How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical research thanks to their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory network. Latest advances in transcriptomics have made it possible to infer basic topologies of pluripotency governing networks. The inferred network topologies, however, only encode boolean information while remaining silent about the roles of dynamics and molecular noise in gene expression. These features are widely considered essential for functional decision making. Herein we developed a framework for extending the boolean level networks into models accounting for individual genetic switches and promoter architecture which allows mechanistic interrogation of the roles of molecular noise, external signaling, and network topology. We demonstrate the pluripotent state of the network to be a broad attractor which is robust to variations of gene expression. Dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the molecular noise originating from genetic switching events which makes cells more responsive to extracellular signals. Lastly we show that steady state probability landscape can be significantly remodeled by global gene switching rates alone which can be taken as a proxy for how global epigenetic modifications exert control over stability of pluripotent states.Comment: 11 pages, 7 figure

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations. ©2009 IEEE.published_or_final_versio

Mathematical modelling plant signalling networks

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting

Tehokas strategia biokemiallisten verkkojen päättelyyn mekanististen mallien tilastollisen sovittamisen avulla

Various fields of science employ systems of ordinary differential equations (ODEs) to model the behaviour of dynamical systems, such as gene regulatory networks. However, the system model often contains uncertainty in both its structure and the model parameters. When experimental data are available, the model parameters can be calibrated using well-established statistical techniques and also different model structures can be compared in the light of their statistical evidence. If the set of alternative model structures is small enough, it is possible to evaluate the validity of each individual model separately. However, for biochemical networks, the number of viable model configurations is often enormous, which renders it computationally impossible to draw inferences about the network structure using such an exhaustive strategy. This thesis introduces a novel computationally efficient approach to obtain probabilistic structure inferences for general ODE models. The proposed approach relies on exploring the discrete set of alternative models using Markov chain Monte Carlo methods. Inference problems involving simulated data are used to demonstrate that the method is suitable for efficiently extracting information about the characteristics of the likely models. Furthermore, the method is applied to infer the structure of the transiently evolving core regulatory network that steers the T helper 17 (Th17) cell differentiation. The obtained results are in agreement with earlier studies that suggest that the Th17 differentiation program involves three sequential phases.Differentiaaliyhtälösysteemejä käytetään monilla tieteenaloilla mallintamaan dynaamisia systeemejä, kuten geenisäätelyverkkoja. Systeemiä kuvaavassa mallissa on kuitenkin usein epävarmuutta sekä sen rakenteen että mallin parametrien osalta. Kun kokeellista dataa on saatavilla, mallien parametrit voidaan sovittaa käyttäen vakiintuneita tilastollisia menetelmiä, ja myös erilaisia malleja voidaan vertailla niiden tilastollisen todennäköisyyden avulla. Jos vaihtoehtoisia malleja on vain vähän, voidaan jokainen yksittäinen malli validoida erikseen. Biokemiallisten verkkojen tapauksessa mahdollisia mallikonfiguraatioita on usein lukemattomia, minkä takia yllä kuvattu tapa verkkojen rakenteen päättelyyn on laskennallisesti mahdotonta. Tässä työssä esitellään uusi laskennallisesti tehokas lähestymistapa tehdä probabilistisia päätelmiä differentiaaliyhtälömallien rakenteesta. Ehdotettu lähestymistapa perustuu diskreetin mallijoukon tutkimiseen Markov Chain Monte Carlo -menetelmillä. Työssä muotoillaan simuloituun dataan liittyviä ongelmia, joilla näytetään, että menetelmällä voi tehokkaasti saada tietoa todennäköisimmistä mallirakenteista. Menetelmää sovelletaan myös erään auttaja-T-solujen alityypin (Th17) erilaistumista ajavan aikariippuvan ydinverkon rakenteen päättelyyn. Saadut tulokset ovat linjassa aiempien tutkimusten kanssa, joiden mukaan Th17-solujen erilaistuminen tapahtuu kolmessa peräkkäisessä vaiheessa

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments