Eric Batchelor

National Institutes of Health, Bethesda, MD, United States

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Publications (12)160.94 Total impact

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    ABSTRACT: Emerging technologies such as single cell gene expression analysis and single cell genome sequencing provide an unprecedented opportunity to quantitatively probe biological interactions at the single cell level. This new level of insight has begun to reveal a more accurate picture of cellular behavior, and to highlight the importance of understanding cellular variation in a wide range of biological contexts. The aim of this workshop is to bring together researchers working on identifying and modeling cell heterogeneity that arises by a variety of mechanisms, including but not limited to cell-to-cell noise, cell-state switches and cell differentiation, heterogeneity in immune responses, cancer evolution, and heterogeneity in disease progression.
    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 01/2013;
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    Amie D Moody, Eric Batchelor
    Molecular Systems Biology 01/2013; 9:703. · 11.34 Impact Factor
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    ABSTRACT: Cells transmit information through molecular signals that often show complex dynamical patterns. The dynamic behavior of the tumor suppressor p53 varies depending on the stimulus; in response to double-strand DNA breaks, it shows a series of repeated pulses. Using a computational model, we identified a sequence of precisely timed drug additions that alter p53 pulses to instead produce a sustained p53 response. This leads to the expression of a different set of downstream genes and also alters cell fate: Cells that experience p53 pulses recover from DNA damage, whereas cells exposed to sustained p53 signaling frequently undergo senescence. Our results show that protein dynamics can be an important part of a signal, directly influencing cellular fate decisions.
    Science 06/2012; 336(6087):1440-4. · 31.20 Impact Factor
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    ABSTRACT: Many biological networks respond to various inputs through a common signaling molecule that triggers distinct cellular outcomes. One potential mechanism for achieving specific input-output relationships is to trigger distinct dynamical patterns in response to different stimuli. Here we focused on the dynamics of p53, a tumor suppressor activated in response to cellular stress. We quantified the dynamics of p53 in individual cells in response to UV and observed a single pulse that increases in amplitude and duration in proportion to the UV dose. This graded response contrasts with the previously described series of fixed pulses in response to γ-radiation. We further found that while γ-triggered p53 pulses are excitable, the p53 response to UV is not excitable and depends on continuous signaling from the input-sensing kinases. Using mathematical modeling and experiments, we identified feedback loops that contribute to specific features of the stimulus-dependent dynamics of p53, including excitability and input-duration dependency. Our study shows that different stresses elicit different temporal profiles of p53, suggesting that modulation of p53 dynamics might be used to achieve specificity in this network.
    Molecular Systems Biology 05/2011; 7:488. · 11.34 Impact Factor
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    Eric Batchelor, Galit Lahav
    Molecular Systems Biology 01/2011; 7:520. · 11.34 Impact Factor
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    ABSTRACT: Recent studies have shown that many cell-signaling networks contain interactions and feedback loops that give rise to complex dynamics. Synthetic biology has allowed researchers to construct and analyze well-defined signaling circuits exhibiting behavior that can be predicted and quantitatively understood. Combining these approaches--wiring natural network components together with engineered interactions--has the potential to precisely modulate the dynamics of endogenous signaling processes and control the cell decisions they influence. Here, we focus on the p53 signaling pathway as a template for constructing a tunable oscillator comprised of both natural and synthetic components in mammalian cells. We find that a reduced p53 circuit implementing a single feedback loop preserves some features of the full network's dynamics, exhibiting pulses of p53 with tightly controlled timing. However, in contrast to the full natural p53 network, these pulses are damped in individual cells, with amplitude that depends on the input strength. Guided by a computational model of the reduced circuit, we constructed and analyzed circuit variants supplemented with synthetic positive and negative feedback loops and subjected to chemical perturbation. Our work demonstrates that three important features of oscillator dynamics--amplitude, period, and the rate of damping--can be controlled by manipulating stimulus level, interaction strength, and feedback topology. The approaches taken here may be useful for the rational design of synthetic networks with defined dynamics, and for identifying perturbations that control dynamics in natural biological circuits for research or therapeutic purposes.
    Proceedings of the National Academy of Sciences 09/2010; 107(39):17047-52. · 9.81 Impact Factor
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    ABSTRACT: A key circuit in the response of cells to damage is the p53-mdm2 feedback loop. This circuit shows sustained, noisy oscillations in individual human cells following DNA breaks. Here, we apply an engineering approach known as systems identification to quantify the in vivo interactions in the circuit on the basis of accurate measurements of its power spectrum. We obtained oscillation time courses of p53 and Mdm2 protein levels from several hundred cells and analyzed their Fourier spectra. We find characteristic spectra with distinct low-frequency components that are well-described by a third-order linear model with white noise. The model identifies the sign and strength of the known interactions, including a negative feedback loop between p53 and its upstream regulator. It also implies that noise can trigger and maintain the oscillations. The model also captures the power spectra of p53 dynamics without DNA damage. Parameters such as noise amplitudes and protein lifetimes are estimated. This approach employs natural biological noise as a diagnostic that stimulates the system at many frequencies at once. It seems to be a useful way to find the in vivo design of circuits and may be applied to other systems by monitoring their power spectrum in individual cells.
    Proceedings of the National Academy of Sciences 07/2010; 107(30):13550-5. · 9.81 Impact Factor
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    ABSTRACT: The tumor suppressor p53 is activated by stress and leads to cellular outcomes such as apoptosis and cell-cycle arrest. Its activation must be highly sensitive to ensure that cells react appropriately to damage. However, proliferating cells often encounter transient damage during normal growth, where cell-cycle arrest or apoptosis may be unfavorable. How does the p53 pathway achieve the right balance between high sensitivity and tolerance to intrinsic damage? Using quantitative time-lapse microscopy of individual human cells, we found that proliferating cells show spontaneous pulses of p53, which are triggered by an excitable mechanism during cell-cycle phases associated with intrinsic DNA damage. However, in the absence of sustained damage, posttranslational modifications keep p53 inactive, preventing it from inducing p21 expression and cell-cycle arrest. Our approach of quantifying basal dynamics in individual cells can now be used to study how other pathways in human cells achieve sensitivity in noisy environments.
    Cell 07/2010; 142(1):89-100. · 31.96 Impact Factor
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    Eric Batchelor, Alexander Loewer, Galit Lahav
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    ABSTRACT: Cells living in a complex environment must constantly detect, process and appropriately respond to changing signals. Therefore, all cellular information processing is dynamic in nature. As a consequence, understanding the process of signal transduction often requires detailed quantitative analysis of dynamic behaviours. Here, we focus on the oscillatory dynamics of the tumour suppressor protein p53 as a model for studying protein dynamics in single cells to better understand its regulation and function.
    Nature Reviews Cancer 05/2009; 9(5):371-7. · 29.54 Impact Factor
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    ABSTRACT: DNA damage initiates a series of p53 pulses. Although much is known about the interactions surrounding p53, little is known about which interactions contribute to p53's dynamical behavior. The simplest explanation is that these pulses are oscillations intrinsic to the p53/Mdm2 negative feedback loop. Here we present evidence that this simple mechanism is insufficient to explain p53 pulses; we show that p53 pulses are externally driven by pulses in the upstream signaling kinases, ATM and Chk2, and that the negative feedback between p53 and ATM, via Wip1, is essential for maintaining the uniform shape of p53 pulses. We propose that p53 pulses result from repeated initiation by ATM, which is reactivated by persistent DNA damage. Our study emphasizes the importance of collecting quantitative dynamic information at high temporal resolution for understanding the regulation of signaling pathways and opens new ways to manipulate p53 pulses to ask questions about their function in response to DNA damage.
    Molecular cell 06/2008; 30(3):277-89. · 14.61 Impact Factor
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    ABSTRACT: 1 Abstract p53 is a transcription factor involved in cellular stress response, and plays a role in directing the programs of cell cycle arrest, DNA damage repair, and apoptosis. p53 protein levels are seen to undergo a series of pulses after ionizing radiation induced DNA damage 1 . The signaling network associated with this response is complex, consisting of a number of feedback loops that both positively and negatively regulate p53 levels 2 . Quantitative models are necessary to understand complex dynamics such as oscillations in such systems. Many such models have been proposed since the publication of these experimental findings; however, fundamental questions about the system's operation remain unanswered. The models published disagree on the combinations of negative and positive feedback loops responsible for oscillation; it is yet unclear which network actually drives oscillation. Additionally, these models have been constructed at varying levels of detail, ranging from 5-30 parameters, making it challenging to compare models' behavior. To address the former question, we aim to obtain constraints on the combination of negative and positive feedback loops driving oscillation. By introducing p53 driven by an inducible promoter and increasing p53 production from this promoter, w e a r e a b l e t o e l i c i t a p 5 3 r e s p o n s e w i t h o u t c a u s i n g i t s a c t i va t i o n 3 . Th i s s t i m u l u s e xp e r i m e n t a l l y i s o l a t e s t h e p 5 3 n e t wo r k ' s c o r e n e ga t i v e f e e d b a c k l o o p . In this loop, p53 activates the ubiquitin ligase Mdm2, which subsequently signals p53 for degradation. This allows us to investigate a system in which the observed dynamics are linked to a known network topology. Here, we report that increasing p53's transcription rate leads to damped oscillation in p53 and Mdm2 concentration in individual cells, and the behavior of a typical cell can be fit to models of this negative feedback loop. To address the latter question, we are investigating whether the simplest, abstract models of biochemical feedback networks correspond in function to the detailed map of chemical reactions that more accurately represent them. We constructed multiple models of the p53-Mdm2 negative feedback loop ranging in detail from a minimal 3-equation abstract model to a detailed, 25-equation mass-action model. We have developed a method to analyze the strength and transit time around feedback loops consisting of chains of chemical reactions, and have applied this method to both models to compare the mechanism of their operation. This method reduces to the lifetime of species in a reaction cascade under hypothesis that allow this lifetime to be computed 5 .
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Publication Stats

439 Citations
160.94 Total Impact Points

Institutions

  • 2011–2013
    • National Institutes of Health
      • • Center for Cancer Research
      • • Laboratory of Pathology
      Bethesda, MD, United States
  • 2008–2012
    • Harvard Medical School
      • Department of Systems Biology
      Boston, MA, United States