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Efficient Computational Methods and Software Tools for the Inference of Stochastic Models of Biochemical Reaction Networks

English title Efficient Computational Methods and Software Tools for the Inference of Stochastic Models of Biochemical Reaction Networks
Applicant Khammash Mustafa
Number 157129
Funding scheme Project funding (Div. I-III)
Research institution Computational Systems Biology Department of Biosystems, D-BSSE ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Other disciplines of Engineering Sciences
Start/End 01.09.2015 - 31.08.2018
Approved amount 320'000.00
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All Disciplines (2)

Discipline
Other disciplines of Engineering Sciences
Mathematics

Keywords (7)

Chemical Master Equation; Parameter inference; System identification; Bayesian inference; Stochastic gene expression; Gene regulatory networks; Discrete stochastic models

Lay Summary (German)

Lead
Die Modellierung von interzellulären Prozessen ist ein zentrales Thema der Systembiologie.In den letzten Jahren ist die Verfügbarkeit von Einzelzell-Daten markant gestiegen,was ein grosses Interesse geweckt hat diese Informationen möglichst effizient zur Konstruktionmechanistischer Modelle von Zellprozessen zu verwenden. Insbesondere stochastischeModelle gewinnen mehr und mehr Beachtung, da die Zufälligkeit bestimmterzellulärer Prozesse massgeblich deren Verhalten beeinflusst. Aufgrund ihrer Komplexitätsind stochastische Modelle jedoch nur schwer zu handhaben. Ein ganz konkretes - undessenzielles - Problem dabei ist die Bestimmung von Modellparametern, denn erst durchdie Parametrisierung wird das Modellverhalten bestimmt. In diesem Projekt entwickelnwir Methoden, die abhängig von den experimentell beobachteten Abläufen in einzelnenZellen, verlässlich die dem Modell zugrunde liegenden Parameter bestimmen.
Lay summary
Inhalt und Ziel des Forschungsprojekts Unser Ziel ist es Methoden zur Inferenz von Modellparametern für stochastische Modelle zu entwickeln, welche einerseits die Informationen, die in experimentellen Beobachtungen von einzelnen Zellen verfügbar sind vollständig ausnutzen und gleichzeitig skalierbar sind, sodass sie selbst für grosse Modelle anwendbar sind. Wir werden auch eine performative und parallele Implementierung der entwickelten Methoden liefern und diese an diversen realistischen Beispielmodellen evaluieren. Wissenschaftlicher und gesellschaftlicher Kontext des Forschungsprojekts Die effiziente Inferenz von Parametern für stochastische Modelle ermöglicht Wissenschaftler im Bereich von System- und Molekularbiologie genauso selbstverständlich mit stochastischen Modellen zu arbeiten wie bisher mit deterministischen. Damit tragen wir bei zum Verstehen biologischer Prozesse und insbesondere zum Verständnis von Variabilität in Zellpopulationen.
Direct link to Lay Summary Last update: 17.07.2015

Responsible applicant and co-applicants

Employees

Publications

Publication
Parameter inference for stochastic single-cell dynamics from lineage tree data
Kuzmanovska Irena, Milias-Argeitis Andreas, Mikelson Jan, Zechner Christoph, Khammash Mustafa (2017), Parameter inference for stochastic single-cell dynamics from lineage tree data, in BMC Systems Biology, 11(1), 52-52.
A finite state projection algorithm for the stationary solution of the chemical master equation
Gupta Ankit, Mikelson Jan, Khammash Mustafa (2017), A finite state projection algorithm for the stationary solution of the chemical master equation, in The Journal of Chemical Physics, 147(15), 154101-154101.
Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate
Gupta Ankit, Milias-Argeitis Andreas, Khammash Mustafa (2017), Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate, in Journal of The Royal Society Interface, 14(132), 20170311-20170311.
Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
Briat Corentin, Gupta Ankit, Khammash Mustafa (2016), Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks, in Cell Systems, 2(1), 15-26.
Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks
Milias-Argeitis Andreas, Engblom Stefan, Bauer Pavol, Khammash Mustafa (2015), Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks, in Journal of The Royal Society Interface, 12(113), 20150831-20150831.

Collaboration

Group / person Country
Types of collaboration
Olivier Pertz Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
International Conference on Systems Biology Talk given at a conference Rationally Designed Biomolecular Control Systems
 28.10.2018 Lyon, France Khammash Mustafa;
Computational Methods for Systems Biology Talk given at a conference Biomolecular Control Systems 12.09.2018 Brno, Czech Republic Khammash Mustafa;
CompSysBio - Advanced lecture course on computational systems biology Poster Parameter Estimation of Stochastic Models from Lineage Tree Data 19.03.2017 Aussois, France Mikelson Jan;


Self-organised

Title Date Place
MBI Control of Cellular and Molecular Systems 02.10.2017 Columbus, Ohio, United States of America

Communication with the public

Communication Title Media Place Year
Media relations: radio, television Fiksut solut näkevät valoa, viestivät ja parantavat sairauksia? Tiedeykkönen Yle Radio 1 tiistaisin ja perjantaisin International 2016

Use-inspired outputs

Software

Name Year
INSIGHTv3 2018


Associated projects

Number Title Start Funding scheme
182653 An Advanced Stochastic Filtering Framework for the Analysis of Multiscale Biochemical Reaction Networks 01.11.2019 Project funding (Div. I-III)

Abstract

Heterogeneity of many cell populations in spite of genetic homogeneity is widely accepted. The advent of new technologies is making single cell data more available, shining a light on, and quantifying cell-to-cell variability. At the same time, models of stochastic chemical kinetics provide a rigorous framework for capturing cell-to-cell variability in a natural way. By describing stochastic dynamics of the underlying networks, such models provide a deeper understanding of biological function, including the nature, extent, and role of cellular noise. However, very few methods exist for using single-cell-data to infer stochastic model parameters. This is the main problem addressed in the proposed project. In particular, we aim to a) develop fast, scalable, computational methods for the parameter inference of stochastic models based on time-course density measurements, as for example can be obtained by flow cytometry and FiSH mRNA technologies; b) develop efficient computational methods for parameter inference of stochastic models based on measurements of a collection of single-cell trajectories as may be obtained with fluorescence microscopy; c) develop efficient and robust software tools to implement above computational inference methods; and d) apply the created methods and software to prototype systems consisting of a constructed synthetic microRNA gene circuit and to a gene regulatory network. The research is expected to advance the state-of-the-art in inference of stochastic models, and to provide new valuable tools for systems biologists and synthetic biologists.
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