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Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales., Alexander Gross et al. present ProbRules, a dynamic modeling approach that combines probabilities and logical rules to represent network dynamics over multiple scales. They apply ProbRules to a Wnt network, predicting gene expression readouts that they confirm with wet-lab experiments.

Nowadays, due to the development of potent Direct-Acting Antiviral Agents (DAAs) that specifically target NS3, NS5A and NS5B viral proteins, several new and highly efficacious options to treat chronic Hepatitis C virus (HCV) infection are available. The natural presence of resistance associated substitutions (RASs), as well as their rapid emergence during incomplete drug-pressure, are intrinsic characteristics of HCV that greatly affect treatment outcome and the chances to achieve a virolgical cure. To date, a high number of RASs in NS3, NS5A, and NS5B have been associated in vivo and/or in vitro with reduced susceptibility to DAAs, but no comprehensive RASs list is available. This review thus provides an updated, systematic overview of the role of RASs to currently approved DAAs or in phase II/III of clinical development against HCV-infection, discriminating their impact in different HCV-genotypes and DAAs, providing assistance for a fruitful use of HCV resistance testing in clinical practice.

Objective: Systematic tracking of microRNA (miRNA) targets remains a challenge. In our work, we aimed to use TargetScore to investigate the potential targets of miRNA203 and miRNA-146a in psoriasis by integrating miRNA overexpression information and sequence data, and to further uncover the functions of miRNA203 and miRNA-146a in psoriasis. Methods: This was a case-control bioinformatics analysis using already published microarray data of psoriasis. We calculated targetScores by combining log fold-change and sequence scores obtained from TargetScan context score, probabilities of conserved targeting, and derived the distribution of targetScores. The scoring cutoff was chosen based on the different targetScore distributions for the nonvalidated and validated targets. The potential target genes for miRNA-203 and miRNA-146a were predicted based on the targetScore threshold. To reveal the functions of miRNA-203 and miRNA-146a, we implemented pathway enrichment analyses for the targets of miRNA-203 and miRNA-146a. Results: TargetScore >0.4 was selected as the threshold to filter out less confidence targets because we observed little overlap between the 2 distribution at targetScore = 0.4. Based on the targetScore >0.4, 49 target genes for miRNA-203 and 17 targets for miRNA-146a were identified. Pathway enrichment results showed that the target genes of miRNA-203 (including KIR2DL1, HLA-DQA1, KIR3DL1) only participated in antigen processing and presentation. The target genes of miRNA-146a (covering ADORA3, CYSLTR2, HRH4) were only involved in neuroactive ligand-receptor interaction. Conclusion: MiRNA203 and miRNA-146a played important roles in psoriasis progression, partially through regulating the pathways of antigen processing and presentation, and neuroactive ligand-receptor interaction, respectively.

Background The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed “transcript pattern”. A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. Results In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80 % fold-change concordance with the gold standard qRT-PCR (TaqMan). Conclusions This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1913-6) contains supplementary material, which is available to authorized users.

Biological systems produce outputs in response to variable inputs. Input-output relations tend to follow a few regular patterns. For example, many chemical processes follow the S-shaped Hill equation relation between input concentrations and output concentrations. That Hill equation pattern contradicts the fundamental Michaelis-Menten theory of enzyme kinetics. I use the discrepancy between the expected Michaelis-Menten process of enzyme kinetics and the widely observed Hill equation pattern of biological systems to explore the general properties of biological input-output relations. I start with the various processes that could explain the discrepancy between basic chemistry and biological pattern. I then expand the analysis to consider broader aspects that shape biological input-output relations. Key aspects include the input-output processing by component subsystems and how those components combine to determine the system's overall input-output relations. That aggregate structure often imposes strong regularity on underlying disorder. Aggregation imposes order by dissipating information as it flows through the components of a system. The dissipation of information may be evaluated by the analysis of measurement and precision, explaining why certain common scaling patterns arise so frequently in input-output relations. I discuss how aggregation, measurement and scale provide a framework for understanding the relations between pattern and process. The regularity imposed by those broader structural aspects sets the contours of variation in biology. Thus, biological design will also tend to follow those contours. Natural selection may act primarily to modulate system properties within those broad constraints., Comment: Biology Direct 8:31