In this tutorial the author introduce some of the concepts that frequently appear at the intersec... more In this tutorial the author introduce some of the concepts that frequently appear at the intersection of control theory and systems biology. Mustafa Khammash and Brian Munsky outline some of the key approaches for the modeling and analysis on cellular noise and the resulting fluctuations in the copy numbers of cellular constituents. Eduardo Sontagintroduces some tools for analyzing deterministic biochemical reaction networks. Pablo Iglesias looks at models of spatially varying chemical reactions, and in particular, how gradients and patterns are formed in cells. Finally, Domitilla Del Vecchio considers important control-theoretic problems in synthetic biology - that is, the synthesis of basic circuits inside cells using genetic regulatory components.
The cellular environment is abuzz with noise originating from the inherent random motion of react... more The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.
In this tutorial the author introduce some of the concepts that frequently appear at the intersec... more In this tutorial the author introduce some of the concepts that frequently appear at the intersection of control theory and systems biology. Mustafa Khammash and Brian Munsky outline some of the key approaches for the modeling and analysis on cellular noise and the resulting fluctuations in the copy numbers of cellular constituents. Eduardo Sontagintroduces some tools for analyzing deterministic biochemical reaction networks. Pablo Iglesias looks at models of spatially varying chemical reactions, and in particular, how gradients and patterns are formed in cells. Finally, Domitilla Del Vecchio considers important control-theoretic problems in synthetic biology - that is, the synthesis of basic circuits inside cells using genetic regulatory components.
The cellular environment is abuzz with noise originating from the inherent random motion of react... more The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.
Uploads
Papers by Brian Munsky