Toward Genome‐Scale Models of Signal Transduction Networks

Münzner, Ulrike; Lubitz, Timo; Klipp, Edda; Krantz, Marcus

doi:10.1002/9783527696130.ch8

Cited by 12 publications

(16 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although this continues to be debated, more recent advances in CBM have included the development of more sophisticated algorithms that allow modelling of more complex behaviours, as briefly discussed later in this review. Additionally, the majority of genome scale models are based on metabolism rather than signalling, although this area is also progressing (Hyduke and Palsson, 2010; Münzner et al, 2017).…”

Section: Advances In Systems Mitochondrial Biologymentioning

confidence: 99%

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

et al. 2019

View full text Add to dashboard Cite

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, “bottom-up” systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

show abstract

Section: Advances In Systems Mitochondrial Biologymentioning

confidence: 99%

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

et al. 2019

View full text Add to dashboard Cite

show abstract

“…6 An open source R package (v 2. This can also be done through the Anaconda Navigator, if Anaconda is installed, in which case the RStudio install includes R. 8 R can be installed through Anaconda, by opening the console and typing: "conda install -c r ressentials" 9 In the console, type "R" to enter the R environment. Then type "install.packages("BoolNet")"…”

Section: Figure Legendsmentioning

confidence: 99%

Using rxncon to Develop Rule-Based Models

Romers

Thieme

Münzner

et al. 2019

Modeling Biomolecular Site Dynamics

Self Cite

View full text Add to dashboard Cite

We present a protocol for building, validating and simulating models of signal transduction networks.These networks are challenging modelling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalise the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalise large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signalling pathway to illustrate the protocol, together with some of the challenges -and some of their solutions -in modelling signal transduction.

show abstract

“…A systems perspective on signaling is required to better understand responses to co-stimulation and predict the effects of drugs. Such an understanding could be obtained through genome-scale computer simulations of signaling that have long been anticipated [6][7][8] . By now, many requisites for genome-scale models of signaling are in place.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks enable genome-scale simulations of intracellular signaling

Nilsson

Peters

Bryson

et al. 2021

Preprint

View full text Add to dashboard Cite

Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor (TF) activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network constrained by prior knowledge of the signaling network with ligand concentrations as input, TF activity as output and signaling molecules as hidden nodes. Simulations are assumed to reach steady state, and we regularize the parameters to enforce this. Using synthetic data, we train models that generalize to unseen data and predict the effects of gene knockouts. We also fit models to a small experimental data set from literature and confirm the predictions using cross validation. This demonstrates the feasibility of simulating intracellular signaling at the genome-scale.

show abstract

Toward Genome‐Scale Models of Signal Transduction Networks

Cited by 12 publications

References 54 publications

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Using rxncon to Develop Rule-Based Models

Artificial neural networks enable genome-scale simulations of intracellular signaling

Contact Info

Product

Resources

About