17 results found
Fidanza A, Stumpf PS, Ramachandran P, et al., 2020, Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs, Blood, Vol: 136, Pages: 2893-2904, ISSN: 0006-4971
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.
Babtie AC, 2019, Modelling heterogeneous intracellular networks at the cellular scale, Current Opinion in Systems Biology, Vol: 16, Pages: 10-16, ISSN: 2452-3100
Cell function relies on the coordinated action of heterogeneous interconnected networks of biomolecules. Mathematical models help us explore the dynamics and behaviour of these intracellular networks in greater detail. Models of increasing scale and complexity are being developed to probe cellular processes, often necessitating the use of several types of mathematical representation in hybrid models. Here we review recent efforts to incorporate the influences of stochasticity and spatial heterogeneity into cellular level models, ranging from abstract coarse-grained representations to large-scale hybrid models comprising thousands of biological components. We discuss the key challenges involved in, and recent mathematical advances enabling the development and analysis of mathematical models of complex intracellular processes.
Chan TE, Stumpf MPH, Babtie AC, 2019, Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions., Methods Mol Biol, Vol: 1975, Pages: 211-238
Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.
Babtie AC, Chan TE, Stumpf MPH, 2017, Learning regulatory models for cell development from single cell transcriptomic data, Current Opinion in Systems Biology, Vol: 5, Pages: 72-81, ISSN: 2452-3100
Stumpf PS, Smith RCG, Lenz M, et al., 2017, Stem cell differentiation as a non-Markov stochastic process, Cell Systems, Vol: 5, Pages: 268-282.e7, ISSN: 2405-4712
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.
Chan TE, Stumpf MPH, Babtie AC, 2017, Gene regulatory network inference from sngle-cell data using multivariate information measures, Cell Systems, Vol: 5, Pages: 251-267.e3, ISSN: 2405-4712
While single-cell gene expression experiments presentnew challenges for data processing, the cellto-cellvariability observed also reveals statisticalrelationships that can be used by information theory.Here, we use multivariate information theory toexplore the statistical dependencies between tripletsof genes in single-cell gene expression datasets. Wedevelop PIDC, a fast, efficient algorithm that usespartial information decomposition (PID) to identifyregulatory relationships between genes. We thoroughlyevaluate the performance of our algorithmand demonstrate that the higher-order informationcaptured by PIDC allows it to outperform pairwisemutual information-based algorithms when recoveringtrue relationships present in simulated data.We also infer gene regulatory networks from threeexperimental single-cell datasets and illustrate hownetwork context, choices made during analysis,and sources of variability affect network inference.PIDC tutorials and open-source software for estimatingPID are available. PIDC should facilitate theidentification of putative functional relationshipsand mechanistic hypotheses from single-cell transcriptomicdata.
Babtie AC, Stumpf MPH, 2017, How to deal with parameters for whole-cell modelling, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 14, ISSN: 1742-5689
Ale A, Crepin VF, Collins, et al., 2016, Model of host-pathogen Interaction dynamics links In vivo optical imaging and immune responses, Infection and Immunity, Vol: 85, ISSN: 1098-5522
Tracking disease progression in vivo is essential for the development of treatments against bacterial infection. Optical imaging has become a central tool for in vivo tracking of bacterial population development and therapeutic response. For a precise understanding of in vivo imaging results in terms of disease mechanisms derived from detailed postmortem observations, however, a link between the two is needed. Here, we develop a model that provides that link for the investigation of Citrobacter rodentium infection, a mouse model for enteropathogenic Escherichia coli (EPEC). We connect in vivo disease progression of C57BL/6 mice infected with bioluminescent bacteria, imaged using optical tomography and X-ray computed tomography, to postmortem measurements of colonic immune cell infiltration. We use the model to explore changes to both the host immune response and the bacteria and to evaluate the response to antibiotic treatment. The developed model serves as a novel tool for the identification and development of new therapeutic interventions.
Fan S, Geissmann Q, Lakatos E, et al., 2016, MEANS: python package for Moment Expansion Approximation, iNference and Simulation, Bioinformatics, Vol: 32, Pages: 2863-2865, ISSN: 1367-4803
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@example.com or firstname.lastname@example.orgSupplementary information: Supplementary data are available at Bioinformatics online.
Kirk PDW, Babtie AC, Stumpf MPH, 2015, Systems biology (un)certainties, SCIENCE, Vol: 350, Pages: 386-388, ISSN: 0036-8075
Babtie AC, Kirk P, Stumpf MPH, 2014, Topological sensitivity analysis for systems biology, Proceedings of the National Academy of Sciences of the United States of America, Vol: 111, Pages: 18507-18512, ISSN: 0027-8424
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.
Zhang J, Babtie A, Stephanopoulos G, 2012, Metabolic engineering: enabling technology of a bio-based economy, Current Opinion in Chemical Engineering, Vol: 1, Pages: 355-362, ISSN: 2211-3398
Babtie AC, Lima MF, Kirby AJ, et al., 2012, Kinetic and computational evidence for an intermediate in the hydrolysis of sulfonate esters, ORGANIC & BIOMOLECULAR CHEMISTRY, Vol: 10, Pages: 8095-8101, ISSN: 1477-0520
Babtie A, Tokuriki N, Hollfelder F, 2010, What makes an enzyme promiscuous?, Current Opinion in Chemical Biology, Vol: 14, Pages: 200-207, ISSN: 1367-5931
van Loo B, Jonas S, Babtie AC, et al., 2010, An efficient, multiply promiscuous hydrolase in the alkaline phosphatase superfamily, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 107, Pages: 2740-2745, ISSN: 0027-8424
Shim J-U, Olguin LF, Whyte G, et al., 2009, Simultaneous Determination of Gene Expression and Enzymatic Activity in Individual Bacterial Cells in Microdroplet Compartments, Journal of the American Chemical Society, Vol: 131, Pages: 15251-15256, ISSN: 0002-7863
Babtie AC, Bandyopadhyay S, Olguin LF, et al., 2009, Efficient Catalytic Promiscuity for Chemically Distinct Reactions, ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, Vol: 48, Pages: 3692-3694, ISSN: 1433-7851
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