Publications
2022
Megan A. Coomer; Lucy Ham; Michael P.H. Stumpf
Noise distorts the epigenetic landscape and shapes cell-fate decisions Journal Article
In: Cell Systems, vol. 13, no. 1, pp. 83-102.e6, 2022, ISSN: 2405-4712.
Abstract | Links | BibTeX | Tags: Feature
@article{coomer2022,
title = {Noise distorts the epigenetic landscape and shapes cell-fate decisions},
author = {Megan A. Coomer and Lucy Ham and Michael P.H. Stumpf},
doi = {10.1016/j.cels.2021.09.002},
issn = {2405-4712},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Cell Systems},
volume = {13},
number = {1},
pages = {83-102.e6},
abstract = {The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data. A record of this paper’s transparent peer review process is included in the supplemental information.},
keywords = {Feature},
pubstate = {published},
tppubtype = {article}
}
The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data. A record of this paper’s transparent peer review process is included in the supplemental information.
2021
Sean T. Vittadello; Michael P.H. Stumpf
Model comparison via simplicial complexes and persistent homology Journal Article
In: Royal Society Open Science, vol. 8, no. 10, pp. 211361, 2021, ISSN: 2054-5703.
Abstract | Links | BibTeX | Tags:
@article{vittadello2021a,
title = {Model comparison via simplicial complexes and persistent homology},
author = {Sean T. Vittadello and Michael P.H. Stumpf},
doi = {10.1098/rsos.211361},
issn = {2054-5703},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Royal Society Open Science},
volume = {8},
number = {10},
pages = {211361},
abstract = {In many scientific and technological contexts, we have only a poor understanding of the structure and details of appropriate mathematical models. We often, therefore, need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. There is, however, a lack of rigorous methods to compare different models a priori. Here, we develop and illustrate two such approaches that allow us to compare model structures in a systematic way by representing models as simplicial complexes. Using well-developed concepts from simplicial algebraic topology, we define a distance between models based on their simplicial representations. Employing persistent homology with a flat filtration provides for alternative representations of the models as persistence intervals, which represent model structure, from which the model distances are also obtained. We then expand on this measure of model distance to study the concept of model equivalence to determine the conceptual similarity of models. We apply our methodology for model comparison to demonstrate an equivalence between a positional-information model and a Turing-pattern model from developmental biology, constituting a novel observation for two classes of models that were previously regarded as unrelated.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In many scientific and technological contexts, we have only a poor understanding of the structure and details of appropriate mathematical models. We often, therefore, need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. There is, however, a lack of rigorous methods to compare different models a priori. Here, we develop and illustrate two such approaches that allow us to compare model structures in a systematic way by representing models as simplicial complexes. Using well-developed concepts from simplicial algebraic topology, we define a distance between models based on their simplicial representations. Employing persistent homology with a flat filtration provides for alternative representations of the models as persistence intervals, which represent model structure, from which the model distances are also obtained. We then expand on this measure of model distance to study the concept of model equivalence to determine the conceptual similarity of models. We apply our methodology for model comparison to demonstrate an equivalence between a positional-information model and a Turing-pattern model from developmental biology, constituting a novel observation for two classes of models that were previously regarded as unrelated.
2020
Lucy Ham; David Schnoerr; Rowan D. Brackston; Michael P.H. Stumpf
Exactly solvable models of stochastic gene expression Journal Article
In: The Journal of Chemical Physics, vol. 152, pp. 144106, 2020.
