Cellular signaling involves the transmission of environmental information
through cascades of stochastic biochemical reactions, inevitably introducing
noise that compromises signal fidelity. Each stage of the cascade often takes
the form of a kinase-phosphatase push-pull network, a basic unit of signaling
pathways whose malfunction is linked with a host of cancers. We show this
ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov
(WK) optimal noise filter. Using concepts from umbral calculus, we generalize
the linear WK theory, originally introduced in the context of communication and
control engineering, to take nonlinear signal transduction and discrete
molecule populations into account. This allows us to derive rigorous
constraints for efficient noise reduction in this biochemical system. Our
mathematical formalism yields bounds on filter performance in cases important
to cellular function---like ultrasensitive response to stimuli. We highlight
features of the system relevant for optimizing filter efficiency, encoded in a
single, measurable, dimensionless parameter. Our theory, which describes noise
control in a large class of signal transduction networks, is also useful both
for the design of synthetic biochemical signaling pathways, and the
manipulation of pathways through experimental probes like oscillatory input.Comment: 15 pages, 5 figures; to appear in Phys. Rev. 

Hinczewski, Michael

Thirumalai, D.

English

arXiv

Thirumalai, Devarajan

Elsevier - Publisher Connector 

Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise 

Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input

Michael Hinczewski

D. Thirumalai

Directory of Open Access Journals

Physical Review X

Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

Wednesday, February 11, 2015 613aTheguinea pig HF proteome exhibited classic biosignatures of cardiac HYP,
left ventricular dysfunction, fibrosis, cellular degeneration, inflammation and
extravasation. Fatty acid metabolism, mitochondrial transcription/translation
factors, and other mitochondrial processes, were downregulated. Processes/
proteins upregulated in HF include fibrillogenesis, extracellular matrix remod-
eling, cytoskeletal proteins, the unfolded-protein response, and acute phase
inflammation markers. Among metabolites, downregulation of acyl-carnitines
was observed in HYP, while fatty acids accumulated in HF. Levels of the
TCA cycle metabolite, citrate, and the potent inhibitor, 2-methylcitrate,
increased upon transition from HYP to HF. Among transcripts, downregulation
of repolarizing Kþ channels, SERCA2a, phospholamban and ryanodine recep-
tor was noted, and upregulation of mRNAs for cyclic-nucleotide gated
hyperpolarization-activated channels and TRPC6.
The biosignature of decompensation in the guinea pig HF/SCD model parallels
prior rodent models but spotlights metabolic bottlenecks. Fatty acid use is
likely abrogated by impaired mitochondrial import and oxidation, while
compensation by glycolytic flux is predicted to be impaired, along with TCA
cycle dysfunction and accumulation of inhibitors (e.g. citrate). Pathway anal-
ysis suggests avenues for metabolic therapy beyond PPAR activation.
3090-Pos Board B520
Cellular Signaling Networks Function as GeneralizedWiener-Kolmogorov
Filters to Suppress Noise
Michael Hinczewski1, Devarajan Thirumalai2.
1Department of Physics, Case Western Reserve University, Cleveland, OH,
USA, 2Institute for Physical Science and Technology, University of
Maryland, College Park, College Park, MD, USA.
Cells process and transmit information about their environment through
complex signaling cascades. The accurate transmission of the information is
crucial for normal function, with defects in the cascades linked to a host
of cancers. As in many designed communications systems, these biological
circuits must cope with noise that inevitably corrupts the signal. How to effi-
ciently filter noise, to reconstruct the best estimate of the input, has been a key
problem in engineering. One of the great advances in this area was a mathe-
matical framework developed by Norbert Wiener and Andrey Kolmogorov
during the Second World War, originally inspired by the need to filter noise
in the targeting of anti-aircraft systems. Remarkably, we show that cells effec-
tively implement the same mathematical solution encoded within the chemical
reaction network of a kinase phosphatase push-pull loop, a basic unit of
signaling pathways. To demonstrate this, we generalize the ideas of Wiener
and Kolmogorov to deal with some of the challenges that arise in the bio-
logical context, including signaling through discrete changes in molecular
populations, and the highly nonlinear relation between input and output. We
provide mathematically rigorous bounds on the performance of biochemical
noise filters, and highlight features of the system relevant for optimizing filter
efficiency, encoded in a single, measurable, dimensionless parameter. Our
theory, which describes noise control in a large class of signal transduction
networks, is also useful both for the design of synthetic biochemical signaling
pathways, and the manipulation of pathways through experimental probes like
oscillatory input.
3091-Pos Board B521
To Grow is not Enough: The Impact of Cell Response Time on Fitness
Nash Rochman, Fangwei Si, Sean Sun.
Johns Hopkins, Baltimore, MD, USA.
Recent results showing the magnitude of fluctuations in individual cell division
times to be highly concerted across widely varying cell types and environmental
conditions have been difficult to explain with current molecular cell cycle
models. Herewe present a phenomenologicalmodel for the regulation of cellular
division time distributions determining both bulk growth rate and ensemble fluc-
tuations. A cellular ‘‘fitness’’ function is proposed which incorporates not only
growth rate, which is maximized when fluctuations are minimized, but also
