25 research outputs found
Intrinsic limits to gene regulation by global crosstalk
Gene regulation relies on the specificity of transcription factor (TF) - DNA
interactions. In equilibrium, limited specificity may lead to crosstalk: a
regulatory state in which a gene is either incorrectly activated due to
noncognate TF-DNA interactions or remains erroneously inactive. We present a
tractable biophysical model of global crosstalk, where many genes are
simultaneously regulated by many TFs. We show that in the simplest regulatory
scenario, a lower bound on crosstalk severity can be analytically derived
solely from the number of (co)regulated genes and a suitable parameter that
describes binding site similarity. Estimates show that crosstalk could present
a significant challenge for organisms with low-specificity TFs, such as
metazoans, unless they use appropriate regulation schemes. Strong cooperativity
substantially decreases crosstalk, while joint regulation by activators and
repressors, surprisingly, does not; moreover, certain microscopic details about
promoter architecture emerge as globally important determinants of crosstalk
strength. Our results suggest that crosstalk imposes a new type of global
constraint on the functioning and evolution of regulatory networks, which is
qualitatively distinct from the known constraints acting at the level of
individual gene regulatory elements
Statistical mechanics for metabolic networks during steady-state growth
Which properties of metabolic networks can be derived solely from
stoichiometric information about the network's constituent reactions?
Predictive results have been obtained by Flux Balance Analysis (FBA), by
postulating that cells set metabolic fluxes within the allowed stoichiometry so
as to maximize their growth. Here, we generalize this framework to single cell
level using maximum entropy models from statistical physics. We define and
compute, for the core metabolism of Escherichia coli, a joint distribution over
all fluxes that yields the experimentally observed growth rate. This solution,
containing FBA as a limiting case, provides a better match to the measured
fluxes in the wild type and several mutants. We find that E. coli metabolism is
close to, but not at, the optimality assumed by FBA. Moreover, our model makes
a wide range of predictions: (i) on flux variability, its regulation, and flux
correlations across individual cells; (ii) on the relative importance of
stoichiometric constraints vs. growth rate optimization; (iii) on quantitative
scaling relations for singe-cell growth rate distributions. We validate these
scaling predictions using data from individual bacterial cells grown in a
microfluidic device at different sub-inhibitory antibiotic concentrations.
Under mild dynamical assumptions, fluctuation-response relations further
predict the autocorrelation timescale in growth data and growth rate adaptation
times following an environmental perturbation.Comment: 12 pages, 4 figure
Modeling network dynamics: the lac operon, a case study
We use the lac operon in Escherichia coli as a prototype system to illustrate the current state, applicability, and limitations of modeling the dynamics of cellular networks. We integrate three different levels of description (molecular, cellular, and that of cell population) into a single model, which seems to capture many experimental aspects of the system
Uncovering cis Regulatory Codes Using Synthetic Promoter Shuffling
Revealing the spectrum of combinatorial regulation of transcription at individual promoters is essential for understanding the complex structure of biological networks. However, the computations represented by the integration of various molecular signals at complex promoters are difficult to decipher in the absence of simple cis regulatory codes. Here we synthetically shuffle the regulatory architecture — operator sequences binding activators and repressors — of a canonical bacterial promoter. The resulting library of complex promoters allows for rapid exploration of promoter encoded logic regulation. Among all possible logic functions, NOR and ANDN promoter encoded logics predominate. A simple transcriptional cis regulatory code determines both logics, establishing a straightforward map between promoter structure and logic phenotype. The regulatory code is determined solely by the type of transcriptional regulation combinations: two repressors generate a NOR: NOT (a OR b) whereas a repressor and an activator generate an ANDN: a AND NOT b. Three-input versions of both logics, having an additional repressor as an input, are also present in the library. The resulting complex promoters cover a wide dynamic range of transcriptional strengths. Synthetic promoter shuffling represents a fast and efficient method for exploring the spectrum of complex regulatory functions that can be encoded by complex promoters. From an engineering point of view, synthetic promoter shuffling enables the experimental testing of the functional properties of complex promoters that cannot necessarily be inferred ab initio from the known properties of the individual genetic components. Synthetic promoter shuffling may provide a useful experimental tool for studying naturally occurring promoter shuffling
Minimally invasive determination of mRNA concentration in single living bacteria
Fluorescence correlation spectroscopy (FCS) has permitted the characterization of high concentrations of noncoding RNAs in a single living bacterium. Here, we extend the use of FCS to low concentrations of coding RNAs in single living cells. We genetically fuse a red fluorescent protein (RFP) gene and two binding sites for an RNA-binding protein, whose translated product is the RFP protein alone. Using this construct, we determine in single cells both the absolute [mRNA] concentration and the associated [RFP] expressed from an inducible plasmid. We find that the FCS method allows us to reliably monitor in real-time [mRNA] down to ∼40 nM (i.e. approximately two transcripts per volume of detection). To validate these measurements, we show that [mRNA] is proportional to the associated expression of the RFP protein. This FCS-based technique establishes a framework for minimally invasive measurements of mRNA concentration in individual living bacteria
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Uncovering <i>cis</i> Regulatory Codes Using Synthetic Promoter Shuffling
Revealing the spectrum of combinatorial regulation of transcription at individual promoters is essential for understanding the complex structure of biological networks. However, the computations represented by the integration of various molecular signals at complex promoters are difficult to decipher in the absence of simple cis regulatory codes. Here we synthetically shuffle the regulatory architecture — operator sequences binding activators and repressors — of a canonical bacterial promoter. The resulting library of complex promoters allows for rapid exploration of promoter encoded logic regulation. Among all possible logic functions, NOR and ANDN promoter encoded logics predominate. A simple transcriptional cis regulatory code determines both logics, establishing a straightforward map between promoter structure and logic phenotype. The regulatory code is determined solely by the type of transcriptional regulation combinations: two repressors generate a NOR: NOT (a OR b) whereas a repressor and an activator generate an ANDN: a AND NOT b. Three-input versions of both logics, having an additional repressor as an input, are also present in the library. The resulting complex promoters cover a wide dynamic range of transcriptional strengths. Synthetic promoter shuffling represents a fast and efficient method for exploring the spectrum of complex regulatory functions that can be encoded by complex promoters. From an engineering point of view, synthetic promoter shuffling enables the experimental testing of the functional properties of complex promoters that cannot necessarily be inferred ab initio from the known properties of the individual genetic components. Synthetic promoter shuffling may provide a useful experimental tool for studying naturally occurring promoter shuffling.</p
Supplementary materials and methods; Full data set from Effects of mutations in phage restriction sites during escape from restriction–modification
information on culture conditions, phage mutagenesis, verification and lysate preparation; Raw dat
Promoter fragments used to construct combinatorial library.
<p>The first six rows correspond to the original promoters on which the library is based, the two activators: P<sub>A+</sub> and P<sub>λ+</sub>, and the four repressors: P<sub>λ-</sub>, P<sub>L1</sub>, P<sub>L2</sub>, and P<sub>T</sub>. Promoter fragments ‘Upstream’, ‘Core’, and ‘Downstream’ of the −35 (blue) and −10 (red) regions are displayed. Each fragment has unique three-nucleotide overhangs, allowing properly-ordered assembly upon ligation to each other and the plasmid backbone. Binding regions of specific regulators are underscored and labeled. “Additional Binding Sites” refers to additional promoter fragments that were created to expand the library. The lone nucleotide in green upstream of the −35 site in P<sub>L2</sub> indicates the accidental insertion of a ‘T’ when we designed this promoter fragment; it has negligible effect on the strength of repression by LacI.</p