Topological sensitivity analysis for systems biology.

Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.

MOTIVATION: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system's moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. RESULTS: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/theosysbio/means CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

Haematopoietic stem and progenitor cells (HSPCs) develop through distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) is able to recapitulate some of these processes, however, it has proven difficult to generate functional haematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we exploited single cell RNA sequencing (scRNAseq) in combination with single cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors as well as erythroid, megakaryocyte and leukocyte-committed progenitors and we identified CD44, CD326, ICAM2/CD9 and CD18 as markers of these progenitors, respectively. Using an artificial neural network (ANN), that we trained on a scRNAseq derived from human fetal liver, we were able to identify a wide range of hPSCs-derived HPSC phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded and was completely missing in the dataset that had been generated using cells selected on the basis of CD43expression. By comparing the single cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver we identified transcription factors and molecular pathways that can be exploited in the future to improve the in vitro production of HSCs

Stem cell differentiation as a non-Markov stochastic process

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular ‘macrostates’ and functionally similar molecular ‘microstates’, and propose a model of stem cell differentiation as a non-Markov stochastic process