Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments

Global quantitative understanding of non-equilibrium cell fate decision-making in response to pheromone

Cell-cycle arrest and polarized growth are commonly used to characterize the response of yeast to pheromone. However, the quantitative decision-making processes underlying time-dependent changes in cell fate remain unclear. In this study, we conducted single-cell level experiments to observe multidimensional responses, uncovering diverse fates of yeast cells. Multiple states are revealed, along with the kinetic switching rates and pathways among them, giving rise to a quantitative landscape of mating response. To quantify the experimentally observed cell fates, we developed a theoretical framework based on non-equilibrium landscape and flux theory. Additionally, we performed stochastic simulations of biochemical reactions to elucidate signal transduction and cell growth. Notably, our experimental findings have provided the first global quantitative evidence of the real-time synchronization between intracellular signaling, physiological growth, and morphological functions. These results validate the proposed underlying mechanism governing the emergence of multiple cell fate states. This study introduces an emerging mechanistic approach to understand non-equilibrium cell fate decision-making in response to pheromone.

wpLogicNet: logic gate and structure inference in gene regulatory networks

MOTIVATION

The gene regulatory process resembles a logic system in which a target gene is regulated by a logic gate among its regulators. While various computational techniques are developed for a gene regulatory network (GRN) reconstruction, the study of logical relationships has received little attention. Here, we propose a novel tool called wpLogicNet that simultaneously infers both the directed GRN structures and logic gates among genes or transcription factors (TFs) that regulate their target genes, based on continuous steady-state gene expressions.

RESULTS

wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF-gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes.

AVAILABILITY AND IMPLEMENTATION

The datasets and R package of wpLogicNet are available in the github repository, https://github.com/CompBioIPM/wpLogicNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects

Motivation

Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model?

Results

Here, we introduce an enhanced version of linear effects models (LEMs), which extends the original by accounting for both negative and positive contributions of the perturbed network proteins to the observed phenotype. We prove that the enhanced LEMs are identified from data measured under perturbations of all single, pairs and triplets of network proteins. For small networks of up to five nodes, only perturbations of single and pairs of proteins are required for identifiability. Extensive simulations demonstrate that enhanced LEMs achieve excellent accuracy of parameter estimation and network structure learning, outperforming the previous version on realistic data. LEMs applied to Bartonella henselae infection RNAi screening data identified known interactions between eight nodes of the infection network, confirming high specificity of our model and suggested one new interaction.

Availability and implementation

https://github.com/EwaSzczurek/LEM

Supplementary information

Supplementary data are available at Bioinformatics online.

CrossPlan: systematic planning of genetic crosses to validate mathematical models

Motivation

Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test.

Results

We present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. We prove that the CrossPlan problem is NP-complete. We develop an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. We also extend our solution to incorporate other experimental conditions such as a delay factor that decides the availability of a mutant and genetic markers to confirm gene deletions. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence, our framework should be relevant in plant and animal systems as well.

Availability and implementation

CrossPlan code is freely available under GNU General Public Licence v3.0 at https://github.com/Murali-group/crossplan.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dynamics in Epistasis Analysis

Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations. Here, we explore the possibility of dynamic epistasis analysis, in which, in addition to performing genetic perturbations of a pathway, we drive the pathway by a time-varying upstream signal. We explore the theoretical power of dynamical epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We find that even relatively simple input dynamics greatly increases the power of epistasis analysis to discriminate alternative network structures. Further, we explore the question of experiment design, and show that a subset of short time-varying signals, which we call dynamic primitives, allow maximum discriminative power with a reduced number of experiments.

Designing optimal experiments to discriminate interaction graph models

Modern methods for the inference of cellular networks from experimental data often express nondeterminism through an ensemble of candidate models. To discriminate among these candidates new experiments need to be carried out. Theoretically, the number of possible experiments is exponential in the number of possible perturbations. In praxis, experiments are expensive and there exist several limiting constraints. Limiting factors exist on the combinations of perturbations that are technically possible, which components can be measured, and on the number of affordable experiments. Further, not all experiments are equally well suited to discriminate model candidates. The goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. We present an approach for experiment planning with interaction graph models and sign consistency methods. This new approach can be used in combination with methods for network inference and consistency checking. We applied our method to study the Erythropoietin signal transduction in human kidney cells HEK293. We first used simulated experiment data from an ODE model to demonstrate in silico that our experimental design results in the inference of the gold standard model. Finally, we used the approach to plan in vivo experiments that discriminate model candidates for the Erythropoietin signal transduction in this cell line.

