Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in a population of cells. The discussion suggests that stochastic neural network models can give better descriptions of gene regulatory networks and provide criteria for measuring the reasonableness of mathematical models. 1

This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in general. Preface Concrete examples help to understand complex systems. In this report, the key point is to illustrate the basic mechanisms and properties of neocybernetic system models. Good visualizations are certainly needed. It is biological systems, or living systems, that are perhaps the most characteristic examples of cybernetic systems. This intuition is extended here to natural systems in general — indeed, it is all other than man-made ones that seem to be cybernetic. The word “biological ” in the title should be interpreted as “bio-logical ” — referring to general studies of any living systems, independent of the phenosphere. Starting from the concrete examples, connections to more abstract systems are found, and the discussions become more and more all-embracing in this text. However, the neocybernetic model framework still makes it possible to conceptually master the complexity. There is more information about neocybernetics available in Internet — also this report is available there in electronic form:

this document may be downloaded from: http://www.itee.uq.edu.au/nic/ accs-grn/modelling-grns.pdf Overview This document provides an overview of approaches to the modelling of genetic regulatory networks, with an emphasis on techniques from complex systems

Living systems are able to evolve, adapt, and self-organise. Genes store vital information, and, as part of genetic regulatory networks (GRNs), orchestrate the use of this information. GRNs control cellular metabolism and respond to changes in environment. Evolution has produced organisms that are adapted to a variety of niches, with the advent of multicellularity being a major breakthrough in self-organised complexity. In multicellular organisms cells are differentiated into many types, resulting in efficient division of labour. The aim of this thesis is to abstract the principles of genetic regulatory network evolution, and assess whether these principles are applicable to novel computation and automated complex systems engineering. Evolving artificial GRNs de novo can give new insights into the constraints that real GRNs are subject to, and harnessing the evolvability of GRNs could lead to huge improvements in the automatic engineering of control systems. The study starts by analysing natural GRNs and identifying mechanisms

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To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.

This supplementary material is divided as follows: Section 1 has a description about how to simulate Genetic Regulatory Networks and Section 2 explains how to perform these simulations using Netbuilder. Section 3 presents details about the Graphical Gaussian Models and Section 4 presents details about Bayesian Networks. In Section 5 we discuss how the data was pre-processed for our analysis. Section 6 presents the v-structure network, which was used to create simulated data to give some idea of how the network topology influences the different inference methods. In Section 7 we present an overview of all the results. Sections 8 and 9 present tables for comparing the performance between methods. These sections present comparisons for AUC scores and TP scores respectively. Sections 10 and 11 present tables for comparing the performance

receptors, etc.) have composite dose–response behaviors in relation to concentrations of protein receptors and endogenous signaling molecules. “Molecular circuits ” include the biological components and their interactions that comprise the workings of these signaling motifs. Many of these molecular circuits have nonlinear dose–response behaviors for endogenous ligands and for exogenous toxicants, acting as switches with “all-or-none ” responses over a narrow range of concentration. In turn, these biological switches regulate large-scale cellular processes, e.g., commitment to cell division, cell differentiation, and phenotypic alterations. Biologically based dose–response (BBDR) models accounting for these biological switches would improve risk assessment for many nonlinear processes in toxicology. These BBDR models must account for normal control of the signaling motifs and for perturbations by toxic compounds. We describe several of these biological switches, current tools available for constructing BBDR models of these processes, and the potential value of these models in risk assessment. Key words: biological switches, dose–response relationships, endocrine-active compounds, estrogen, molecular circuitry, pharmacodynamic models, risk assessment, TCDD. Environ Health Perspect 110(suppl 6):971–978 (2002).

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae