2010 - Fellow of the International Federation of Automatic Control (IFAC)
2007 - IEEE Fellow For contributions to robust control and its applications
Mustafa Khammash focuses on Control theory, Robustness, Mathematical optimization, Gene regulatory network and Stochastic process. His Robust control, Linear system and Control theory study in the realm of Control theory connects with subjects such as Negative feedback and Interconnection. His research in Robustness intersects with topics in Discrete system, Matrix algebra and Eigenvalues and eigenvectors.
His work on Linear programming, Optimization problem and Optimal control as part of general Mathematical optimization research is frequently linked to Large class, bridging the gap between disciplines. His Gene regulatory network study is focused on Genetics in general. The Stochastic process study combines topics in areas such as Projection, Dykstra's projection algorithm, Reduction, Differential equation and Monte Carlo method.
His scientific interests lie mostly in Control theory, Mathematical optimization, Applied mathematics, Robustness and Robust control. His Control theory research is multidisciplinary, relying on both Control engineering and Norm. His research integrates issues of Algorithm, Upper and lower bounds and Linear system in his study of Mathematical optimization.
His Applied mathematics study deals with Ergodicity intersecting with Algebraic number. In his study, which falls under the umbrella issue of Robustness, Markov process, Stochastic simulation and Markov chain is strongly linked to Monte Carlo method. His Stochastic process research is multidisciplinary, incorporating elements of Biological system, Stochastic modelling and Reduction.
His scientific interests lie mostly in Applied mathematics, Computational biology, Control theory, Ergodicity and Biological system. His Applied mathematics study combines topics in areas such as Projection method, Stochastic modelling, Markov process and Poisson's equation. His work carried out in the field of Computational biology brings together such families of science as Nucleic acid structure, Gene expression, Gene and Optogenetics.
His Control theory course of study focuses on Control and State. The study incorporates disciplines such as In silico, Invariant, Chemical species and Robustness in addition to Biological system. As part of one scientific family, Mustafa Khammash deals mainly with the area of Transcription, narrowing it down to issues related to the Single-cell analysis, and often Regulation of gene expression.
Computational biology, Control theory, Robustness, Control theory and Control system are his primary areas of study. Mustafa Khammash has researched Computational biology in several fields, including Nucleic acid structure and Gene expression, Gene. In his study, Basis, Realization and Metabolic engineering is strongly linked to Adaptation, which falls under the umbrella field of Control theory.
His Robustness research is multidisciplinary, incorporating perspectives in Structure and Chemical species. Mustafa Khammash integrates several fields in his works, including Control theory and Negative feedback. His Control system research focuses on Synthetic biology and how it connects with Biochemical engineering and Simple.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The finite state projection algorithm for the solution of the chemical master equation.
Brian Munsky;Mustafa Khammash.
Journal of Chemical Physics (2006)
Parameter Estimation and Model Selection in Computational Biology
Gabriele Lillacci;Mustafa Khammash.
PLOS Computational Biology (2010)
Surviving heat shock: Control strategies for robustness and performance
H. El-Samad;H. Kurata;J. C. Doyle;C. A. Gross.
Proceedings of the National Academy of Sciences of the United States of America (2005)
In silico feedback for in vivo regulation of a gene expression circuit
Andreas Milias-Argeitis;Sean Summers;Jacob Stewart-Ornstein;Ignacio Zuleta.
Nature Biotechnology (2011)
Performance robustness of discrete-time systems with structured uncertainty
M. Khammash;J.B. Pearson.
IEEE Transactions on Automatic Control (1991)
Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
Corentin Briat;Ankit Gupta;Mustafa Khammash.
Cell systems (2016)
Systematic Identification of Signal-Activated Stochastic Gene Regulation
Gregor Neuert;Gregor Neuert;Brian Munsky;Rui Zhen Tan;Rui Zhen Tan;Rui Zhen Tan;Leonid Teytelman.
Listening to the noise: random fluctuations reveal gene network parameters
Brian Munsky;Brooke Trinh;Mustafa Khammash.
Molecular Systems Biology (2009)
Calcium homeostasis and parturient hypocalcemia: an integral feedback perspective.
H. El-Samad;J.P. Goff;M. Khammash.
Journal of Theoretical Biology (2002)
Structured optimal and robust control with multiple criteria: a convex solution
Xin Qi;M.V. Salapaka;P.G. Voulgaris;M. Khammash.
IEEE Transactions on Automatic Control (2004)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: