Mustafa Hani Khammash

Last Name

Khammash

First Name

Mustafa Hani

ORCID

0000-0002-4855-9220

Organisational unit

03921 - Khammash, Mustafa / Khammash, Mustafa

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Publications1 - 10 of 146

Likelihood-free nested sampling for parameter inference of biochemical reaction networks
Mikelson, Jan; Khammash, Mustafa Hani (2020)
PLoS Computational Biology
The development of mechanistic models of biological systems is a central part of Systems Biology. One major challenge in developing these models is the accurate inference of model parameters. In recent years, nested sampling methods have gained increased attention in the Systems Biology community due to the fact that they are parallelizable and provide error estimates with no additional computations. One drawback that severely limits the usability of these methods, however, is that they require the likelihood function to be available, and thus cannot be applied to systems with intractable likelihoods, such as stochastic models. Here we present a likelihood-free nested sampling method for parameter inference which overcomes these drawbacks. This method gives an unbiased estimator of the Bayesian evidence as well as samples from the posterior. We derive a lower bound on the estimators variance which we use to formulate a novel termination criterion for nested sampling. The presented method enables not only the reliable inference of the posterior of parameters for stochastic systems of a size and complexity that is challenging for traditional methods, but it also provides an estimate of the obtained variance. We illustrate our approach by applying it to several realistically sized models with simulated data as well as recently published biological data. We also compare our developed method with the two most popular other likelioodfree approaches: pMCMC and ABC-SMC. The C++ code of the proposed methods, together with test data, is available at the github web page https://github.com/Mijan/LFNS_paper. © 2020 Mikelson, Khammash. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
Briat, Corentin; Gupta, Ankit; Khammash, Mustafa Hani (2018)
Journal of the Royal Society. Interface
Digital quantification of proteins and mRNA in single mammalian cells
Albayrak, Cem; Jordi, Christian A.; Zechner, Christoph; et al. (2016)
Molecular Cell
Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximations
Fang, Zhou; Gupta, Ankit; Khammash, Mustafa Hani (2022)
Journal of Computational Physics
In the past few decades, the development of fluorescent technologies and microscopic techniques has greatly improved scientists' ability to observe real-time single-cell activities. In this paper, we consider the filtering problem associate with these advanced technologies, i.e., how to estimate latent dynamic states of an intracellular multiscale stochastic reaction network from time-course measurements of fluorescent reporters. A good solution to this problem can further improve scientists' ability to extract information about intracellular systems from time-course experiments. A straightforward approach to this filtering problem is to use a particle filter where particles are generated by simulation of the full model and weighted according to observations. However, the exact simulation of the full dynamic model usually takes an impractical amount of computational time and prevents this type of particle filters from being used for real-time applications, such as transcription regulation networks. Inspired by the recent development of hybrid approximations to multiscale chemical reaction networks, we approach the filtering problem in an alternative way. We first prove that accurate solutions to the filtering problem can be constructed by solving the filtering problem for a reduced model that represents the dynamics as a hybrid process. The model reduction is based on exploiting the time-scale separations in the original network and, therefore, can greatly reduce the computational effort required to simulate the dynamics. As a result, we are able to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model. We illustrate the accuracy and the computational efficiency of our approach using several numerical examples.
SynthEvo: A Gradient-Guided Evolutionary Approach for Synthetic Circuit Design
Rossi, Nicolò; Gupta, Ankit; Khammash, Mustafa Hani (2024)
2024 IEEE 63rd Conference on Decision and Control (CDC)
It is well-established that complex intracellular mechanisms often comprise simpler “motifs” specializing in specific cellular functions. Consequently, it is crucial to systematically explore various motif topologies for a chosen function. Achieving this topological characterization can be facilitated by evolutionary algorithms, but the main challenge is to adequately explore the vast topological search space to identify optimal configurations for specific functions. In this paper, we aim to address this challenge by initially employing a “fully-connected” topology and then evolving connection strengths (i.e. reaction rates), through gradient-descent to optimize for both connection sparsity and effectiveness in fulfilling the desired function. We call this method SynthEvo, and we illustrate its effectiveness in discovering circuit topologies for two important synthetic biology functions: near-perfect adaptation and ultrasensitivity.
The signal within the noise
Lillacci, Gabriele; Khammash, Mustafa Hani (2013)
Bioinformatics
In-Silico Proportional-Integral Moment Control of Stochastic Gene Expression
Briat, Corentin; Khammash, Mustafa Hani (2021)
IEEE Transactions on Automatic Control
In-silico control of complex stochastic biological systems is a promising area of Cybergenetics, which provides enabling theoretical and practical tools for the real-time control of living cells with digital computers. We demonstrate here that simple PI control laws can be used to control both the mean and the variance of the expression product of a simple gene expression network. Extensions to more general unimolecular networks, networks with input delay, and to a gene expression network involving protein dimerization are also provided. Notably, no moment closure method is used for dealing with the network with dimerization. The obtained results are illustrated by simulations.
Flow Cytometry Based State Aggregation of a Stochastic Model of Protein Expression
Mirtabatabaei, Anahita; Bullo, Francesco; Khammash, Mustafa Hani (2011)
2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC)
A universal biomolecular integral feedback controller for robust perfect adaptation
Aoki, Stephanie; Lillacci, Gabriele; Gupta, Ankit; et al. (2019)
Nature
Homeostasis is a recurring theme in biology that ensures that regulated variables robustly—and in some systems, completely—adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation. Despite its benefits, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. Here we prove mathematically that there is a single fundamental biomolecular controller topology that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. Our results provide conceptual and practical tools in the area of cybergenetics, for engineering synthetic controllers that steer the dynamics of living systems.
Computer control of gene expression
Briat, Corentin; Khammash, Mustafa Hani (2012)

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Mustafa Hani Khammash

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