A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

<p>Abstract</p> <p>Background</p> <p>Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks.</p> <p>Results</p> <p>We use an S-system based model for the transcription and translation process. We propose an optimization-based regulatory network inference approach that uses time-varying data from DNA microarray analysis. Currently, this seems to be the only model-based method that can be used for the analysis of time-course "relative" expressions (expression ratios). We perform an analysis of the dynamic behavior of the system when the number of experimental samples available is varied, when there are different levels of noise in the data and when there are genes that are not considered by the experimenter. Our studies show that the principal factor affecting the ability of a method to infer interactions correctly is the similarity in the time profiles of some or all the genes. The less similar the profiles are to each other the easier it is to infer the interactions. We propose a heuristic method for resolving networks and show that it displays reasonable performance on a synthetic network. Finally, we validate our approach using real experimental data for a chosen subset of genes involved in the sporulation cascade of <it>Bacillus anthracis</it>. We show that the method captures most of the important known interactions between the chosen genes.</p> <p>Conclusion</p> <p>The performance of any inference method for regulatory interactions between genes depends on the noise in the data, the existence of unknown genes affecting the network genes, and the similarity in the time profiles of some or all genes. Though subject to these issues, the inference method proposed in this paper would be useful because of its ability to infer important interactions, the fact that it can be used with time-course DNA microarray data and because it is based on a non-linear model of the process that explicitly accounts for the regulatory role of proteins.</p

Optimization in computational systems biology

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This commentary is focused on applications of mathematical optimization in computational systems biology. Examples are given where optimization methods are used for topics ranging from model building and optimal experimental design to metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined

An integrated approach for the systematic identification and characterization of heart-enriched genes with unknown functions

<p>Abstract</p> <p>Background</p> <p>High throughput techniques have generated a huge set of biological data, which are deposited in various databases. Efficient exploitation of these databases is often hampered by a lack of appropriate tools, which allow easy and reliable identification of genes that miss functional characterization but are correlated with specific biological conditions (e.g. organotypic expression).</p> <p>Results</p> <p>We have developed a simple algorithm (DGSA = Database-dependent Gene Selection and Analysis) to identify genes with unknown functions involved in organ development concentrating on the heart. Using our approach, we identified a large number of yet uncharacterized genes, which are expressed during heart development. An initial functional characterization of genes by loss-of-function analysis employing morpholino injections into zebrafish embryos disclosed severe developmental defects indicating a decisive function of selected genes for developmental processes.</p> <p>Conclusion</p> <p>We conclude that DGSA is a versatile tool for database mining allowing efficient selection of uncharacterized genes for functional analysis.</p

Drug target optimization in chronic myeloid leukemia using innovative computational platform.

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.We would like to thank the members of the Fisher laboratory, in particular to Gavin Smyth and Caroline Dahl for their help with the BMA development, and Alex Hajnal for valuable comments on the manuscript and insightful discussions. Research in BG laboratory is supported by the Medical Research Council, Leukaemia and Lymphoma Research, The Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute.This is the final published version. It was originally published in Scientific Reports 5: 8190. DOI: 10.1038/srep08190

Targets of the Entamoeba histolytica Transcription Factor URE3-BP

The Entamoeba histolytica transcription factor Upstream Regulatory Element 3-Binding Protein (URE3-BP) is a calcium-responsive regulator of two E. histolytica virulence genes, hgl5 and fdx1. URE3-BP was previously identified by a yeast one-hybrid screen of E. histolytica proteins capable of binding to the sequence TATTCTATT (Upstream Regulatory Element 3 (URE3)) in the promoter regions of hgl5 and fdx1. In this work, precise definition of the consensus URE3 element was performed by electrophoretic mobility shift assays (EMSA) using base-substituted oligonucleotides, and the consensus motif validated using episomal reporter constructs. Transcriptome profiling of a strain induced to produce a dominant-positive URE3-BP was then used to identify additional genes regulated by URE3-BP. Fifty modulated transcripts were identified, and of these the EMSA defined motif T[atg]T[tc][cg]T[at][tgc][tg] was found in over half of the promoters (54% p<0.0001). Fifteen of the URE3-BP regulated genes were potential membrane proteins, suggesting that one function of URE3-BP is to remodel the surface of E. histolytica in response to a calcium signal. Induction of URE3-BP leads to an increase in tranwell migration, suggesting a possible role in the regulation of cellular motility

Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model

<p>Abstract</p> <p>Background</p> <p>Tumor angiogenesis is a highly regulated process involving intercellular communication as well as the interactions of multiple downstream signal transduction pathways. Disrupting one or even a few angiogenesis pathways is often insufficient to achieve sustained therapeutic benefits due to the complexity of angiogenesis. Targeting multiple angiogenic pathways has been increasingly recognized as a viable strategy. However, translation of the polypharmacology of a given compound to its antiangiogenic efficacy remains a major technical challenge. Developing a global functional association network among angiogenesis-related genes is much needed to facilitate holistic understanding of angiogenesis and to aid the development of more effective anti-angiogenesis therapeutics.</p> <p>Results</p> <p>We constructed a comprehensive gene functional association network or interactome by transcript profiling an in vitro angiogenesis model, in which human umbilical vein endothelial cells (HUVECs) formed capillary structures when co-cultured with normal human dermal fibroblasts (NHDFs). HUVEC competence and NHDF supportiveness of cord formation were found to be highly cell-passage dependent. An enrichment test of Biological Processes (BP) of differentially expressed genes (DEG) revealed that angiogenesis related BP categories significantly changed with cell passages. Built upon 2012 DEGs identified from two microarray studies, the resulting interactome captured 17226 functional gene associations and displayed characteristics of a scale-free network. The interactome includes the involvement of oncogenes and tumor suppressor genes in angiogenesis. We developed a network walking algorithm to extract connectivity information from the interactome and applied it to simulate the level of network perturbation by three multi-targeted anti-angiogenic kinase inhibitors. Simulated network perturbation correlated with observed anti-angiogenesis activity in a cord formation bioassay.</p> <p>Conclusion</p> <p>We established a comprehensive gene functional association network to model in vitro angiogenesis regulation. The present study provided a proof-of-concept pilot of applying network perturbation analysis to drug phenotypic activity assessment.</p

Inference of complex biological networks: distinguishability issues and optimization-based solutions

<p>Abstract</p> <p>Background</p> <p>The inference of biological networks from high-throughput data has received huge attention during the last decade and can be considered an important problem class in systems biology. However, it has been recognized that reliable network inference remains an unsolved problem. Most authors have identified lack of data and deficiencies in the inference algorithms as the main reasons for this situation.</p> <p>Results</p> <p>We claim that another major difficulty for solving these inference problems is the frequent lack of uniqueness of many of these networks, especially when prior assumptions have not been taken properly into account. Our contributions aid the distinguishability analysis of chemical reaction network (CRN) models with mass action dynamics. The novel methods are based on linear programming (LP), therefore they allow the efficient analysis of CRNs containing several hundred complexes and reactions. Using these new tools and also previously published ones to obtain the network structure of biological systems from the literature, we find that, often, a unique topology cannot be determined, even if the structure of the corresponding mathematical model is assumed to be known and all dynamical variables are measurable. In other words, certain mechanisms may remain undetected (or they are falsely detected) while the inferred model is fully consistent with the measured data. It is also shown that sparsity enforcing approaches for determining 'true' reaction structures are generally not enough without additional prior information.</p> <p>Conclusions</p> <p>The inference of biological networks can be an extremely challenging problem even in the utopian case of perfect experimental information. Unfortunately, the practical situation is often more complex than that, since the measurements are typically incomplete, noisy and sometimes dynamically not rich enough, introducing further obstacles to the structure/parameter estimation process. In this paper, we show how the structural uniqueness and identifiability of the models can be guaranteed by carefully adding extra constraints, and that these important properties can be checked through appropriate computation methods.</p

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories

iNID: An Analytical Framework for Identifying Network Models for Interplays among Developmental Signaling in Arabidopsis

Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs.X115Ysciescopu

A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data-1

<p><b>Copyright information:</b></p><p>Taken from "A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/228</p><p>BMC Bioinformatics 2007;8():228-228.</p><p>Published online 29 Jun 2007</p><p>PMCID:PMC1940027.</p><p></p>rror obtained with the smallest number of samples. The variations are with respect to the number of experimental samples chosen and the number of discretizations, . The "experimental" data are obtained by simulation using the "Low" synthetic network (see Figure 1)