179 results found
Ale A, Crepin VF, Collins JW, et al., 2017, Model of Host-Pathogen Interaction Dynamics Links In Vivo Optical Imaging and Immune Responses, INFECTION AND IMMUNITY, Vol: 85, ISSN: 0019-9567
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
Chan TE, Stumpf MPH, Babtie AC, 2017, Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures, CELL SYSTEMS, Vol: 5, Pages: 251-+, ISSN: 2405-4712
Dianzani C, Bellavista E, Liepe J, et al., 2017, Extracellular proteasome-osteopontin circuit regulates cell migration with implications in multiple sclerosis, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Fisher AG, Stumpf MPH, Merkenschlager M, 2017, Reconciling Epigenetic Memory and Transcriptional Responsiveness, CELL SYSTEMS, Vol: 4, Pages: 373-374, ISSN: 2405-4712
Hansel TT, Tunstall T, Trujillo-Torralbo M-B, et al., 2017, A Comprehensive Evaluation of Nasal and Bronchial Cytokines and Chemokines Following Experimental Rhinovirus Infection in Allergic Asthma: Increased Interferons (IFN-gamma and IFN-lambda) and Type 2 Inflammation (IL-5 and IL-13), EBIOMEDICINE, Vol: 19, Pages: 128-138, ISSN: 2352-3964
Lakatos E, Salehi-Reyhani A, Barclay M, et al., 2017, Protein degradation rate is the dominant mechanism accounting for the differences in protein abundance of basal p53 in a human breast and colorectal cancer cell line, PLOS ONE, Vol: 12, ISSN: 1932-6203
Lakatos E, Stumpf MPH, 2017, Control mechanisms for stochastic biochemical systems via computation of reachable sets, ROYAL SOCIETY OPEN SCIENCE, Vol: 4, ISSN: 2054-5703
MacLean AL, Lo Celso C, Stumpf MPH, 2017, Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis, STEM CELLS, Vol: 35, Pages: 80-88, ISSN: 1066-5099
MacLean AL, Smith MA, Liepe J, et al., 2017, Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection, STEM CELLS, Vol: 35, Pages: 2292-2304, ISSN: 1066-5099
Crowell HL, MacLean AL, Stumpf MPH, 2016, Feedback mechanisms control coexistence in a stem cell model of acute myeloid leukaemia, JOURNAL OF THEORETICAL BIOLOGY, Vol: 401, Pages: 43-53, ISSN: 0022-5193
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: firstname.lastname@example.org or email@example.comSupplementary information: Supplementary data are available at Bioinformatics online.
Filippi S, Barnes CP, Kirk PDW, et al., 2016, Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling, CELL REPORTS, Vol: 15, Pages: 2524-2535, ISSN: 2211-1247
Lenive O, Kirk PDW, Stumpf MPH, 2016, Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation, BMC SYSTEMS BIOLOGY, Vol: 10, ISSN: 1752-0509
Liepe J, Marino F, Sidney J, et al., 2016, A large fraction of HLA class I ligands are proteasome-generated spliced peptides, SCIENCE, Vol: 354, Pages: 354-358, ISSN: 0036-8075
Liepe J, Sim A, Weavers H, et al., 2016, Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data, CELL SYSTEMS, Vol: 3, Pages: 102-107, ISSN: 2405-4712
MacLean AL, Harrington HA, Stumpf MPH, et al., 2016, Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study., Methods Mol Biol, Vol: 1386, Pages: 405-439
The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.
