San Diego Center for Systems Biology

San Diego, CA, United States

San Diego Center for Systems Biology

San Diego, CA, United States

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Selimkhanov J.,University of California at San Diego | Taylor B.,University of California at San Diego | Yao J.,University of California at San Diego | Pilko A.,University of California at San Diego | And 7 more authors.
Science | Year: 2014

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyzed the ability of temporal signal modulation-that is, dynamics-to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca2+), and nuclear factor kappa-B (NF-kB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to nondynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise levels confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise-induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of bichemical signaling networks.


News Article | November 16, 2016
Site: www.sciencedaily.com

A new bioinformatic framework developed by researchers at University of California San Diego School of Medicine has identified key proteins significantly altered at the gene-expression level in biopsied tissue from patients with diabetic kidney disease, a result that may reveal new therapeutic targets. In a recently published paper in JCI Insights, researchers, led by Kumar Sharma, MD, professor of medicine at UC San Diego School of Medicine, revealed that the protein MDM2 was consistently down-regulated and played a key role in diabetic kidney disease progression. The researchers used the new "MetBridge Generator" bioinformatics framework to identify the relevant enzymes and bridge proteins that link human metabolomics data to the pathophysiology of diabetic kidney disease at a molecular level. "MetBridge Generator allows for efficient, focused analysis of urine metabolomics data from patients with diabetic kidney disease, providing researchers an opportunity to develop new hypotheses based on the possible cellular or physiological role of key proteins," said Sharma, senior author and director of the Institute for Metabolomic Medicine and the Center for Renal Translational Medicine at UC San Diego School of Medicine. "The framework may also be used in the interpretation of other metabolomic signatures from a variety of diseases. For example, MDM2 is also involved in regulating tumor protein p53, which is a target for cancer treatments." In a previous study, the authors identified 13 metabolites that were found to be altered in patients with diabetic kidney disease. Combining this information and publicly available data on metabolic pathways, the researchers tested an hypothesis that some proteins act as bridges creating less well-defined pathways. The framework then created a map of metabolic and protein-protein interaction (PPI) networks. This allowed the team to look deeper into relevant bridges with the greatest number of interactions with enzymes that regulate the 13-metabolite signature of diabetic kidney disease. The authors already identified protein-RNA interactions as possible sources for additional key pathways underlying disease progression that could be added to the MetBridge Generator network. This growth will continue to add to possible therapeutic targets for disease treatment. Study co-authors include: Rintaro Saito, Young-Hyun You, Manjula Darshi, Benjamin Van Espen, Satoshi Miyamoto, Jessica Pham, Minya Pu, Loki Natarajan, Keiichiro Ono, Trey Ideker, UC San Diego; Anaïs Rocanin-Arjo, Simone Romoli, Dana Thomasova, Shrikant R. Mulay, Hans Joachim Anders, Klinikum der Universität München, LMU Munich; Wenjun Ju, Matthias Kretzler, University of Michigan; Robert Nelson, National Institute of Diabetes and Digestive and Kidney Diseases; Vivette D'Agati, Columbia University; Ergin Beyret, and Juan Carlos Izpisua Belmonte, Salk Institute for Biological Studies. This research was funded, in part, by the National Institutes of Health (DP3 DK094352), National Resource for Network Biology (P41 GM103504), San Diego Center for Systems Biology (P50 GM085764), University of Michigan O'Brien Kidney Translational Core Center (P30DK081943), Juvenile Diabetes Research Foundation and a VA Merit Award (5101BX000277), Deutsche Forschungsgemeinschaft (TH 1836/1-2, AN372/11-2), Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. Disclosure: Sharma is the co-founder of ClinMet, Inc. He was on the board of directors, a scientific adviser and held an equity interest. Sharma's spouse is co-founder and was the president and chief operating officer and also held an equity interest.


News Article | August 22, 2016
Site: news.mit.edu

Researchers at MIT and the University of California at San Diego (UCSD) have recruited some new soldiers in the fight against cancer — bacteria. In a study appearing in the July 20 of Nature, the scientists programmed harmless strains of bacteria to deliver toxic payloads. When deployed together with a traditional cancer drug, the bacteria shrank aggressive liver tumors in mice much more effectively than either treatment alone. The new approach exploits bacteria’s natural tendency to accumulate at disease sites. Certain strains of bacteria thrive in low-oxygen environments such as tumors, and suppression of the host’s immune system also creates favorable conditions for bacteria to flourish. “Tumors can be friendly environments for bacteria to grow, and we’re taking advantage of that,” says Sangeeta Bhatia, who is the John and Dorothy Wilson Professor of Health Sciences and Electrical Engineering and Computer Science at MIT and a member of MIT’s Koch Institute for Integrative Cancer Research and its Institute for Medical Engineering and Science. Bhatia and Jeff Hasty, a professor of bioengineering at UCSD, are the senior authors of the paper. Lead authors are UCSD graduate student Omar Din and former MIT postdoc Tal Danino, who is now an assistant professor of biomedical engineering at Columbia University. The research team began looking into the possibility of harnessing bacteria to fight cancer several years ago. In a study published last year focusing on cancer diagnosis, the researchers engineered a strain of probiotic bacteria (similar to those found in yogurt) to express a genetic circuit that produces a luminescent signal, detectable with a simple urine test, if liver cancer is present. These harmless strains of E. coli, which can be either injected or consumed orally, tend to accumulate in the liver because one of the liver’s jobs is to filter bacteria out of the bloodstream. In their new study, the researchers delivered artificial genetic circuits into the bacteria, that allow the microbes to kill cancer cells in three different ways. One circuit produces a molecule called hemolysin, which destroys tumor cells by damaging their cell membranes. Another produces a drug that induces the cell to undergo programmed suicide, and the third circuit releases a protein that stimulates the body’s immune system to attack the tumor. To prevent potential side effects from these drugs, the researchers added another genetic circuit that allows the cells to detect how many other bacteria are in their environment, through a process known as quorum sensing. When the population reaches a predetermined target level, the bacterial cells self-destruct, releasing their toxic contents all at once. A few of the cells survive to begin the cycle again, which takes about 18 hours, allowing for repeated release of the drugs. “That allows us to maintain the burden of the bacteria in the whole organism at a low level and to keep pumping the drugs only into the tumor,” Bhatia says. The researchers tested the bacteria in mice with a very aggressive form of colon cancer that spreads to the liver. The bacteria accumulated in the liver and began their cycle of growth and drug release. On their own, they reduced tumor growth slightly, but when combined with the chemotherapy drug 5-fluorouracil, often used to treat liver cancer, they achieved a dramatic reduction in tumor size — much more extensive than if the drug was used on its own. This approach is well suited to liver tumors because bacteria taken orally have high exposure there, Bhatia says. “If you want to treat tumors outside the gut or liver with this strategy, then you would need to give a higher dose, inject them directly into the tumor, or add additional homing strategies,” she says. In previous studies, the researchers found that engineered bacteria that escape from the liver are effectively cleared by the immune system, and that they tend to thrive only in tumor environments, which should help to minimize any potential side effects. Martin Fussenegger, a professor of biotechnology and bioengineering at ETH Zurich, calls the new approach “unconventional” and “highly promising.” “This is a fascinating, refreshing, and beautiful concept,” says Fussenegger, who was not involved in the study. “In a world of mainstream cancer therapy concepts with often limited success, new therapy strategies are badly needed.” The researchers are now working on programming the bacteria to deliver other types of lethal cargo. They also plan to investigate which combinations of bacterial strains and tumor-targeting circuits would be the most effective against different types of tumors. The study was funded by the San Diego Center for Systems Biology, the National Institute of General Medical Sciences, the Ludwig Center for Molecular Oncology at MIT, an Amar G. Bose Research Grant, the Howard Hughes Medical Institute, a Koch Institute Support Grant from the National Cancer Institute, and a Core Center Grant from the National Institute of Environmental Health Sciences.


Razinkov I.A.,University of California at San Diego | Baumgartner B.L.,University of California at San Diego | Bennett M.R.,Rice University | Tsimring L.S.,University of California at San Diego | And 3 more authors.
Journal of Physical Chemistry B | Year: 2013

Most yeast genes are dispensable for optimal growth in laboratory cultures. However, this apparent lack of fitness contribution is difficult to reconcile with the theory of natural selection. Here we use stochastic modeling to show that environmental fluctuations can select for a genetic mechanism that does not affect growth in static laboratory environments. We then present a novel experimental platform for measuring the fitness levels of specific genotypes in fluctuating environments. We test this platform by monitoring a mixed culture of two yeast strains that differ in their ability to respond to changes in carbon source yet exhibit the same fitness level in static conditions. When the sugar in the growth medium was switched between galactose and glucose, the wild-type strain gained a growth advantage over the mutant strain. Interestingly, both our computational and experimental results show that the strength of the adaptive advantage conveyed by the wild-type genotype depends on the total number of carbon source switches, not on the frequency of these fluctuations. Our results illustrate the selective power of environmental fluctuations on seemingly slight phenotypic differences in cellular response dynamics and underscore the importance of dynamic processes in the evolution of species. © 2013 American Chemical Society.


Shokhirev M.N.,University of California at San Diego | Shokhirev M.N.,San Diego Center for Systems Biology | Hoffmann A.,University of California at San Diego | Hoffmann A.,San Diego Center for Systems Biology
PLoS ONE | Year: 2013

The immune response is a concerted dynamic multi-cellular process. Upon infection, the dynamics of lymphocyte populations are an aggregate of molecular processes that determine the activation, division, and longevity of individual cells. The timing of these single-cell processes is remarkably widely distributed with some cells undergoing their third division while others undergo their first. High cell-to-cell variability and technical noise pose challenges for interpreting popular dye-dilution experiments objectively. It remains an unresolved challenge to avoid under- or over-interpretation of such data when phenotyping gene-targeted mouse models or patient samples. Here we develop and characterize a computational methodology to parameterize a cell population model in the context of noisy dye-dilution data. To enable objective interpretation of model fits, our method estimates fit sensitivity and redundancy by stochastically sampling the solution landscape, calculating parameter sensitivities, and clustering to determine the maximum-likelihood solution ranges. Our methodology accounts for both technical and biological variability by using a cell fluorescence model as an adaptor during population model fitting, resulting in improved fit accuracy without the need for ad hoc objective functions. We have incorporated our methodology into an integrated phenotyping tool, FlowMax, and used it to analyze B cells from two NFκB knockout mice with distinct phenotypes; we not only confirm previously published findings at a fraction of the expended effort and cost, but reveal a novel phenotype of nfkb1/p105/50 in limiting the proliferative capacity of B cells following B-cell receptor stimulation. In addition to complementing experimental work, FlowMax is suitable for high throughput analysis of dye dilution studies within clinical and pharmacological screens with objective and quantitative conclusions. © 2013 Shokhirev, Hoffmann.


Mather W.H.,Virginia Polytechnic Institute and State University | Hasty J.,University of California at San Diego | Hasty J.,San Diego Center for Systems Biology | Tsimring L.S.,University of California at San Diego | Williams R.J.,University of California at San Diego
Biophysical Journal | Year: 2013

It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime. © 2013 Biophysical Society.


