3/4 – Design principles of altruistic behavior

When: Wednesday, 3.4.2013  15:00

Where:  Katzir Club (Green Bldg)

Speaker: Ishay Ben-Zion (Eldar lab, TAU)

 

Many theoretical studies in the last 50 years have tried to explain the evolutionary maintenance of altruistic behavior in the face of exploitive cheaters. It is well known that a strong population structure is crucial for the maintenance of altruistic cooperation. However, it is unclear how this requirement is affected by the regulatory structure of the cooperative trait. The aim of this work is to examine the role of feedback regulation in maintaining cooperation in a public goods model. Our results suggest an important advantage of positive feedback regulation over negative or no feedback: a positive-feedback structure promotes cooperation in weaker population structures as well, thus relieving a major limitation of explaining altruism. These results demonstrate that social traits may have different design principles than non-social traits. Moreover, since bacterial cooperative traits are often feedback-regulated through sensory mechanisms, elucidating the role of feedbacks may lead to experimentally amenable predictions.

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by biomathsjctau on March 29, 2013  •  Permalink
Posted in cell communication, evolution

Posted by biomathsjctau on March 29, 2013

http://biomathsjctau.wordpress.com/2013/03/29/34-design-principles-of-altruistic-behavior/

13/3 – Modeling of cytomegalovirus viral kinetic patterns and clinical implications

When: Wednesday, 13.3.2013  15:00

Where:  Katzir Club (Green Bldg)

Speaker: Dr. Jessica Rose (Bar Ilan University)

The herpes virus CMV has co-evolved with humans over many millennia and is well adapted to the immunocompetent host.  However, in a variety of immune deficient hosts including the neonate, organ transplant recipients, patients with common variable immune deficiency and HIV-infected patients, the virus can cause life-threatening pathologies.  The synthetic nucleoside analogue Ganciclovir (GCV) has become the antiviral drug of choice for controlling CMV infection and disease.  The drug requires initial phosphorylation by the viral kinase UL97: an orthologue of the cellular cyclin-dependent kinases family which performs a variety of roles in the viral life cycle. Using frequent CMV load measurements over the course of 21 days of GCV therapy, we show that novel viral decay patterns are apparent in a large percentage of individuals (>60%) that cannot be explained by conventional viral dynamic models.  We therefore developed a novel 7-dimensional dynamic model that includes an intracellular feedback loop between UL97 and GCV which adequately describes the unique kinetic patterns of CMV decline observed in patients following exposure to therapy.  This has vital clinical, pharmaceutical and general scientific implications as the link between the unique viral kinetics and mode-of-action of GCV has never been reported.

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by biomathsjctau on March 7, 2013  •  Permalink
Posted in epidemiology

Posted by biomathsjctau on March 7, 2013

http://biomathsjctau.wordpress.com/2013/03/07/133-modeling-of-cytomegalovirus-viral-kinetic-patterns-and-clinical-implications/

28/1 – Modeling the handicap principle?

When: Monday,  28.1.2013  14:00

Where:  Katzir Club (Green Bldg)

Speaker: Keith Harris (TAU)

Since Zahavi [1] suggested the handicap principle, there have been many attempts to produce a formal mathematical model to understand whether costly signaling is an evolutionarily stable strategy (ESS). Maynard Smith [2] initially rejected the handicap principle, however, his model assumed that the handicap is a gene that is inherited and causes a loss in fitness in males who carry the handicap gene. In 1990, Grafen [3] published the first ESS model of the handicap principle, but his model assumed that the signaling parties must have a conflict of interest to necessitate reliable signaling, such as the conflict of interest between males competing for mates. This assumption contradicts Zahavi’s claim that the handicap principle is necessary even between isogenic cells in multicellular organisms [4], a claim later modeled by Krakauer and Pagel [5]. I will present the original models of Maynard Smith and Grafen, along with the more recent criticism of Számadó [6] and Zollman et al. [7], all the while focusing on the biological significance of the assumptions of each model.

References:

[1] Zahavi, Amotz. Mate selection—a selection for a handicap. Journal of theoretical Biology 53.1 (1975): 205-214.

[2] Maynard Smith, John. Sexual selection and the handicap principle. Journal of Theoretical Biology 57.1 (1976): 239-242.

[3] Grafen, Alan. Biological signals as handicaps. Journal of theoretical biology 144.4 (1990): 517-546.

[4] Zahavi, Amotz. The fallacy of conventional signalling. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 340.1292 (1993): 227-230.

[5] Krakauer, David C., and Mark Pagel. Selection by somatic signals: the advertisement of phenotypic state through costly intercellular signals. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 351.1340 (1996): 647-658.

[6] Számadó, Szabolcs. The cost of honesty and the fallacy of the handicap principle. Animal Behaviour 81.1 (2011): 3-10.

[7] Zollman, Kevin JS, Carl T. Bergstrom, and Simon M. Huttegger. Between cheap and costly signals: the evolution of partially honest communication. Proceedings of the Royal Society B: Biological Sciences 280.1750 (2013).

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by biomathsjctau on January 21, 2013  •  Permalink
Posted in evolution
Tagged

Posted by biomathsjctau on January 21, 2013

http://biomathsjctau.wordpress.com/2013/01/21/281-modeling-the-handicap-principle/

21/1 – The lifetime of synaptic intermediates during the search for homology on DNA

When: Monday,  21.1.2013  14:00

Where:  Katzir Club (Green Bldg)

Speaker: Adam Mani (Joel Stavans Lab, Weizmann)

Homologous recombination plays central roles in DNA repair and in the generation of genetic diversity in organisms from bacteria to man. At the heart of this process, a segment of DNA in the genome is replaced by a new one. In order for strand exchange to occur, a single-stranded DNA-recombinase complex must locate a homologous target among a large number of sequences on a genome. The search must be completed within physiologically relevant timescales, and a key element in this search is the synapse lifetime, i.e. the time needed for a comparison of the sequences in one iteration of the search. In this work, we visualized fluorescently tagged individual synapses formed by RecA, a prokaryotic recombinase, and measured their lifetime as a function of synapse length and differences in sequence between the participating DNAs. Surprisingly, lifetimes can be ~10 s long when the DNAs are fully heterologous, and much longer for partial homology. These results suggest the need for facilitated homology search mechanisms for the search to be completed within the timescales required in vivo.

 

References:

 Mani A et al  Caught in the Act: the Lifetime of Synaptic Intermediates During the Search for Homology on DNA , Nucleic Acids Research, 38:6, 2036–2042 (2010).
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by biomathsjctau on January 13, 2013  •  Permalink
Posted in single molecule/single cell
Tagged ,

Posted by biomathsjctau on January 13, 2013

http://biomathsjctau.wordpress.com/2013/01/13/211-the-lifetime-of-synaptic-intermediates-during-the-search-for-homology-on-dna/

24/12 – De Bruijn meets DNA

When: Monday,  24.12.2012  12:00 noon (not at 14:00 as was previously published)
Where:  Britannia 202 (Map)
Speaker: Yaron Orenstein (TAU)

Utilizing reverse complementarity to efficiently cover all k-mers, with application to protein binding microarrays

Protein binding micorarrays are a novel technology that measures binding intensity of a protein to thousands of short double stranded DNA sequences called probes. By using probes that cover all k-mers, a comprehensive picture of the binding spectrum is obtained.
Space constraints limit the number of probes that can be put on an array. The standard way to create the probes is by using a de Bruijn sequence, which contains every k-mer exactly once.
Here we show that by utilizing the reverse complementarity of DNA we can create a sequence that is much shorter with the property that it contains each k-mer or its reverse complement. The obtained saving factor is near optimal and approaches 2 as k increases. This allows generation of protein binding microarrays with more effective coverage.
Keywords:  de Bruijn sequence, de Bruijn graph, Euler tour
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by biomathsjctau on December 4, 2012  •  Permalink
Posted in comp genomics

Posted by biomathsjctau on December 4, 2012

http://biomathsjctau.wordpress.com/2012/12/04/2412-de-bruijn-meets-dna/

3/12 – Measuring collective tumor cell migration

When: Monday,  3.12.2012  12:00 noon

Where:   Britannia 202 (Map)

Speaker: Assaf Zaritsky (TAU)

Collective cell migration plays a major role in many essential biological processes. Understanding the molecular and cellular mechanisms of this migration mode is limited despite decades of extensive investigations.
A set of quantitative measures, based on live cell imaging is presented, to demonstrate enhanced analyses of collective cell motility in a high throughput manner. The toolbox includes multicellular segmentation, motion estimation and tracking, motion in clusters, and measures for cells’ individuality, indirect morphology and spatiotemporal motility and visualization.
The subject matter studied is induction of collective tumor cell migration by Hepatocyte Growth Factor / Scatter Factor (HGF/SF) – Met-signalling, master regulators of cell motility in normal and malignant processes. HGF/SF-Met-signalling dramatically alters the morphokinetic dynamics of collective migration of breast cancer tumor cells by increasing cells’ orientation and cooperation capabilities. Met-inhibition reduces these phenomena.
Exploiting these high-throughput phenotyping capabilities revealed that HGF/SF signalling induced metabolic plasticity of the cells altering from glycolysis to oxidative phosphorylation thus increasing collective cell motility. However a profound inhibition of hexokinase activity significantly reduces it.
Another application was to investigate cells that maintain amoeboid-like motility while traversing in a tissue consisting of epithelial cells, as a simple model for initial metastasis formation. Low concentrations of exogenous YFP- WT-Met, activating and inhibitory forms, were expressed in a monolayer of tumor cells to simulate the phenomenon. Activating Met-variants are generally characterized by cells’ amoeboid-like motility; Met-inhibition induces epithelial motility, while HGF/SF-induces increased individuality, a measure for the motility of transfected cells in relation to their vicinity of untreated cells.
The findings reported here are anticipated to shed light on the molecular and cellular alterations that enable metastasis formation in breast cancer and hence to aid in identifying new targets for therapy.

