729 research outputs found
Diffusion, dimensionality and noise in transcriptional regulation
The precision of biochemical signaling is limited by randomness in the
diffusive arrival of molecules at their targets. For proteins binding to the
specific sites on the DNA and regulating transcription, the ability of the
proteins to diffuse in one dimension by sliding along the length of the DNA, in
addition to their diffusion in bulk solution, would seem to generate a larger
target for DNA binding, consequently reducing the noise in the occupancy of the
regulatory site. Here we show that this effect is largely cancelled by the
enhanced temporal correlations in one dimensional diffusion. With realistic
parameters, sliding along DNA has surprisingly little effect on the physical
limits to the precision of transcriptional regulation.Comment: 8 pages, 2 figure
An operational view of intercellular signaling pathways
Animal cells use a conserved repertoire of intercellular signaling pathways to communicate with one another. These pathways are well-studied from a molecular point of view. However, we often lack an āoperationalā understanding that would allow us to use these pathways to rationally control cellular behaviors. This requires knowing what dynamic input features each pathway perceives and how it processes those inputs to control downstream processes. To address these questions, researchers have begun to reconstitute signaling pathways in living cells, analyzing their dynamic responses to stimuli, and developing new functional representations of their behavior. Here we review important insights obtained through these new approaches, and discuss challenges and opportunities in understanding signaling pathways from an operational point of view
Serially-regulated biological networks fully realize a constrained set of functions
We show that biological networks with serial regulation (each node regulated
by at most one other node) are constrained to {\it direct functionality}, in
which the sign of the effect of an environmental input on a target species
depends only on the direct path from the input to the target, even when there
is a feedback loop allowing for multiple interaction pathways. Using a
stochastic model for a set of small transcriptional regulatory networks that
have been studied experimentally, we further find that all networks can achieve
all functions permitted by this constraint under reasonable settings of
biochemical parameters. This underscores the functional versatility of the
networks.Comment: 9 pages, 3 figure
Rules for biological regulation based on error minimization
The control of gene expression involves complex mechanisms that show large
variation in design. For example, genes can be turned on either by the binding
of an activator (positive control) or the unbinding of a repressor (negative
control). What determines the choice of mode of control for each gene? This
study proposes rules for gene regulation based on the assumption that free
regulatory sites are exposed to nonspecific binding errors, whereas sites bound
to their cognate regulators are protected from errors. Hence, the selected
mechanisms keep the sites bound to their designated regulators for most of the
time, thus minimizing fitness-reducing errors. This offers an explanation of
the empirically demonstrated Savageau demand rule: Genes that are needed often
in the natural environment tend to be regulated by activators, and rarely
needed genes tend to be regulated by repressors; in both cases, sites are bound
for most of the time, and errors are minimized. The fitness advantage of error
minimization appears to be readily selectable. The present approach can also
generate rules for multi-regulator systems. The error-minimization framework
raises several experimentally testable hypotheses. It may also apply to other
biological regulation systems, such as those involving protein-protein
interactions.Comment: biological physics, complex networks, systems biology,
transcriptional regulation
http://www.weizmann.ac.il/complex/tlusty/papers/PNAS2006.pdf
http://www.pnas.org/content/103/11/3999.ful
Nonlinear Protein Degradation and the Function of Genetic Circuits
The functions of most genetic circuits require sufficient degrees of
cooperativity in the circuit components. While mechanisms of cooperativity have
been studied most extensively in the context of transcriptional initiation
control, cooperativity from other processes involved in the operation of the
circuits can also play important roles. In this study, we examine a simple
kinetic source of cooperativity stemming from the nonlinear degradation of
multimeric proteins. Ample experimental evidence suggests that protein subunits
can degrade less rapidly when associated in multimeric complexes, an effect we
refer to as cooperative stability. For dimeric transcription factors, this
effect leads to a concentration-dependence in the degradation rate because
monomers, which are predominant at low concentrations, will be more rapidly
degraded. Thus cooperative stability can effectively widen the accessible range
of protein levels in vivo. Through theoretical analysis of two exemplary
genetic circuits in bacteria, we show that such an increased range is important
for the robust operation of genetic circuits as well as their evolvability. Our
calculations demonstrate that a few-fold difference between the degradation
rate of monomers and dimers can already enhance the function of these circuits
substantially. These results suggest that cooperative stability needs to be
considered explicitly and characterized quantitatively in any systematic
experimental or theoretical study of gene circuits.Comment: 42 pages, 10 figure
Brf1 posttranscriptionally regulates pluripotency and differentiation responses downstream of Erk MAP kinase
AU-rich element mRNA-binding proteins (AUBPs) are key regulators of development, but how they are controlled and what functional roles they play depends on cellular context. Here, we show that Brf1 (zfp36l1), an AUBP from the Zfp36 protein family, operates downstream of FGF/Erk MAP kinase signaling to regulate pluripotency and cell fate decision making in mouse embryonic stem cells (mESCs). FGF/Erk MAP kinase signaling up-regulates Brf1, which disrupts the expression of core pluripotency-associated genes and attenuates mESC self-renewal without inducing differentiation. These regulatory effects are mediated by rapid and direct destabilization of Brf1 targets, such as Nanog mRNA. Enhancing Brf1 expression does not compromise mESC pluripotency but does preferentially regulate mesendoderm commitment during differentiation, accelerating the expression of primitive streak markers. Together, these studies demonstrate that FGF signals use targeted mRNA degradation by Brf1 to enable rapid posttranscriptional control of gene expression in mESCs
Dynamic Ligand Discrimination in the Notch Signaling Pathway
The Notch signaling pathway comprises multiple ligands that are used in distinct biological contexts. In principle, different ligands could activate distinct target programs in signal-receiving cells, but it is unclear how such ligand discrimination could occur. Here, we show that cells use dynamics to discriminate signaling by the ligands Dll1 and Dll4 through the Notch1 receptor. Quantitative single-cell imaging revealed that Dll1 activates Notch1 in discrete, frequency-modulated pulses that specifically upregulate the Notch target gene Hes1. By contrast, Dll4 activates Notch1 in a sustained, amplitude-modulated manner that predominantly upregulates Hey1 and HeyL. Ectopic expression of Dll1 or Dll4 in chick neural crest produced opposite effects on myogenic differentiation, showing that ligand discrimination can occur in vivo. Finally, analysis of chimeric ligands suggests that ligand-receptor clustering underlies dynamic encoding of ligand identity. The ability of the pathway to utilize ligands as distinct communication channels has implications for diverse Notch-dependent processes
Optimizing information flow in small genetic networks. I
In order to survive, reproduce and (in multicellular organisms)
differentiate, cells must control the concentrations of the myriad different
proteins that are encoded in the genome. The precision of this control is
limited by the inevitable randomness of individual molecular events. Here we
explore how cells can maximize their control power in the presence of these
physical limits; formally, we solve the theoretical problem of maximizing the
information transferred from inputs to outputs when the number of available
molecules is held fixed. We start with the simplest version of the problem, in
which a single transcription factor protein controls the readout of one or more
genes by binding to DNA. We further simplify by assuming that this regulatory
network operates in steady state, that the noise is small relative to the
available dynamic range, and that the target genes do not interact. Even in
this simple limit, we find a surprisingly rich set of optimal solutions.
Importantly, for each locally optimal regulatory network, all parameters are
determined once the physical constraints on the number of available molecules
are specified. Although we are solving an over--simplified version of the
problem facing real cells, we see parallels between the structure of these
optimal solutions and the behavior of actual genetic regulatory networks.
Subsequent papers will discuss more complete versions of the problem
Regulatory control and the costs and benefits of biochemical noise
Experiments in recent years have vividly demonstrated that gene expression
can be highly stochastic. How protein concentration fluctuations affect the
growth rate of a population of cells, is, however, a wide open question. We
present a mathematical model that makes it possible to quantify the effect of
protein concentration fluctuations on the growth rate of a population of
genetically identical cells. The model predicts that the population's growth
rate depends on how the growth rate of a single cell varies with protein
concentration, the variance of the protein concentration fluctuations, and the
correlation time of these fluctuations. The model also predicts that when the
average concentration of a protein is close to the value that maximizes the
growth rate, fluctuations in its concentration always reduce the growth rate.
However, when the average protein concentration deviates sufficiently from the
optimal level, fluctuations can enhance the growth rate of the population, even
when the growth rate of a cell depends linearly on the protein concentration.
The model also shows that the ensemble or population average of a quantity,
such as the average protein expression level or its variance, is in general not
equal to its time average as obtained from tracing a single cell and its
descendants. We apply our model to perform a cost-benefit analysis of gene
regulatory control. Our analysis predicts that the optimal expression level of
a gene regulatory protein is determined by the trade-off between the cost of
synthesizing the regulatory protein and the benefit of minimizing the
fluctuations in the expression of its target gene. We discuss possible
experiments that could test our predictions.Comment: Revised manuscript;35 pages, 4 figures, REVTeX4; to appear in PLoS
Computational Biolog
Timing molecular motion and production with a synthetic transcriptional clock
The realization of artificial biochemical reaction networks with unique functionality is one of the main challenges for the development of synthetic biology. Due to the reduced number of components, biochemical circuits constructed in vitro promise to be more amenable to systematic design and quantitative assessment than circuits embedded within living organisms. To make good on that promise, effective methods for composing subsystems into larger systems are needed. Here we used an artificial biochemical oscillator based on in vitro transcription and RNA degradation reactions to drive a variety of āloadā processes such as the operation of a DNA-based nanomechanical device (āDNA tweezersā) or the production of a functional RNA molecule (an aptamer for malachite green). We implemented several mechanisms for coupling the load processes to the oscillator circuit and compared them based on how much the load affected the frequency and amplitude of the core oscillator, and how much of the load was effectively driven. Based on heuristic insights and computational modeling, an āinsulator circuitā was developed, which strongly reduced the detrimental influence of the load on the oscillator circuit. Understanding how to design effective insulation between biochemical subsystems will be critical for the synthesis of larger and more complex systems
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