Abstract | Links | BibTeX | Tags:
@article{ham2020b,
title = {Exactly solvable models of stochastic gene expression},
author = {Lucy Ham and David Schnoerr and Rowan D. Brackston and Michael P.H. Stumpf},
doi = {10.1063/1.5143540},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {The Journal of Chemical Physics},
volume = {152},
pages = {144106},
abstract = {Stochastic models are key to understanding the intricate dynamics of gene expression. However, the simplest models that only account for active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here, we consider multistate models of gene expression that generalize the canonical Telegraph process and are capable of capturing the joint effects of transcription factors, heterochromatin state, and DNA accessibility (or, in prokaryotes, sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalized systems. The first approach offers a fresh perspective on a general class of multistate models and allows us to “decompose” more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. We further show that models from both classes cannot have a heavy-tailed distribution in the absence of extrinsic noise. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems and control the behavior of naturally evolved gene expression systems in guiding cell-fate decisions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stochastic models are key to understanding the intricate dynamics of gene expression. However, the simplest models that only account for active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here, we consider multistate models of gene expression that generalize the canonical Telegraph process and are capable of capturing the joint effects of transcription factors, heterochromatin state, and DNA accessibility (or, in prokaryotes, sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalized systems. The first approach offers a fresh perspective on a general class of multistate models and allows us to “decompose” more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. We further show that models from both classes cannot have a heavy-tailed distribution in the absence of extrinsic noise. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems and control the behavior of naturally evolved gene expression systems in guiding cell-fate decisions.
2019
Leander Dony; Fei He; Michael P.H. Stumpf
Parametric and non-parametric gradient matching for network inference: a comparison Journal Article
In: BMC Bioinformatics, vol. 20, no. 1, 2019, ISSN: 1471-2105.
Links | BibTeX | Tags: Networks | trees and cell structures
@article{Dony2019,
title = {Parametric and non-parametric gradient matching for network inference: a comparison},
author = {Leander Dony and Fei He and Michael P.H. Stumpf},
doi = {10.1186/s12859-018-2590-7},
issn = {1471-2105},
year = {2019},
date = {2019-12-00},
urldate = {2019-12-00},
journal = {BMC Bioinformatics},
volume = {20},
number = {1},
publisher = {Springer Science and Business Media LLC},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
2018
Tomasz Jetka; Karol Nienałtowski; Sarah Filippi; Michael P.H. Stumpf; Michał Komorowski
An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling Journal Article
In: Nat Commun, vol. 9, no. 1, 2018, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags: Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics
@article{Jetka2018,
title = {An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling},
author = {Tomasz Jetka and Karol Nienałtowski and Sarah Filippi and Michael P.H. Stumpf and Michał Komorowski},
doi = {10.1038/s41467-018-07085-1},
issn = {2041-1723},
year = {2018},
date = {2018-12-00},
urldate = {2018-12-00},
journal = {Nat Commun},
volume = {9},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {<jats:title>Abstract</jats:title><jats:p>Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.</jats:p>},
keywords = {Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.</jats:p>
2017
Adam L. MacLean; Maia A. Smith; Juliane Liepe; Aaron Sim; Reema Khorshed; Narges M. Rashidi; Nico Scherf; Axel Krinner; Ingo Roeder; Cristina Lo Celso; Michael P.H. Stumpf
Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection Journal Article
In: vol. 35, no. 11, pp. 2292–2304, 2017, ISSN: 1549-4918.
Abstract | Links | BibTeX | Tags: Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics
@article{MacLean2017,
title = {Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection},
author = {Adam L. MacLean and Maia A. Smith and Juliane Liepe and Aaron Sim and Reema Khorshed and Narges M. Rashidi and Nico Scherf and Axel Krinner and Ingo Roeder and Cristina Lo Celso and Michael P.H. Stumpf},
doi = {10.1002/stem.2692},
issn = {1549-4918},
year = {2017},
date = {2017-11-01},
urldate = {2017-11-01},
volume = {35},
number = {11},
pages = {2292--2304},
publisher = {Oxford University Press (OUP)},
abstract = {<jats:title>Abstract</jats:title>
<jats:p>The hematopoietic stem cell (HSC) niche provides essential microenvironmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, hematopoietic dynamics are perturbed, but it is not known whether changes to the HSC–niche interaction occur as a result. We visualize HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behavior: (a) a pattern of revisiting previously explored space and (b) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (a), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC–niche microenvironments following infection.</jats:p>},
keywords = {Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title>
<jats:p>The hematopoietic stem cell (HSC) niche provides essential microenvironmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, hematopoietic dynamics are perturbed, but it is not known whether changes to the HSC–niche interaction occur as a result. We visualize HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behavior: (a) a pattern of revisiting previously explored space and (b) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (a), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC–niche microenvironments following infection.</jats:p>
<jats:p>The hematopoietic stem cell (HSC) niche provides essential microenvironmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, hematopoietic dynamics are perturbed, but it is not known whether changes to the HSC–niche interaction occur as a result. We visualize HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behavior: (a) a pattern of revisiting previously explored space and (b) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (a), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC–niche microenvironments following infection.</jats:p>
Thalia E. Chan; Michael P.H. Stumpf; Ann C. Babtie
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures Journal Article
In: Cell Systems, vol. 5, no. 3, pp. 251–267.e3, 2017, ISSN: 2405-4712.
Links | BibTeX | Tags: Networks | trees and cell structures
@article{Chan2017,
title = {Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures},
author = {Thalia E. Chan and Michael P.H. Stumpf and Ann C. Babtie},
doi = {10.1016/j.cels.2017.08.014},
issn = {2405-4712},
year = {2017},
date = {2017-09-00},
urldate = {2017-09-00},
journal = {Cell Systems},
volume = {5},
number = {3},
pages = {251--267.e3},
publisher = {Elsevier BV},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
Ann C. Babtie; Michael P.H. Stumpf
How to deal with parameters for whole-cell modelling Journal Article
In: J. R. Soc. Interface., vol. 14, no. 133, 2017, ISSN: 1742-5662.
Abstract | Links | BibTeX | Tags: None
@article{Babtie2017,
title = {How to deal with parameters for whole-cell modelling},
author = {Ann C. Babtie and Michael P.H. Stumpf},
doi = {10.1098/rsif.2017.0237},
issn = {1742-5662},
year = {2017},
date = {2017-08-00},
urldate = {2017-08-00},
journal = {J. R. Soc. Interface.},
volume = {14},
number = {133},
publisher = {The Royal Society},
abstract = {<jats:p>Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.</jats:p>
2016
Helen Weavers; Juliane Liepe; Aaron Sim; Will Wood; Paul Martin; Michael P.H. Stumpf
Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient Journal Article
In: Current Biology, vol. 26, no. 15, pp. 1975–1989, 2016, ISSN: 0960-9822.
Links | BibTeX | Tags: Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics
@article{Weavers2016,
title = {Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient},
author = {Helen Weavers and Juliane Liepe and Aaron Sim and Will Wood and Paul Martin and Michael P.H. Stumpf},
doi = {10.1016/j.cub.2016.06.012},
issn = {0960-9822},
year = {2016},
date = {2016-08-00},
urldate = {2016-08-00},
journal = {Current Biology},
volume = {26},
number = {15},
pages = {1975--1989},
publisher = {Elsevier BV},
keywords = {Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics},
pubstate = {published},
tppubtype = {article}
}
Sarah Filippi; Chris P. Barnes; Paul D.W. Kirk; Takamasa Kudo; Katsuyuki Kunida; Siobhan S. McMahon; Takaho Tsuchiya; Takumi Wada; Shinya Kuroda; Michael P.H. Stumpf
Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling Journal Article
In: Cell Reports, vol. 15, no. 11, pp. 2524–2535, 2016, ISSN: 2211-1247.
Links | BibTeX | Tags: Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics
@article{Filippi2016,
title = {Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling},
author = {Sarah Filippi and Chris P. Barnes and Paul D.W. Kirk and Takamasa Kudo and Katsuyuki Kunida and Siobhan S. McMahon and Takaho Tsuchiya and Takumi Wada and Shinya Kuroda and Michael P.H. Stumpf},
doi = {10.1016/j.celrep.2016.05.024},
issn = {2211-1247},
year = {2016},
date = {2016-06-00},
urldate = {2016-06-00},
journal = {Cell Reports},
volume = {15},
number = {11},
pages = {2524--2535},
publisher = {Elsevier BV},
keywords = {Fate cells | Cell states and Cell maps, Stochastic and nonlinear biological dynamics},
pubstate = {published},
tppubtype = {article}
}
2015
Paul D.W. Kirk; Ann C. Babtie; Michael P.H. Stumpf
Systems biology (un)certainties Journal Article
In: Science, vol. 350, no. 6259, pp. 386–388, 2015, ISSN: 1095-9203.
