Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model

Gene regulatory network underlying the immortalization of epithelial cells

Abstract Background: Tumorigenic transformationofhumanepithelialcellsinvitrohasbeendescribedexperimentallyas thepotentialresultofspontaneousimmortalization.Thisprocessischaracterizedbyaseriesofcell–statetransitions,in whichnormalepithelialcellsacquirefirstasenescentstatewhichislatersurpassedtoattainamesenchymalstem–like phenotypewithapotentiallytumorigenicbehavior.Inthispaperweaimtoprovideasystem–levelmechanistic explanationtotheemergenceofthesecelltypes,andtothetime–orderedtransitionpatternsthatarecommonto neoplasiasofepithelialorigin.Tothisend,wefirstintegratepublishedfunctionalandwell–curatedmoleculardataof thecomponentsandinteractionsthathavebeenfoundtobeinvolvedinsuchcellstatesandtransitionsintoa networkof41molecularcomponents.Wethenreducethisinitialnetworkbyremovingsimplemediators(i.e.,linear pathways),andformalizetheresultingregulatorycoreintologicalrulesthatgovernthedynamicsofeachofthe networkcomponentsasafunctionofthestatesofitsregulators. Results: ComputationaldynamicanalysisshowsthatourproposedGeneRegulatoryNetworkmodelrecoversexactly threeattractors,eachofthemdefinedbyaspecificgeneexpressionprofilethatcorrespondstotheepithelial, senescent,andmesenchymalstem–likecellularphenotypes,respectively.Weshowthatalthoughamesenchymal stem–likestatecanbeattainedevenunderunperturbedphysiologicalconditions,thelikelihoodofconvergingtothis stateisincreasedwhenpro–inflammatoryconditionsaresimulated,providingasystems–levelmechanistic explanationforthecarcinogenicroleofchronicinflammatoryconditionsobservedintheclinic.Wealsofoundthat theregulatorycoreyieldsanepigeneticlandscapethatrestrictstemporalpatternsofprogressionbetweenthesteady states,suchthatrecoveredpatternsresemblethetime–orderedtransitionsobservedduringthespontaneous immortalizationofepithelialcells,bothinvivoandinvitro. Conclusion: Ourstudystronglysuggeststhattheinvitrotumorigenictransformationofepithelialcells,which stronglycorrelateswiththepatternsobservedduringthepathologicalprogressionofepithelialcarcinogenesisinvivo, emergesfromunderlyingregulatorynetworksinvolvedinepithelialtrans–differentiationduringdevelopment. Keywords: Carcinomas,Generegulatorynetworks,Epigeneticlandscape,Booleanmodels,Phenotypicattractor

Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing

The accumulation of protein damage is one of the major drivers of replicative ageing, describing a cell’s reduced ability to reproduce over time even under optimal conditions. Reactive oxygen and nitrogen species are precursors of protein damage and therefore tightly linked to ageing. At the same time, they are an inevitable by-product of the cell’s metabolism. Cells are able to sense high levels of reactive oxygen and nitrogen species and can subsequently adapt their metabolism through gene regulation to slow down damage accumulation. However, the older or damaged a cell is the less flexibility it has to allocate enzymes across the metabolic network, forcing further adaptions in the metabolism. To investigate changes in the metabolism during replicative ageing, we developed an multi-scale mathematical model using budding yeast as a model organism. The model consists of three interconnected modules: a Boolean model of the signalling network, an enzyme-constrained flux balance model of the central carbon metabolism and a dynamic model of growth and protein damage accumulation with discrete cell divisions. The model can explain known features of replicative ageing, like average lifespan and increase in generation time during successive division, in yeast wildtype cells by a decreasing pool of functional enzymes and an increasing energy demand for maintenance. We further used the model to identify three consecutive metabolic phases, that a cell can undergo during its life, and their influence on the replicative potential, and proposed an intervention span for lifespan control

2012 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

URC Schedule and Abstract Book for the Fourth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 17-18, 2012Plenary speaker: Christine E. Heitsch, Associate Professor of Mathematics at Georgia Institute of TechnologyFeatured speaker: John W. Glasser, Center for Disease Contro

Statistical-mechanical lattice models for protein-DNA binding in chromatin

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibriums measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.Comment: 19 pages, 7 figures, accepted author manuscript, to appear in J. Phys.: Cond. Mat

Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Modeling complex cellular systems: from differential equations to constraint-based models

In the beginning of the 20th century, scientists realized the necessity of purifying enzymes to unravel their mechanistic nature. A century and tremendous progresses in the natural sciences later, molecular and systems biology became fundamental pillars of modern biology. Moreover, natural scientists developed an increasing interest in theoretical models. In the first part of my thesis, I present my contribution to the field of studying the dynamics of biological phenomena. I present fundamental issues arising, when neglecting substrate inhibition in kinetic modeling. Furthermore, I describe a model that considers experimental data to simulate the transition of normal proliferating into cellular senescent cells. Since large-scaled models are more comprehensive, they commonly prohibit a mechanistic modeling approach. In order to analyze such models, nevertheless, constraint-based methods proved to be suitable tools. In the second part of my thesis, I contribute three studies to constraint-based modeling. I describe the established concept of elementary flux modes, which resemble non-decomposable and theoretically feasible pathways of metabolic networks. Subsequently, I present the analysis of the nitrogen metabolism network of Chlamydomonas reinhardtii with respect to circadian regulation, which gives rise to about three million elementary flux modes. In the last study, I present a comprehensive work on metabolic costs of amino acid and protein production in Escherichia coli. These costs were manually calculated as well as based on a flux balance analysis of an E. coli genome-scale metabolic model. Both approaches, either dynamic or constraint-based modeling, proved to be suitable strategies to describe biological processes at different levels. Whereas dynamic modeling allowed for a precise description of the temporal behavior of biological species, constraint-based modeling enabled studies, where the complexity of the investigated phenomena prohibits kinetic modeling