Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

Intosalmi, Jukka; Ahlfors, Helena; Rautio, Sini; Mannerstöm, Henrik; Chen, Zhi; Lahesmaa, Riitta; Stockinger, Brigitta; Lähdesmäki, Harri

doi:10.1186/s12918-015-0223-6

Cited by 14 publications

(12 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mechanistic models are capable of describing the actual molecular dynamics originating from reaction kinetics and the underlying network structure can also be accurately estimated by combining these models with appropriate statistical techniques (see e.g. Intosalmi et al , 2015 ; Schulz et al , 2009 ; Xu et al , 2010 ). Typically, a mechanistic model consists of a set of interacting molecular components and a formal description for the possible mechanisms affecting the time-evolution of the components.…”

Section: Introductionmentioning

confidence: 99%

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

et al. 2016

Self Cite

View full text Add to dashboard Cite

Motivation: Mechanistic models based on ordinary differential equations provide powerful and accurate means to describe the dynamics of molecular machinery which orchestrates gene regulation. When combined with appropriate statistical techniques, mechanistic models can be calibrated using experimental data and, in many cases, also the model structure can be inferred from time–course measurements. However, existing mechanistic models are limited in the sense that they rely on the assumption of static network structure and cannot be applied when transient phenomena affect, or rewire, the network structure. In the context of gene regulatory network inference, network rewiring results from the net impact of possible unobserved transient phenomena such as changes in signaling pathway activities or epigenome, which are generally difficult, but important, to account for.Results: We introduce a novel method that can be used to infer dynamically evolving regulatory networks from time–course data. Our method is based on the notion that all mechanistic ordinary differential equation models can be coupled with a latent process that approximates the network structure rewiring process. We illustrate the performance of the method using simulated data and, further, we apply the method to study the regulatory interactions during T helper 17 (Th17) cell differentiation using time–course RNA sequencing data. The computational experiments with the real data show that our method is capable of capturing the experimentally verified rewiring effects of the core Th17 regulatory network. We predict Th17 lineage specific subnetworks that are activated sequentially and control the differentiation process in an overlapping manner.Availability and Implementation: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/lem/.Contacts: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fi

show abstract

Section: Introductionmentioning

confidence: 99%

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Early mathematical models of this differentiation process utilized ordinary differential equations (ODE) and were developed from existing knowledge [22,23]. More recently different modeling techniques have been used, including logical network models [24,25], and increasingly, experimental data have been used to drive the models [26,27]. …”

Section: Differentiation Of Cytokine-producing Cells and The Contrmentioning

confidence: 99%

“…In another approach aimed at investigating Th17 differentiation the authors used a RNASeq dataset generated over a detailed time course to explore the validity of several potential models [27]. In this study a dynamic ODE model of Th17 differentiation was used as a framework and four hypothetical mechanisms were identified that resulted in 12 alternative models for the molecular interactions important for Th17 differentiation.…”

Section: Differentiation Of Cytokine-producing Cells and The Contrmentioning

confidence: 99%

“…This approach identified three models that best fit the RNASeq data, and by incorporating additional protein data on the dose response relationship between TGF-β and Foxp3 expression a single model was identified. The most robust model was one that incorporated the mechanisms of RORγt inhibition by Foxp3 and the inhibition of Foxp3 by STAT3 signaling downstream of IL-6 [27]. This study is a good example of combining modeling with detailed time course experimental data to provide important new insights into the complexities of Th17 differentiation.…”

Section: Differentiation Of Cytokine-producing Cells and The Contrmentioning

confidence: 99%

See 1 more Smart Citation

Demystifying the cytokine network: Mathematical models point the way

2017

View full text Add to dashboard Cite

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

show abstract

“…While being useful in building a general understanding of different mechanisms across the scales, most of these approaches do not allow experimentally-based model construction and validation. Experimentally-based model construction approaches have been successfully applied in the context of mechanistic modeling of molecular mechanisms (Schulz et al, 2009;Intosalmi et al, 2015;Chan et al, 2016) and extending these approaches to more complex multiscale models will be essential for methodological advancement in systems biology (Skupin et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

Intosalmi

Scott

Hays

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Motivation: Multicellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell-cell and metabolic coupling produce functionally optimized structures is still limited. Results: Here, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation. Availability: Experimental data and a computational implementation to reproduce the results are available at

show abstract

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

Cited by 14 publications

References 43 publications

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

Demystifying the cytokine network: Mathematical models point the way

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

Contact Info

Product

Resources

About