Abstract | Links | BibTeX | Tags: None
@article{Kirk2015,
title = {Systems biology (un)certainties},
author = {Paul D.W. Kirk and Ann C. Babtie and Michael P.H. Stumpf},
doi = {10.1126/science.aac9505},
issn = {1095-9203},
year = {2015},
date = {2015-10-23},
urldate = {2015-10-23},
journal = {Science},
volume = {350},
number = {6259},
pages = {386--388},
publisher = {American Association for the Advancement of Science (AAAS)},
abstract = {<jats:p>How can modelers restore confidence in systems and computational biology?</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>How can modelers restore confidence in systems and computational biology?</jats:p>
Juliane Liepe; Hermann-Georg Holzhütter; Elena Bellavista; Peter M. Kloetzel; Michael P.H. Stumpf; Michele Mishto
Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes Journal Article
In: vol. 4, 2015, ISSN: 2050-084X.
Abstract | Links | BibTeX | Tags: None
@article{Liepe2015,
title = {Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes},
author = {Juliane Liepe and Hermann-Georg Holzhütter and Elena Bellavista and Peter M. Kloetzel and Michael P.H. Stumpf and Michele Mishto},
doi = {10.7554/elife.07545},
issn = {2050-084X},
year = {2015},
date = {2015-09-22},
urldate = {2015-09-22},
volume = {4},
publisher = {eLife Sciences Publications, Ltd},
abstract = {<jats:p>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 varies 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.</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>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 varies 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.</jats:p>
Siobhan S. Mc Mahon; Oleg Lenive; Sarah Filippi; Michael P.H. Stumpf
Information processing by simple molecular motifs and susceptibility to noise Journal Article
In: J. R. Soc. Interface., vol. 12, no. 110, 2015, ISSN: 1742-5662.
Abstract | Links | BibTeX | Tags: None
@article{McMahon2015,
title = {Information processing by simple molecular motifs and susceptibility to noise},
author = {Siobhan S. Mc Mahon and Oleg Lenive and Sarah Filippi and Michael P.H. Stumpf},
doi = {10.1098/rsif.2015.0597},
issn = {1742-5662},
year = {2015},
date = {2015-09-00},
urldate = {2015-09-00},
journal = {J. R. Soc. Interface.},
volume = {12},
number = {110},
publisher = {The Royal Society},
abstract = {<jats:p>Biological organisms rely on their ability to sense and respond appropriately to their environment. The molecular mechanisms that facilitate these essential processes are however subject to a range of random effects and stochastic processes, which jointly affect the reliability of information transmission between receptors and, for example, the physiological downstream response. Information is mathematically defined in terms of the entropy; and the extent of information flowing across an information channel or signalling system is typically measured by the ‘mutual information’, or the reduction in the uncertainty about the output once the input signal is known. Here, we quantify how extrinsic and intrinsic noise affects the transmission of simple signals along simple motifs of molecular interaction networks. Even for very simple systems, the effects of the different sources of variability alone and in combination can give rise to bewildering complexity. In particular, extrinsic variability is apt to generate ‘apparent’ information that can, in extreme cases, mask the actual information that for a single system would flow between the different molecular components making up cellular signalling pathways. We show how this artificial inflation in apparent information arises and how the effects of different types of noise alone and in combination can be understood.</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Biological organisms rely on their ability to sense and respond appropriately to their environment. The molecular mechanisms that facilitate these essential processes are however subject to a range of random effects and stochastic processes, which jointly affect the reliability of information transmission between receptors and, for example, the physiological downstream response. Information is mathematically defined in terms of the entropy; and the extent of information flowing across an information channel or signalling system is typically measured by the ‘mutual information’, or the reduction in the uncertainty about the output once the input signal is known. Here, we quantify how extrinsic and intrinsic noise affects the transmission of simple signals along simple motifs of molecular interaction networks. Even for very simple systems, the effects of the different sources of variability alone and in combination can give rise to bewildering complexity. In particular, extrinsic variability is apt to generate ‘apparent’ information that can, in extreme cases, mask the actual information that for a single system would flow between the different molecular components making up cellular signalling pathways. We show how this artificial inflation in apparent information arises and how the effects of different types of noise alone and in combination can be understood.</jats:p>
2014
Ann C. Babtie; Paul D.W. Kirk; Michael P.H. Stumpf
Topological sensitivity analysis for systems biology Journal Article
In: Proc. Natl. Acad. Sci. U.S.A., vol. 111, no. 52, pp. 18507–18512, 2014, ISSN: 1091-6490.
