construction method, the interspecies network construction approach allows further characterization and elucidation of the host-pathogen interactions. With continued accumulation of interspecies transcriptomics data, the proposed method could be used to explore progressive network rewiring over time, which could benefit the development of network medicine for infectious diseases.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Candida albicans: A Model Organism for Studying Fungal Pathogens

Candida albicans is an opportunistic human fungal pathogen that causes candidiasis. As healthcare has been improved worldwide, the number of immunocompromised patients has been increased to a greater extent and they are highly susceptible to various pathogenic microbes and C. albicans has been prominent among the fungal pathogens. The complete genome sequence of this pathogen is now available and has been extremely useful for the identification of repertoire of genes present in this pathogen. The major challenge is now to assign the functions to these genes of which 13% are specific to C. albicans. Due to its close relationship with yeast Saccharomyces cerevisiae, an edge over other fungal pathogens because most of the technologies can be directly transferred to C. albicans from S. cerevisiae and it is amenable to mutation, gene disruption, and transformation. The last two decades have witnessed enormous amount of research activities on this pathogen that leads to the understanding of host-parasite interaction, infections, and disease propagation. Clearly, C. albicans has emerged as a model organism for studying fungal pathogens along with other two fungi Aspergillus fumigatus and Cryptococcus neoformans. Understanding its complete life style of C. albicans will undoubtedly be useful for developing potential antifungal drugs and tackling Candida infections. This will also shed light on the functioning of other fungal pathogens.

Prediction of phenotype-associated genes via a cellular network approach: a Candida albicans infection case study

Candida albicans is the most prevalent opportunistic fungal pathogen in humans causing superficial and serious systemic infections. The infection process can be divided into three stages: adhesion, invasion, and host cell damage. To enhance our understanding of these C. albicans infection stages, this study aimed to predict phenotype-associated genes involved during these three infection stages and their roles in C. albicans-host interactions. In light of the principles that proteins that lie closer to one another in a protein interaction network are more likely to have similar functions, and that genes regulated by the same transcription factors tend to have similar functions, a cellular network approach was proposed to predict the phenotype-associated genes in this study. A total of 4, 12, and 3 genes were predicted as adhesion-, invasion-, and damage-associated genes during C. albicans infection, respectively. These predicted genes highlight the facts that cell surface components are critical for cell adhesion, and that morphogenesis is crucial for cell invasion. In addition, they provide targets for further investigations into the mechanisms of the three C. albicans infection stages. These results give insights into the responses elicited in C. albicans during interaction with the host, possibly instrumental in identifying novel therapies to treat C. albicans infection.

[Progress in zinc finger nuclease engineering for targeted genome modification]

Zinc finger nuclease (ZFN) is an artificially engineered hybrid protein that contains a zinc finger protein (ZFP) domain and a Fok I endonuclease cleavage domain. It has recently emerged as a powerful molecular tool for targeted genome modifications. ZFNs recognize and bind to specific DNA sequences to generate a double-strand break (DSB) by its nuclease activity. Based on this finding, various genetic methods, including gene targeting (gene disruption), gene addition, gene correction etc., are being designed to manipulate the genomes of different species at specific loci. One particular advantage of this new technique is its broad applications, which can be employed to generate desirable inheritable mutations both at the organismal level and at the cellular level. Here, we review the recent progress and prospects of ZFN technology. This article focused on the mechanism of how it works, currently available target assessment, ZFP library construction and screening methods, target modification strategies, as well as a collection of specie and genes that have been successfully modified by ZFN. This review will provide a useful reference for researchers who are interested in applying this new technique in their studies.

Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana

BACKGROUND

Photosynthetic light acclimation is an important process that allows plants to optimize the efficiency of photosynthesis, which is the core technology for green energy. However, currently little is known about the molecular mechanisms behind the regulation of the photosynthetic light acclimation response. In this study, a systematic method is proposed to investigate this mechanism by constructing gene regulatory networks from microarray data of Arabidopsis thaliana.

METHODS

The potential TF-gene regulatory pairs of photosynthetic light acclimation have been obtained by data mining of literature and databases. Following the identification of these potential TF-gene pairs, they have been refined using Pearson's correlation, allowing the construction of a rough gene regulatory network. This rough gene regulatory network is then pruned using time series microarray data of Arabidopsis thaliana via the maximum likelihood system identification method and Akaike's system order detection method to approach the real gene regulatory network of photosynthetic light acclimation.

RESULTS

By comparing the gene regulatory networks under the PSI-to-PSII light shift and the PSII-to-PSI light shift, it is possible to identify important transcription factors for the different light shift conditions. Furthermore, the robustness of the gene network, in particular the hubs and weak linkage points, are also discussed under the different light conditions to gain further insight into the mechanisms of photosynthesis.

CONCLUSIONS

This study investigates the molecular mechanisms of photosynthetic light acclimation for Arabidopsis thaliana from the physiological level. This has been achieved through the construction of gene regulatory networks from the limited data sources and literature via an efficient computation method. If more experimental data for whole-genome ChIP-chip data and microarray data with multiple sampling points becomes available in the future, the proposed method will be improved on by constructing the whole-genome gene regulatory network. These advances will greatly improve our understanding of the mechanisms of the photosynthetic system.

Prediction of body fluids where proteins are secreted into based on protein interaction network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

Background

Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective.

Methods

In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples.

Results

Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs). In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer.

Conclusions

A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

Network biology: a direct approach to study biological function

In this paper we discuss the dualism of gene networks and their role in systems biology. We argue that gene networks (1) can serve as a conceptual framework, forming a fundamental level of a phenomenological description, and (2) are a means to represent and analyze data. The latter point does not only allow a systems analysis but is even amenable for a direct approach to study biological function. Here we focus on the clarity of our main arguments and conceptual meaning of gene networks, rather than the causal inference of gene networks from data. WIREs Syst Biol Med 2011 3 379-391 DOI: 10.1002/wsbm.134 For further resources related to this article, please visit the WIREs website.

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Background

This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.

Results

The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems.

Conclusions

Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

Systems biology approaches to understanding mycobacterial survival mechanisms

The advent of high-throughput platforms for the interrogation of biological systems at the cellular and molecular level have allowed living cells to be observed and understood at a hitherto unprecedented level of detail and have enabled the construction of comprehensive, predictive in silico models. Here, we review the application of such high-throughput, systems-biological techniques to mycobacteria-specifically to the pernicious human pathogen Mycobacterium tuberculosis (MTb) and its ability to survive in human hosts. We discuss the development and application of transcriptomic, proteomic, regulomic, and metabolomic techniques for MTb as well as the development and application of genome-scale in silico models. Thus far, systems-biological approaches have largely focused on in vitro models of MTb growth; reliably extending these approaches to in vivo conditions relevant to infection is a significant challenge for the future that holds the ultimate promise of novel chemotherapeutic interventions.