Longo D.M.,University of California at San Diego | Selimkhanov J.,University of California at San Diego | Selimkhanov J.,San Diego Center for Systems Biology | Kearns J.D.,University of California at San Diego | And 6 more authors.
PLoS Computational Biology | Year: 2013

Many cellular stress-responsive signaling systems exhibit highly dynamic behavior with oscillatory features mediated by delayed negative feedback loops. What remains unclear is whether oscillatory behavior is the basis for a signaling code based on frequency modulation (FM) or whether the negative feedback control modules have evolved to fulfill other functional requirements. Here, we use experimentally calibrated computational models to interrogate the negative feedback loops that regulate the dynamic activity of the transcription factor NF-κB. Linear stability analysis of the model shows that oscillatory frequency is a hard-wired feature of the primary negative feedback loop and not a function of the stimulus, thus arguing against an FM signaling code. Instead, our modeling studies suggest that the two feedback loops may be tuned to provide for rapid activation and inactivation capabilities for transient input signals of a wide range of durations; by minimizing late phase oscillations response durations may be fine-tuned in a graded rather than quantized manner. Further, in the presence of molecular noise the dual delayed negative feedback system minimizes stochastic excursions of the output to produce a robust NF-κB response. © 2013 Longo et al.


Kolnik M.,University of California at San Diego | Tsimring L.S.,University of California at San Diego | Tsimring L.S.,San Diego Center for Systems Biology | Hasty J.,University of California at San Diego | Hasty J.,San Diego Center for Systems Biology
Lab on a Chip - Miniaturisation for Chemistry and Biology | Year: 2012

Microfluidic perfusion cultures for mammalian cells provide a novel means for probing single-cell behavior but require the management of culture parameters such as flow-induced shear stress. Methods to eliminate shear stress generally focus on capturing cells in regions with high resistance to fluid flow. Here, we present a novel trapping design to easily and reliably load a high density of cells into culture chambers that are extremely isolated from potentially damaging flow effects. We utilize a transient on-chip vacuum to remove air from the culture chambers and rapidly replace the volume with a liquid cell suspension. We demonstrate the ability of this simple and robust method to load and culture three commonly used cell lines. We show how the incorporation of an on-chip function generator can be used for dynamic stimulation of cells during long-term continuous perfusion culture. © 2012 The Royal Society of Chemistry.


Selimkhanov J.,University of California at San Diego | Hasty J.,University of California at San Diego | Hasty J.,San Diego Center for Systems Biology | Tsimring L.S.,University of California at San Diego | Tsimring L.S.,San Diego Center for Systems Biology
Current Opinion in Biotechnology | Year: 2012

A mechanistic understanding of gene regulatory network dynamics requires quantitative single-cell data of multiple network components in response to well-defined perturbations. Recent advances in the development of fluorescent biomarkers for proteins, detection of RNA and interactions, microfluidic technology, and high-resolution imaging have set the stage for a host of new studies that elucidate the important roles of stochasticity and cell-cell variability in response to external perturbations. In this review, we briefly describe methods for high-resolution visualization and the control of gene expression, along with application of these novel methods to recent studies involving gene networks. © 2011 Elsevier Ltd.


Naomi Handly L.,University of California at San Diego | Pilko A.,University of California at San Diego | Wollman R.,University of California at San Diego | Wollman R.,San Diego Center for Systems Biology
eLife | Year: 2015

Population averaging due to paracrine communication can arbitrarily reduce cellular response variability. Yet, variability is ubiquitously observed, suggesting limits to paracrine averaging. It remains unclear whether and how biological systems may be affected by such limits of paracrine signaling. To address this question, we quantify the signal and noise of Ca2+and ERK spatial gradients in response to an in vitro wound within a novel microfluidics-based device. We find that while paracrine communication reduces gradient noise, it also reduces the gradient magnitude. Accordingly we predict the existence of a maximum gradient signal to noise ratio. Direct in vitro measurement of paracrine communication verifies these predictions and reveals that cells utilize optimal levels of paracrine signaling to maximize the accuracy of gradient-based positional information. Our results demonstrate the limits of population averaging and show the inherent tradeoffin utilizing paracrine communication to regulate cellular response fidelity. © Handly et al.

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