Abstract | Links | BibTeX | Tags: None
@article{Babtie2014,
title = {Topological sensitivity analysis for systems biology},
author = {Ann C. Babtie and Paul D.W. Kirk and Michael P.H. Stumpf},
doi = {10.1073/pnas.1414026112},
issn = {1091-6490},
year = {2014},
date = {2014-12-30},
urldate = {2014-12-30},
journal = {Proc. Natl. Acad. Sci. U.S.A.},
volume = {111},
number = {52},
pages = {18507--18512},
publisher = {Proceedings of the National Academy of Sciences},
abstract = {<jats:title>Significance</jats:title>
<jats:p>Mathematical models are widely used to study natural systems. They allow us to test and generate hypotheses, and help us to understand the processes underlying the observed behavior. However, such models are, by necessity, simplified representations of the true systems, so it is critical to understand the impact of assumptions made when using a particular model. Here we provide a method to assess how uncertainty about the structure of a natural system affects the conclusions we can draw from mathematical models of its dynamics. We use biological examples to illustrate the importance of considering uncertainty in both model structure and parameters. We show how solely considering the latter source of uncertainty can result in misleading conclusions and incorrect model inferences.</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Significance</jats:title>
<jats:p>Mathematical models are widely used to study natural systems. They allow us to test and generate hypotheses, and help us to understand the processes underlying the observed behavior. However, such models are, by necessity, simplified representations of the true systems, so it is critical to understand the impact of assumptions made when using a particular model. Here we provide a method to assess how uncertainty about the structure of a natural system affects the conclusions we can draw from mathematical models of its dynamics. We use biological examples to illustrate the importance of considering uncertainty in both model structure and parameters. We show how solely considering the latter source of uncertainty can result in misleading conclusions and incorrect model inferences.</jats:p>
<jats:p>Mathematical models are widely used to study natural systems. They allow us to test and generate hypotheses, and help us to understand the processes underlying the observed behavior. However, such models are, by necessity, simplified representations of the true systems, so it is critical to understand the impact of assumptions made when using a particular model. Here we provide a method to assess how uncertainty about the structure of a natural system affects the conclusions we can draw from mathematical models of its dynamics. We use biological examples to illustrate the importance of considering uncertainty in both model structure and parameters. We show how solely considering the latter source of uncertainty can result in misleading conclusions and incorrect model inferences.</jats:p>
Juliane Liepe; Paul D.W. Kirk; Sarah Filippi; Tina Toni; Chris P. Barnes; Michael P.H. Stumpf
A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation Journal Article
In: Nat Protoc, vol. 9, no. 2, pp. 439–456, 2014, ISSN: 1750-2799.
@article{Liepe2014,
title = {A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation},
author = {Juliane Liepe and Paul D.W. Kirk and Sarah Filippi and Tina Toni and Chris P. Barnes and Michael P.H. Stumpf},
doi = {10.1038/nprot.2014.025},
issn = {1750-2799},
year = {2014},
date = {2014-02-00},
urldate = {2014-02-00},
journal = {Nat Protoc},
volume = {9},
number = {2},
pages = {439--456},
publisher = {Springer Science and Business Media LLC},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
2011
Chris P. Barnes; Daniel Silk; Xia Sheng; Michael P.H. Stumpf
Bayesian design of synthetic biological systems Journal Article
In: Proc. Natl. Acad. Sci. U.S.A., vol. 108, no. 37, pp. 15190–15195, 2011, ISSN: 1091-6490.
