Yishai Shimoni, PhD (pronounced like 'e-shy')
Research Interests
(a more elaborate description)
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Reverse Engineering of Regulatory Networks in Cells
Genes provide the codes for producing the proteins that are necessary
for the continued activity cells.
However, varying internal and external conditions
require different genes to be activated at different times.
This is achieved by an intricate network of
interactions between genes, proteins, and non-coding RNAs,
which enables the controlled synthesis
of proteins with varying conditions.
In the Califano lab we are developing methods to identify this
regulatory network using genome-wide expression measurements,
and then use these networks in order to understand how cells
respond to changes in conditions,
to pathogens, to drugs, or in disease states (such as cancer).
Using this understanding we are then able to predict which genes
are responsible for a change in cellular behaviour,
thus suggesting a candidate for therpeutic intervention.
Similarly, in the case of drug perturbation, such analyses allow
us to understand the mechanisms by which drugs work,
thus predicting, effect, side-effects, and synergy between drugs.
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Stochastic Effects and Dynamics of Gene Regulation Networks
Another way to conceptualize the cell's control over protein
expression levels under varying conditions is as
a computational procedure in which the inputs
are the external and internal conditions (e.g. the concentrations
of proteins, nutrients, hormones, toxins, etc.),
and the output is the production of the appropriate proteins.
However, treating the whole cell as a computation process is too
complicated in practice.
My research involves identifying small subnetworks
that are involved in important cellular processes and
modeling them mathematically as a computational process.
Analysis of these subnetworks can then be performed using accurate
simulations of the dynamics of the expression of each molecule in the
sub network, in order to understand the biochemical processes
that occur within living cells, and their significance.
In many cases, the inherent randomness of the system
(called stochasticity) is a determining factor and requires
incorporating stochastic effects into the mathematical model.
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Quantum Computers, Quantum Algorithms, and their connection to Entanglement:
A few quantum algorithms were developed that
offer the possibility of running algorithm more
efficiently than is possible classically
(Shor's and Grover's algoritms,
other quantum algorithms).
It is not known, however, how to develop new efficient algorithms.
What is known, is that if the quantum algorithm
does not use a quantum property called entanglement
then it can be simulated efficiently on a classical computer,
and is therefore not more efficient than a classical algorithm
(about quantum entanglement -
Stanford Encyclopedia of Philosophy,
Wikipedia).
Still, the nature of quantum entanglement and its relation to the speed
up offered by quantum algorithms is not clear.
It is thus essential to be able to quantify the entanglement
generated during the process of any algorithm.
In my PhD (under the
supervision of Prof.
Ofer Biham) we have developed a method to quantify the
entanglement of any state,
and have applied this method to see how quantum entanglement
changes during the operation of different quantum algorithms.
In this work we elucidated both the physical meaning of
quantum entanglement and
its role in efficient quantum algorithms>
Posters and Presentations
Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression
(
PDF)
Poster abstract:
When high throughput measurements of gene activity levels are associated
with perturbations, such as inhibition of cellular pathway components,
the regulatory interactions between genes can be elucidated.
The activity levels of components in the signaling network that mediate
the changes in gene expression are less accessible to experimental
measurement. Recently, it was shown that the profile of the early gene
program following perturbation provides a sensitive reflection of
the response state of the signaling network. We have now developed a
technique that uses early gene responses to cell stimulation and
systematic signaling component perturbation to reverse engineer the
functional interactions between the signaling components. This approach
is robust to noise and produced novel and verifiable predictions based
on experimental datasets.
(The poster was presented at
the CMPI Symposium on Multi-Scale Modeling of
Host/Pathogen Interactions,
held on June 23 - 25, 2009, at the University of Pittsburgh).