Smadbeck P, Stumpf MPH, 2016, Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 13, ISSN: 1742-5689
Vainieri ML, Blagborough AM, MacLean AL, et al., 2016, Systematic tracking of altered haematopoiesis during sporozoite-mediated malaria development reveals multiple response points, OPEN BIOLOGY, Vol: 6, ISSN: 2046-2441
Weavers H, Liepe J, Sim A, et al., 2016, Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient, CURRENT BIOLOGY, Vol: 26, Pages: 1975-1989, ISSN: 0960-9822
Zurauskiene J, Kirk PDW, Stumpf MPH, 2016, A graph theoretical approach to data fusion, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 15, Pages: 107-122, ISSN: 2194-6302
Johnsony R, Kirky P, Stumpf MPH, 2015, SYSBIONS: nested sampling for systems biology, BIOINFORMATICS, Vol: 31, Pages: 604-605, ISSN: 1367-4803
Jones PJM, Sim A, Taylor HB, et al., 2015, Inference of random walk models to describe leukocyte migration, PHYSICAL BIOLOGY, Vol: 12, ISSN: 1478-3967
Jovanovic G, Sheng X, Ale A, et al., 2015, Phosphorelay of non-orthodox two component systems functions through a bi-molecular mechanism in vivo: the case of ArcB, MOLECULAR BIOSYSTEMS, Vol: 11, Pages: 1348-1359, ISSN: 1742-206X
Kirk P, Rolando DMY, MacLean AL, et al., 2015, Conditional random matrix ensembles and the stability of dynamical systems, New Journal of Physics, Vol: 17, ISSN: 1367-2630
Random matrix theory (RMT) has found applications throughout physics and applied mathematics, in subject areas as diverse as communications networks, population dynamics, neuroscience, and models of the banking system. Many of these analyses exploit elegant analytical results, particularly the circular law and its extensions. In order to apply these results, assumptions must be made about the distribution of matrix elements. Here we demonstrate that the choice of matrix distribution is crucial. In particular, adopting an unrealistic matrix distribution for the sake of analytical tractability is liable to lead to misleading conclusions. We focus on the application of RMT to the long-standing, and at times fractious, 'diversity-stability debate', which is concerned with establishing whether large complex systems are likely to be stable. Early work (and subsequent elaborations) brought RMT to bear on the debate by modelling the entries of a system's Jacobian matrix as independent and identically distributed (i.i.d.) random variables. These analyses were successful in yielding general results that were not tied to any specific system, but relied upon a restrictive i.i.d. assumption. Other studies took an opposing approach, seeking to elucidate general principles of stability through the analysis of specific systems. Here we develop a statistical framework that reconciles these two contrasting approaches. We use a range of illustrative dynamical systems examples to demonstrate that: (i) stability probability cannot be summarily deduced from any single property of the system (e.g. its diversity); and (ii) our assessment of stability depends on adequately capturing the details of the systems analysed. Failing to condition on the structure of dynamical systems will skew our analysis and can, even for very small systems, result in an unnecessarily pessimistic diagnosis of their stability.
Kirk PDW, Babtie AC, Stumpf MPH, 2015, Systems biology (un)certainties, SCIENCE, Vol: 350, Pages: 386-388, ISSN: 0036-8075
Lakatos E, Ale A, Kirk PDW, et al., 2015, Multivariate moment closure techniques for stochastic kinetic models, JOURNAL OF CHEMICAL PHYSICS, Vol: 143, ISSN: 0021-9606
Liepe J, Holzhuetter H-G, Bellavista E, et al., 2015, Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes, ELIFE, Vol: 4, ISSN: 2050-084X
Liepe J, Holzhütter HG, Bellavista E, et al., 2015, Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes, eLife, Vol: 4
© Liepe et al. Proteasomal protein degradation is a key determinant of protein half-life and hence of cellular processes ranging from basic metabolism to a host of immunological processes. Despite its importance the mechanisms regulating proteasome activity are only incompletely understood. Here we use an iterative and tightly integrated experimental and modelling approach to develop, explore and validate mechanistic models of proteasomal peptide-hydrolysis dynamics. The 20S proteasome is a dynamic enzyme and its activity varie s over time because of interactions between substrates and products and the proteolytic and regulatory sites; the locations of these sites and the interactions between them are predicted by the model, and experimentally supported. The analysis suggests that the rate-limiting step of hydrolysis is the transport of the substrates into the proteasome. The transport efficiency varies between human standard- and immuno-proteasomes thereby impinging upon total degradation rate and substrate cleavage-site usage.
MacLean AL, Kirk PDW, Stumpf MPH, 2015, Cellular population dynamics control the robustness of the stem cell niche, BIOLOGY OPEN, Vol: 4, Pages: 1420-1426, ISSN: 2046-6390
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