ensemble response time to environmental stimulus which decreases for
increasing fluctuations. Single cell division data is collected on a population of
isogenic cells subjected to varying environmental stimuli and compared to the
model. Our findings suggest that even cells exhibiting exponential growth do
not optimize their ‘‘fitness’’ through growth rate alone, but also response time.
3092-Pos Board B522
Bcl-2 Overexpression Stimulates Glycolysis and Lactic Fermentation in a
Bax-Dependent Fashion
Bushra Mahmood, Jessica Wilson, Miriam Ahmad, Patricia Olino,
Justin King, Laurent Dejean.
Chemistry, California State University of Fresno, Fresno, CA, USA.
It is now well established that shifts in energy metabolism are associated with
cancer development and progression. The most studied of these phenomenais the Warburg effect, which corresponds to an increase of anaerobic glycol-
ysis vs mitochondrial oxidative phosphorylation to produce energy for
cellular processes. However, the mechanisms related to these metabolic
switches are still a matter of debate. Bcl-2 family proteins contain both
pro- (e.g. Bax), and anti-apoptotic (e.g. Bcl-2) members which are respec-
tively encoded by tumor suppressors and proto-oncogenes. Up-regulation of
the anti-apoptotic proteins Bcl-2 has been associated with Non-Hodgkin’s
lymphoma; and certain studies indicate that Bcl-2 plays a role in the regula-
tion of energy metabolism. However, the molecular players involved in this
regulation are still to be defined. We recently observed that Bcl-2 overexpres-
sion led a significant increase of both glucose consumption and lactate pro-
duction rates in a mouse pro-lymphocyte B cell line. This phenomenon was
associated with a stimulation of the lactate dehydrogenase (LDH) enzyme
specific activity; and a Bcl-2-mediated increase of the expression of the
LDH-A subunit. Also, this phenotype was strongly attenuated if a Bcl-2
mutant of interaction with Bax (Bcl-2-G145E) was overexpressed instead
of native Bcl-2. These data suggest that Bcl-2 expression levels may play
an active role in the stimulation of lactic fermentation commonly observed
in blood cancer cells; and that this effect may be dependent to the ability
of Bcl-2 to physically interact with Bax.
3093-Pos Board B523
Human Adipose Cell Response to Insulin: Analysis of Cellular Switch-Like
Transformations and Distributions
Vladimir A. Lizunov1, Paul S. Blank1, Karin G. Stenkula2, Monica Skarulis3,
Samuel Cushman4, Joshua Zimmerberg1.
1NICHD, Laboratory of Cellular and Molecular Biophysics, National
Institutes of Health, Bethesda, MD, USA, 2Experimental Medical Sciences,
Lund University, Lund, Sweden, 3Diabetes, Endocrinology, and Obesity
Branch, National Institute of Diabetes and Digestive and Kidney Diseases,
Bethesda, MD, USA, 4Experimental Diabetes, Metabolism, and Nutrition
Section, National Institute of Diabetes and Digestive and Kidney Diseases,
Bethesda, MD, USA.
Insulin resistance is associated with decreased glucose transporter-4
(GLUT4) translocation in adipose cells and muscles in response to insulin.
Here we quantified, per cell, insulin’s effects on GLUT4 storage vesicle
(GSV) tethering and fusion in adipose cells from human subjects with vary-
ing insulin sensitivity index (SI). Basal GSV tethering and fusion rates were
distributed unimodally and did not vary significantly with SI. In contrast,
after a maximal insulin challenge, both tethering and fusion rates were bimo-
dally distributed, with two distinct subpopulations; the first subpopulation
was indistinguishable from the basal distribution, while the second corre-
sponded to a normal insulin response. Importantly, the fraction of cells in
the two-subpopulations were strongly correlated with donor subject SI.
These data suggest that the loss of systemic SI is not due to a gradual
decrease in the insulin response of all cells, but rather an increase in the
fraction of cells that switch off their insulin response. This observed hetero-
geneity may be an important consideration in modeling the cellular transi-
tions and systemic changes associated with the onset of Type II diabetes.
Thus, isolated human adipose cells, when treated with high concentrations
of insulin, exist in either the basal or fully insulin-stimulated state, and pop-
ulation dose response curves reflect the proportion of adipose cells in these
two distinct states.
3094-Pos Board B524
Accelerating Systems Biology Computation: Enhanced Sampling of
Spatially Realistic Stochastic Models using the Weighted Ensemble
Approach
Rory Donovan.
University of Pittsburgh, Pittsburgh, PA, USA.
We apply the ‘‘weighted ensemble’’ (WE) simulation strategy, previously em-
ployed in the context of molecular dynamics and spatially homogeneous chem-
ical kinetics simulations, to a number of three dimensional spatially resolved
stochastic systems-biology models. WE is relatively easy to implement, does
not require extensive hand-tuning of parameters, does not depend on the details
of the underlying simulation algorithm, and can facilitate the unbiased sam-
pling of extremely rare events.
We study three systems of varying complexity and structure: a toy model in
an artificial geometry with ~10^3 diffusing molecules, a spatially realistic
cellular model with ~10^6 molecules, and a spatially realistic neuromuscular
junction model with time-varying rate ‘‘constants’’ containing ~10^6 mole-
cules. We are able to speed up the sampling of key events by many orders
of magnitude and obtain unbiased estimates of event probabilities. Data is ob-
tained using the publicly available ‘‘WESTPA’’ implementation of WE,
which orchestrates MCell kinetic Monte Carlo trajectories of the various
models.


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Cellular signaling networks function as generalized Wiener-Kolmogorov filters to suppress noise

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