Abstract | Links | BibTeX | Tags: None
@article{Barnes2011,
title = {Bayesian design of synthetic biological systems},
author = {Chris P. Barnes and Daniel Silk and Xia Sheng and Michael P.H. Stumpf},
doi = {10.1073/pnas.1017972108},
issn = {1091-6490},
year = {2011},
date = {2011-09-13},
urldate = {2011-09-13},
journal = {Proc. Natl. Acad. Sci. U.S.A.},
volume = {108},
number = {37},
pages = {15190--15195},
publisher = {Proceedings of the National Academy of Sciences},
abstract = {<jats:p>
Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or
<jats:italic>in silico</jats:italic>
prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.
</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>
Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or
<jats:italic>in silico</jats:italic>
prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.
</jats:p>
Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or
<jats:italic>in silico</jats:italic>
prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.
</jats:p>
Daniel Silk; Paul D.W. Kirk; Chris P. Barnes; Tina Toni; Anna Rose; Simon Moon; Margaret J. Dallman; Michael P.H. Stumpf
Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes Journal Article
In: Nat Commun, vol. 2, no. 1, 2011, ISSN: 2041-1723.
@article{Silk2011,
title = {Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes},
author = {Daniel Silk and Paul D.W. Kirk and Chris P. Barnes and Tina Toni and Anna Rose and Simon Moon and Margaret J. Dallman and Michael P.H. Stumpf},
doi = {10.1038/ncomms1496},
issn = {2041-1723},
year = {2011},
date = {2011-09-00},
urldate = {2011-09-00},
journal = {Nat Commun},
volume = {2},
number = {1},
publisher = {Springer Science and Business Media LLC},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
2009
Tina Toni; David Welch; Natalja Strelkowa; Andreas Ipsen; Michael P.H. Stumpf
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems Journal Article
In: J. R. Soc. Interface., vol. 6, no. 31, pp. 187–202, 2009, ISSN: 1742-5662.
Abstract | Links | BibTeX | Tags: None
@article{Toni2008,
title = {Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems},
author = {Tina Toni and David Welch and Natalja Strelkowa and Andreas Ipsen and Michael P.H. Stumpf},
doi = {10.1098/rsif.2008.0172},
issn = {1742-5662},
year = {2009},
date = {2009-02-06},
urldate = {2009-02-06},
journal = {J. R. Soc. Interface.},
volume = {6},
number = {31},
pages = {187--202},
publisher = {The Royal Society},
abstract = {<jats:p>Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.</jats:p>},
keywords = {None},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.</jats:p>
2008
Michael P.H. Stumpf; Thomas Thorne; Eric de Silva; Ronald Stewart; Hyeong Jun An; Michael Lappe; Carsten Wiuf
Estimating the size of the human interactome Journal Article
In: Proc. Natl. Acad. Sci. U.S.A., vol. 105, no. 19, pp. 6959–6964, 2008, ISSN: 1091-6490.
Abstract | Links | BibTeX | Tags: Networks | trees and cell structures
@article{Stumpf2008,
title = {Estimating the size of the human interactome},
author = {Michael P.H. Stumpf and Thomas Thorne and Eric de Silva and Ronald Stewart and Hyeong Jun An and Michael Lappe and Carsten Wiuf},
doi = {10.1073/pnas.0708078105},
issn = {1091-6490},
year = {2008},
date = {2008-05-13},
urldate = {2008-05-13},
journal = {Proc. Natl. Acad. Sci. U.S.A.},
volume = {105},
number = {19},
pages = {6959--6964},
publisher = {Proceedings of the National Academy of Sciences},
abstract = {<jats:p>
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the
<jats:italic>Drosophila melanogaster</jats:italic>
interactome and ≈3 times bigger than in
<jats:italic>Caenorhabditis elegans</jats:italic>
.
</jats:p>},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
<jats:p>
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the
<jats:italic>Drosophila melanogaster</jats:italic>
interactome and ≈3 times bigger than in
<jats:italic>Caenorhabditis elegans</jats:italic>
.
</jats:p>
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the
<jats:italic>Drosophila melanogaster</jats:italic>
interactome and ≈3 times bigger than in
<jats:italic>Caenorhabditis elegans</jats:italic>
.
</jats:p>