Stochastic Analysis of Bi-stability in Mixed Feedback Loops
Abstract:
Mixed feedback loops (MFLs) consist of two genes that mutually regulate
each other's expression using different regulatory mechanisms. The first
gene encodes a transcription factor and the second gene encodes either a
small RNA that exerts its regulation post-transcriptionally or a protein
that exerts its regulation post-translationally. We focus on mixed
feedback loops where all regulations are negative: the transcription
factor is a repressor, the small RNA inhibits the translation of the
transcription factor by binding to its mRNA and the protein binds to the
transcription factor and inhibits its activity. While intuitively it
might be expected that such MFLs will exhibit bi-stability, we
demonstrate by computer simulations that the dynamical properties of the
MFLs depend on the regulatory mechanisms involved. For most of the
parameter range the MFL involving post-translational regulation by
protein-protein interaction indeed shows bi-stability, but the MFL
involving post-transcriptional regulation by small RNAs displays a
metastable state. This conclusion has been achieved by using stochastic
simulations for the analysis and is not attainable by deterministic
simulations per se. We investigate the conditions that give rise to
bi-stability and metastability. Focusing on an actual mixed feedback
loop in Escherichia coli involving the transcription factor Fur and the
small RNA RyhB, we demonstrate how metastability fits its cellular role.
(This presentation was given as an invited talk on the
CCS open day 2008,
that took place at the Hebrew University of Jerusalem, Israel,
on Sep. 18, 2008).
Role of Small nonCoding RNA in Genetic Regulation Networks
(
PDF)
Poster abstract:
We show quantitatively that regulation by small RNA
(sRNA) is advantageous when fast responses to external signals are
needed, which is consistent with experimental data about its
involvement in stress responses. We integrate the network of sRNA
regulation in E. coli with the transcription regulation network,
uncovering mixed regulatory circuits consisting of both
transcriptional and post-transcriptional regulations. Analysis of one
such regulatory circuit, a feed-forward loop of OmpR-MicF-ompF,
demonstrates its advantages: tight repression, guaranteed by the
combination of transcriptional and post-transcriptional regulations,
and fast recovery upon the end of the external signal. Another
regulatory circuit is the genetic mixed feedback loop, where gene a
regulates gene b by transcriptional regulation, while gene b regulates
gene a by either protein-protein interaction or small non-coding
RNA-mRNA interaction. Mixed feedback loops tend to exhibit
bi-stability or oscillations.
These loops are analysed using deterministic and stochastic
methods shedding more light on the possible roles of sRNA regulation.
(The poster was presented in two conferences:
Physical and Chemical Foundations of Bioinformatics Methods
(PCFBIM07),
held in Dresden, Germany, on June 18 - 22, 2007;
and Functional Genomics & Systems Biology,
Held at the
Wellcome Trust Conference Centre, in Hinxton, UK,
on October 10 - 13, 2007).
Defining and Measuring Multi-partite Entanglement
(
PDF)
Poster abstract: Quantum entanglement between many qubits plays a
crucial role in quantum algorithms. The structure of this entanglement
cannot be fully described by bipartite entanglement measures. It is
thus important to develop ways to characterize and evaluate such
entanglement. Here, we consider an operational measure of multipartite
entanglement and demonstrate its relevance to quantum algorithms.
(the poster was presented in the 3rd
Workshop on Decoherence, Entanglement and Information in Complex
Systems (DEICS III) and the Workshop on Quantum Dynamics of Cold Atoms
and Light (QUDAL), which were held at the Dan hotel, Eilat, from Feb.
26 to March 3, 2006).
Entanglement during Grover's algorithm
Poster abstract: It is believed that one of the main factors
contributing to the efficiency of quantum algorithms is the fact that,
unlike their classical counterparts, they can use entanglement. In this
work we introduce a calculable measure of entanglement, and proceed to
show that in Grover’s search algorithm entanglement is indeed created,
and then removed in order to reach the final state.
(The poster was presented in two conferences:
Entanglement, information and noise
(EIN04),
held in Krzyzowa (Lower
Silesia), Poland, on June 14 - 20, 2004;
and the summer school on
Quantum Logic and Communication, held in
Cargese, Corsica, France, on August 16-28, 2004).
In 2005-2007 I was a TA (teaching assistatnt) in two courses:
"Electricity and Magnetism",
(course registry, website on the univeristy's
Highlearn system), and
"Mechanics and Relativity"
(course registry, my lecture notes).
In 2004 and 2005 I was the teacher in two courses for
bioinformatics students:
77164 -
Workshop in the principles of physics B (electricity and waves),
and 77163
- Workshop in the principles of physics A (mechanics).
In 2001-2003 I was a TA in the courses "Mechanics for Biology Students",
and "Electricity for Biology Students".
I was voted best TA in the physics department in 2002.
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