TY - JOUR AB - Billions of functionally distinct blood cells emerge from a pool of hematopoietic stem cells in our bodies every day. This progressive differentiation process is hierarchically structured and remarkably robust. We provide an introductory review to mathematical approaches addressing the functional aspects of how lineage choice is potentially implemented on a molecular level. Emerging from studies on the mutual repression of key transcription factors we illustrate how those simple concepts have been challenged in recent years and subsequently extended. Especially the analysis of omics data on the single cell level with computational tools provide descriptive insights on a yet unknown level, while their embedding into a consistent mechanistic and mathematical framework is still incomplete. AU - Glauche, I.* AU - Marr, C. C1 - 62301 C2 - 50762 SP - 100355 TI - Mechanistic models of blood cell fate decisions in the era of single-cell data. JO - Curr. Opin. Syst. Biol. VL - 28 PY - 2021 SN - 2452-3100 ER - TY - JOUR AB - Single cell RNA sequencing measures gene expression at an unprecedented resolution and scale and allows the analysis of cellular phenotypes which was not possible before. In this context, graphs occur as a natural representation of the system - both as gene-centric and cell-centric. However, many advances in machine learning on graphs are not yet harnessed in models on single-cell data. Taking the inference of cell types or gene interactions as examples, graph representation learning has a wide applicability to both cell and gene graphs. Recent advances in spatial molecular profiling additionally put graph-learning in the focus of attention due the innate resemblance of spatial information to spatial graphs. We argue that graph embedding techniques have great potential for various applications across single cell biology. Here, we discuss how graph representation learning maps to current models and concepts used in single cell biology and formalise overlaps to developments in graph-based deep learning. AU - Hetzel, L. AU - Fischer, D.S. AU - Günnemann, S.* AU - Theis, F.J. C1 - 64180 C2 - 52518 TI - Graph representation learning for single cell biology. JO - Curr. Opin. Syst. Biol. VL - 28 PY - 2021 SN - 2452-3100 ER - TY - JOUR AB - As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamic models of metabolism allow for the integration of heterogeneous data and the analysis of dynamic phenotypes. Here, we review recent efforts in using dynamic metabolic models for data integration, focusing on approaches based on ordinary differential equations that are applicable to both time-resolved and steady-state measurements and that do not require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and although challenges remain, dynamic modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential. AU - Lakrisenko, P. AU - Weindl, D. C1 - 62801 C2 - 51070 TI - Dynamic models for metabolomics data integration. JO - Curr. Opin. Syst. Biol. VL - 28 PY - 2021 SN - 2452-3100 ER - TY - JOUR AB - Cellular signaling is essential in information processing and decision-making. Therefore, a variety of experimental approaches have been developed to study signaling on bulk and single-cell level. Single-cell measurements of signaling molecules demonstrated a substantial cell-to-cell variability, raising questions about its causes and mechanisms and about how cell populations cope with or exploit cellular heterogeneity. To gain insights from single-cell signaling data, analysis and modeling approaches have been introduced. This review discusses these modeling approaches, with a focus on recent advances in the development and calibration of mechanistic models. Additionally, it outlines current and future challenges. AU - Loos, C. AU - Hasenauer, J. C1 - 57536 C2 - 47829 SP - 17-24 TI - Mathematical modeling of variability in intracellular signaling. JO - Curr. Opin. Syst. Biol. VL - 16 PY - 2019 SN - 2452-3100 ER - TY - JOUR AB - Single cell high throughput genomic measurements are revolutionizing the fields of biology and medicine, providing a means to tackle biological problems that have thus far been inaccessible, such as the systematic discovery of new cell types, the identification of cellular heterogeneity in health and disease, or the cell-fate decisions taking place during differentiation and reprogramming. Recently implemented multi–omics measurements of genomes, transcriptomes, epigenomes, proteomes and chromatin organization are opening up new avenues to begin to disentangle the causal relationship between -omics layers and how these co-determine higher-order cellular phenotypes. This technological revolution is not restricted to basic science but promises major breakthroughs in medical diagnostics and treatments. In this paper we review existing computational methods for the analysis and integration of different -omics layers and discuss what new approaches are needed to leverage the full potential of single cell multi-omics data. AU - Colomé-Tatché, M. AU - Theis, F.J. C1 - 53898 C2 - 45087 SP - 54-59 TI - Statistical single cell multi-omics integration. JO - Curr. Opin. Syst. Biol. VL - 7 PY - 2018 SN - 2452-3100 ER - TY - JOUR AB - Recent technological advances have enabled unprecedented insight into transcriptomics at the level of single cells. Single cell transcriptomics enables the measurement of tran- scriptomic information of thousands of single cells in a single experiment. The volume and complexity of resulting data make it a paradigm of big data. Consequently, the field is presented with new scientific and, in particular, analytical challenges where currently no scalable solutions exist. At the same time, exciting opportunities arise from increased resolution of single- cell RNA sequencing data and improved statistical power of ever growing datasets. Big single cell RNA sequencing data promises valuable insights into cellular heterogeneity which may significantly improve our understanding of biology and human disease. This review focuses on single cell tran- scriptomics and highlights the inherent opportunities and challenges in the context of big data analytics. AU - Angerer, P. AU - Simon, L. AU - Tritschler, S. AU - Wolf, F.A. AU - Fischer, D. AU - Theis, F.J. C1 - 52199 C2 - 43795 SP - 85-91 TI - Single cells make big data: New challenges and opportunities in transcriptomics. JO - Curr. Opin. Syst. Biol. VL - 4 PY - 2017 SN - 2452-3100 ER - TY - JOUR AU - Falter-Braun, P. AU - Calderwood, M.A.* C1 - 51980 C2 - 43685 SP - iv-vi TI - Big data biology - just the next big hype? JO - Curr. Opin. Syst. Biol. VL - 4 PY - 2017 SN - 2452-3100 ER - TY - JOUR AB - Systemic phenotyping of mutant mice has been established at large scale in the last decade as a new tool to uncover the relations between genotype, phenotype and environment. Recent advances in that field led to the generation of a valuable open access data resource that can be used to better understanding the underlying causes for human diseases. From an ethical perspective, systemic phenotyping significantly contributes to the reduction of experimental animals and the refinement of animal experiments by enforcing standardisation efforts. There are particular logistical, experimental and analytical challenges of systemic large-scale mouse phenotyping. On all levels, IT solutions are critical to implement and efficiently support breeding, phenotyping and data analysis processes that lead to the generation of high-quality systemic phenotyping data accessible for the scientific community. AU - Maier, H. AU - Leuchtenberger, S. AU - Fuchs, H. AU - Gailus-Durner, V. AU - Hrabě de Angelis, M. C1 - 51688 C2 - 43361 SP - 97-104 TI - Big data in large-scale systemic mouse phenotyping. JO - Curr. Opin. Syst. Biol. VL - 4 PY - 2017 SN - 2452-3100 ER -