Linear effects models of signaling pathways from combinatorial perturbation data

Motivation: Perturbations constitute the central means to study signaling pathways. Interrupting components of the pathway and analyzing observed effects of those interruptions can give insight into unknown connections within the signaling pathway itself, as well as the link from the pathway to the effects. Different pathway components may have different individual contributions to the measured perturbation effects, such as gene expression changes. Those effects will be observed in combination when the pathway components are perturbed. Extant approaches focus either on the reconstruction of pathway structure or on resolving how the pathway components control the downstream effects.

Results: Here, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturbation data. We use simulated data to demonstrate the accuracy of learning the pathway structure as well as estimation of the individual contributions of pathway components to the perturbation effects. The practical utility of our approach is illustrated by an application to perturbations of the mitogen-activated protein kinase pathway in Saccharomyces cerevisiae.

Availability and Implementation: lem is available as a R package at http://www.mimuw.edu.pl/∼szczurek/lem.

Contact:szczurek@mimuw.edu.pl; niko.beerenwinkel@bsse.ethz.ch

Supplementary information:Supplementary data are available at Bioinformatics online.

A review of active learning approaches to experimental design for uncovering biological networks

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Designing Experiments to Discriminate Families of Logic Models

Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive. Moreover, experimental data are unavoidably subjected to noise. As a result, the learning process results in a family of feasible logical networks rather than in a single model. This family is composed of logic models implementing different internal wirings for the system and therefore the predictions of experiments from this family may present a significant level of variability, and hence uncertainty. In this paper, we introduce a method based on Answer Set Programming to propose an optimal experimental design that aims to narrow down the variability (in terms of input-output behaviors) within families of logical models learned from experimental data. We study how the fitness with respect to the data can be improved after an optimal selection of signaling perturbations and how we learn optimal logic models with minimal number of experiments. The methods are applied on signaling pathways in human liver cells and phosphoproteomics experimental data. Using 25% of the experiments, we obtained logical models with fitness scores (mean square error) 15% close to the ones obtained using all experiments, illustrating the impact that our approach can have on the design of experiments for efficient model calibration.

Experimental design schemes for learning Boolean network models

MOTIVATION

A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein-protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human. Learning such models requires observing the system under a sufficient number of different conditions. To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies.

RESULTS

We developed novel design approaches that greedily select an experiment to be performed so as to maximize the difference or the entropy in the results it induces with respect to current best-fit models. Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data. We applied both approaches to simulated and real data from the EFGR and IL1 signaling systems in human. We demonstrate the utility of the developed strategies in substantially improving on a random selection approach. Our design schemes highlight the redundancy in these datasets, leading up to 11-fold savings in the number of experiments to be performed.

AVAILABILITY AND IMPLEMENTATION

Source code will be made available upon acceptance of the manuscript.

Computationally Driven Experimental Biology

The complexity, diversity, and richness of experimental data on cellular systems are inspiring the development of computational analysis techniques that can directly prioritize and suggest new experiments.

Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data

Background

Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context.

Results

We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'.

Conclusions

Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.

Towards genome-scale signalling network reconstructions

Biological signalling networks allow living organisms to issue an integrated response to current conditions and make limited predictions about future environmental changes. Small-scale dynamic models of signalling cascades, including mitogen-activated protein kinase cascades, have been developed to generate hypotheses about signal transduction. Owing to technical limitations, these models and the hypotheses they generate have focused on a limited subset of signalling molecules. Now that we can simultaneously measure a substantial portion of the molecular components of a cell, we can begin to develop and test systems-level models of cellular signalling and regulatory processes, therefore gaining insights into the 'thought' processes of a cell.

Inferring large-scale gene regulatory networks using a low-order constraint-based algorithm

Recently, simplified graphical modeling approaches based on low-order conditional (in-)dependence calculations have received attention because of their potential to model gene regulatory networks. Such methods are able to reconstruct large-scale gene networks with a small number of experimental measurements, at minimal computational cost. However, unlike Bayesian networks, current low-order graphical models provide no means to distinguish between cause and effect in gene regulatory relationships. To address this problem, we developed a low-order constraint-based algorithm for gene regulatory network inference. The method is capable of inferring causal directions using limited-order conditional independence tests and provides a computationally-feasible way to analyze high-dimensional datasets while maintaining high reliability. To assess the performance of our algorithm, we compared it to several existing graphical models: relevance networks; graphical Gaussian models; ARACNE; Bayesian networks; and the classical constraint-based algorithm, using realistic synthetic datasets. Furthermore, we applied our algorithm to real microarray data from Escherichia coli Affymetrix arrays and validated the results by comparison to known regulatory interactions collected in RegulonDB. The algorithm was found to be both effective and efficient at reconstructing gene regulatory networks from microarray data.