Systems biology approaches to the dynamics of gene expression and chemical reactions

Systems biology is an emergent interdisciplinary field of study whose main goal is to understand the global properties and functions of a biological system by investigating its structure and dynamics [74]. This high-level knowledge can be reached only with a coordinated approach involving researchers with different backgrounds in molecular biology, the various omics (like genomics, proteomics, metabolomics), computer science and dynamical systems theory. The history of systems biology as a distinct discipline began in the 1960s, and saw an impressive growth since year 2000, originated by the increased accumulation of biological information, the development of high-throughput experimental techniques, the use of powerful computer systems for calculations and database hosting, and the spread of Internet as the standard medium for information diffusion [77]. In the last few years, our research group tried to tackle a set of systems biology problems which look quite diverse, but share some topics like biological networks and system dynamics, which are of our interest and clearly fundamental for this field. In fact, the first issue we studied (covered in Part I) was the reverse engineering of large-scale gene regulatory networks. Inferring a gene network is the process of identifying interactions among genes from experimental data (tipically microarray expression profiles) using computational methods [6]. Our aim was to compare some of the most popular association network algorithms (the only ones applicable at a genome-wide level) in different conditions. In particular we verified the predictive power of similarity measures both of direct type (like correlations and mutual information) and of conditional type (partial correlations and conditional mutual information) applied on different kinds of experiments (like data taken at equilibrium or time courses) and on both synthetic and real microarray data (for E. coli and S. cerevisiae). In our simulations we saw that all network inference algorithms obtain better performances from data produced with \u201cstructural\u201d perturbations (like gene knockouts at steady state) than with just dynamical perturbations (like time course measurements or changes of the initial expression levels). Moreover, our analysis showed differences in the performances of the algorithms: direct methods are more robust in detecting stable relationships (like belonging to the same protein complex), while conditional methods are better at causal interactions (e.g. transcription factor\u2013binding site interactions), especially in presence of combinatorial transcriptional regulation. Even if time course microarray experiments are not particularly useful for inferring gene networks, they can instead give a great amount of information about the dynamical evolution of a biological process, provided that the measurements have a good time resolution. Recently, such a dataset has been published [119] for the yeast metabolic cycle, a well-known process where yeast cells synchronize with respect to oxidative and reductive functions. In that paper, the long-period respiratory oscillations were shown to be reflected in genome-wide periodic patterns in gene expression. As explained in Part II, we analyzed these time series in order to elucidate the dynamical role of post-transcriptional regulation (in particular mRNA stability) in the coordination of the cycle. We found that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates. Moreover, the cascade of events which occurs during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses or stimuli. The concepts of network and of systems dynamics return also as major arguments of Part III. In fact, there we present a study of some dynamical properties of the so-called chemical reaction networks, which are sets of chemical species among which a certain number of reactions can occur. These networks can be modeled as systems of ordinary differential equations for the species concentrations, and the dynamical evolution of these systems has been theoretically studied since the 1970s [47, 65]. Over time, several independent conditions have been proved concerning the capacity of a reaction network, regardless of the (often poorly known) reaction parameters, to exhibit multiple equilibria. This is a particularly interesting characteristic for biological systems, since it is required for the switch-like behavior observed during processes like intracellular signaling and cell differentiation. Inspired by those works, we developed a new open source software package for MATLAB, called ERNEST, which, by checking these various criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results of this analysis can be used, for example, for model discrimination: if for a multistable biological process there are multiple candidate reaction models, it is possible to eliminate some of them by proving that they are always monostationary. Finally, we considered the related property of monotonicity for a reaction network. Monotone dynamical systems have the tendency to converge to an equilibrium and do not present chaotic behaviors. Most biological systems have the same features, and are therefore considered to be monotone or near-monotone [85, 116]. Using the notion of fundamental cycles from graph theory, we proved some theoretical results in order to determine how distant is a given biological network from being monotone. In particular, we showed that the distance to monotonicity of a network is equal to the minimal number of negative fundamental cycles of the corresponding J-graph, a signed multigraph which can be univocally associated to a dynamical system

ERNEST: a toolbox for chemical reaction network theory

Abstract Summary: ERNEST Reaction Network Equilibria Study Toolbox is a MATLAB package which, by checking various different criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results obtained are independent of the rate constants of the reactions, and can be used for model discrimination. Availability and Implementation: The software, implemented in MATLAB, is available under the GNU GPL free software license from http://people.sissa.it/∼altafini/papers/SoAl09/. It requires the MATLAB Optimization Toolbox. Contact: [email protected]

From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis

Background: Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods. Results: We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step. Conclusions: Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network subchallenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference

Nonverbal communication in selfies posted on Instagram: Another look at the effect of gender on vertical camera angle

Background Selfies are a novel social phenomenon that is gradually beginning to receive attention within the cognitive sciences. Several studies have documented biases that may be related to nonverbal communicative intentions. For instance, in selfies posted on the dating platform Tinder males but not females prefer camera views from below (Sedgewick, Flath & Elias, 2017). We re-examined this study to assess whether this bias is confined to dating selection contexts and to compare variability between individuals and between genders. Methods Three raters evaluated vertical camera position in 2000 selfies– 1000 by males and 1000 by females—posted in Instagram. Results We found that the choices of camera angle do seem to vary depending on the context under which the selfies were uploaded. On Tinder, females appear more likely to choose neutral, frontal presentations than they do on Instagram, whereas males on Tinder appear more likely to opt for camera angles from below than on Instagram. Conclusions This result confirms that the composition of selfies is constrained by factors affecting nonverbal communicative intentions

comparing association network algorithms for reverse engineering of large scale gene regulatory networks

Motivation: Inferring a gene regulatory network exclusively from microarray expression profiles is a difficult but important task. The aim of this work is to compare the predictive power of some of the most popular algorithms in different conditions (like data taken at equilibrium or time courses) and on both synthetic and real microarray data. We are in particular interested in comparing similarity measures both of linear type (like correlations and partial correlations) and of non-linear type (mutual information and conditional mutual information), and in investigating the underdetermined case (less samples than genes). Results: In our simulations we see that all network inference algorithms obtain better performances from data produced with 'structural' perturbations, like gene knockouts at steady state, than with any dynamical perturbation. The predictive power of all algorithms is confirmed on a reverse engineering problem from Escherichia coli gene profiling data: the edges of the 'physical' network of transcription factor–binding sites are significantly overrepresented among the highest weighting edges of the graph that we infer directly from the data without any structure supervision. Comparing synthetic and in vivo data on the same network graph allows us to give an indication of how much more complex a real transcriptional regulation program is with respect to an artificial model. Availability: Software is freely available at the URL http://people.sissa.it/~altafini/papers/SoBiAl07/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle

<p>Abstract</p> <p>Background</p> <p>In yeast, genome-wide periodic patterns associated with energy-metabolic oscillations have been shown recently for both short (approx. 40 min) and long (approx. 300 min) periods.</p> <p>Results</p> <p>The dynamical regulation due to mRNA stability is found to be an important aspect of the genome-wide coordination of the long-period yeast metabolic cycle. It is shown that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates.</p> <p>Conclusion</p> <p>The cascade of events occurring during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses/stimuli.</p

Nonverbal communication in selfies posted on Instagram: another look at the effect of gender on vertical camera angle

Background: Selfies are a novel social phenomenon that is gradually beginning to receive attention within the cognitive sciences. Several studies have documented biases that may be related to nonverbal communicative intentions. For instance, in selfies posted on the dating platform Tinder males but not females prefer camera views from below (Sedgewick, Flath & Elias, 2017). We re-examined this study to assess whether this bias is confined to dating selection contexts and to compare variability between individuals and between genders. Methods: Three raters evaluated vertical camera position in 2000 selfies– 1000 by males and 1000 by females—posted in Instagram. Results: We found that the choices of camera angle do seem to vary depending on the context under which the selfies were uploaded. On Tinder, females appear more likely to choose neutral, frontal presentations than they do on Instagram, whereas males on Tinder appear more likely to opt for camera angles from below than on Instagram. Conclusions: This result confirms that the composition of selfies is constrained by factors affecting nonverbal communicative intentions

StatSeq Systems Genetics Benchmark

Description of published synthetic Systems Genetics datasets.The StatSeq benchmark dataset is meant to be used for training and evaluating algorithms and techniques for the inference of networks from systems genetics data. The goal is to comprehend which methodology has the best overall inferring performance, and which eventually performs better under particular conditions (i.e. population size, large or small marker distances, high or low heritability, network size). This short document describes how the data have been generated through SysGenSIM. Detailed information is provided about the construction of the gene networks, the simulation of the genotype and of the gene expression, and the submission and evaluation of the predictions

LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis

Background: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences.Results: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios.We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences.Conclusion: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes

Multicohort analysis of the maternal age effect on recombination

Several studies have reported that the number of crossovers increases with maternal age in humans, but others have found the opposite. Resolving the true effect has implications for understanding the maternal age effect on aneuploidies. Here, we revisit this question in the largest sample to date using single nucleotide polymorphism (SNP)-chip data, comprising over 6,000 meioses from nine cohorts. We develop and fit a hierarchical model to allow for differences between cohorts and between mothers. We estimate that over 10 years, the expected number of maternal crossovers increases by 2.1% (95% credible interval (0.98%, 3.3%)). Our results are not consistent with the larger positive and negative effects previously reported in smaller cohorts. We see heterogeneity between cohorts that is likely due to chance effects in smaller samples, or possibly to confounders, emphasizing that care should be taken when interpreting results from any specific cohort about the effect of maternal age on recombination