3,107 research outputs found

    Bioinformatics tools in predictive ecology: Applications to fisheries

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    This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

    Estimating Network Kinetics of the MAPK/ERK Pathway Using Biochemical Data

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    The MAPK/ERK pathway is a major signal transduction system which regulates many fundamental cellular processes including the growth control and the cell death. As a result of these roles, it has a crucial importance in cancer as well as normal developmental processes. Therefore, it has been intensively studied resulting in a wealth of knowledge about its activation. It is also well documented that the activation kinetics of the pathway is crucial to determine the nature of the biological response. However, while individual biochemical steps are well characterized, it is still difficult to predict or even understand how the activation kinetics works. The aim of this paper is to estimate the stochastic rate constants of the MAPK/ERK network dynamics. Accordingly, taking a Bayesian approach, we combined underlying qualitative biological knowledge in several competing dynamic models via sets of quasireactions and estimated the stochastic rate constants of these reactions. Comparing the resulting estimates via the BIC and DIC criteria, we chose a biological model which includes EGFR degradation—Raf-MEK-ERK cascade without the involvement of RKIPs.

    Tehokas strategia biokemiallisten verkkojen päättelyyn mekanististen mallien tilastollisen sovittamisen avulla

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    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

    Learning unknown ODE models with Gaussian processes

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    In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to determine the equations or interactions governing the underlying dynamics. In these settings, parametric ODE model cannot be formulated. Here, we overcome this issue by introducing a novel paradigm of nonparametric ODE modelling that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We propose to learn non-linear, unknown differential functions from state observations using Gaussian process vector fields within the exact ODE formalism. We demonstrate the model's capabilities to infer dynamics from sparse data and to simulate the system forward into future.Comment: 11 pages, 2 page appendi

    Scalable and flexible inference framework for stochastic dynamic single-cell models

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    Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability

    Causal machine learning for single-cell genomics

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    Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies allow for measuring the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes such as gene regulation, disease progression or cellular development. However, the high-dimensional nature of the data, coupled with the intricate complexity of biological systems renders this task nontrivial. Within the machine learning community, there has been a recent increase of interest in causality, with a focus on adapting established causal techniques and algorithms to handle high-dimensional data. In this perspective, we delineate the application of these methodologies within the realm of single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology and discuss and challenge the assumptions it entails from the biological point of view. We then identify open problems in the application of causal approaches to single-cell data: generalising to unseen environments, learning interpretable models, and learning causal models of dynamics. For each problem, we discuss how various research directions - including the development of computational approaches and the adaptation of experimental protocols - may offer ways forward, or on the contrary pose some difficulties. With the advent of single cell atlases and increasing perturbation data, we expect causal models to become a crucial tool for informed experimental design.Comment: 35 pages, 7 figures, 3 tables, 1 bo
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