References:

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by biomathsjctau on November 29, 2012  •  Permalink
Posted in cell communication, image processing
Tagged ,

Posted by biomathsjctau on November 29, 2012

http://biomathsjctau.wordpress.com/2012/11/29/0312-measuring-collective-tumor-cell-migration/

29/10 – Evolutionary trade-offs, Pareto optimality and the phenotypic space

When: Monday,  29.10.2012  14:00

Where:  Katzir Club (Green Bldg)

Speaker: Hila Sheftel (Uri Alon Lab, Weizmann)

Biological systems that perform multiple tasks face a fundamental trade-off: A given phenotype cannot be optimal at all tasks. Here we ask how trade-offs affect the range of phenotypes found in nature. Using the Pareto front concept from economics and engineering, we find that best–trade-off phenotypes are weighted averages of archetypes – phenotypes specialized for single tasks. For two tasks, phenotypes fall on the line connecting the two archetypes, which could explain linear trait correlations, allometric relationships, as well as bacterial gene-expression patterns. For three tasks, phenotypes fall within a triangle in phenotype space, whose vertices are the archetypes, as evident in morphological studies, including on Darwin’s finches. Tasks can be inferred from measured phenotypes based on the behavior of organisms nearest the archetypes.

 

References:

 O Shoval, H Sheftel, G Shinar, Y Hart, O Ramote, A Mayo, E Dekel, K Kavanagh and U Alon: Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space  Science, 336:1157–1160 (2012).
News and views: E Noor and R Milo: Efficiency of evolutionary trade-offs

 

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by biomathsjctau on October 13, 2012  •  Permalink
Posted in evolution
Tagged ,

Posted by biomathsjctau on October 13, 2012

http://biomathsjctau.wordpress.com/2012/10/13/2910-evolutionary-trade-offs-pareto-optimality-and-the-phenotypic-space/

24/7 Disorganization principles of metazoan genomes

When: Tuesday,  24.7.2012  14:00

Where:  Katzir Club (Green Bldg)

Speaker: Effi Kenigsberg (Amos Tanay Lab, Weizmann)

Genomes encode regulatory information that facilitates interactions with molecules that modify the structure and dynamics of chromatin, DNA methylation and other epigenomic factors. Mutational and selective evolutionary dynamics are affected by these physical genomic constraints, quantitatively and qualitatively. While selection maintains functional and structural elements, mutational mechanisms create non-uniform spurious sequence patterns which may co-localize with functional elements. In the talk, we will discuss sequence patterns and evolutionary dynamics of CpG islands in primates and around conserved elements in flies and show how this triple interaction between genome, epigenome and evolution organize and disorganize these genomes.

References:

NM Cohen, E Kenigsberg, and A Tanay: Primate CpG Islands Are Maintained by Heterogeneous Evolutionary Regimes Involving Minimal Selection.
Cell, (2011)

E Kenigsberg, A Bar, E Segal, A Tanay: Widespread Compensatory Evolution Conserves DNA-Encoded Nucleosome Organization in Yeast. PLoS Comput Biol, (2010)

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Crash guide on – What are CpG islands and what is their connection to epigenome

More:  Resource on evolutionary dynamics of CpG islands

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by biomathsjctau on July 10, 2012  •  Permalink
Posted in comp genomics

Posted by biomathsjctau on July 10, 2012

http://biomathsjctau.wordpress.com/2012/07/10/247-disorganization-principles-of-metazoan-genomes/

13/6 What makes persistent memory persistent? – UPDATE – lecture postponed

When: Wednesday,  13.6.2012  15:05

Where:  Katzir Club (Green Bldg)

Speaker: Uri Livneh (Rony Paz Lab, Weizmann)

Emotional memories can persist for a lifetime, but can also undergo extinction. Although we know about the mechanisms involved in expression and extinction, we know very little about the mechanisms that determine if a specific memory would persist or not.
Here, we use partial reinforcement extinction effect (PREE) to explore the neural mechanisms that differentiate persistent from labile memories. We recorded the simultaneous activity of neurons in the amygdala and the dorsal-anterior-cingulate-cortex (dACC) while monkeys engaged in tone-odor aversive-conditioning. We report that under continuous-reinforcement-schedule (ConS), activity in the amygdala precedes behavioral response; whereas under partial-schedule (ParS), dACC activity precedes. Moreover, we find that ParS induced cross-regional pairwise correlations throughout the entire acquisition session, and their magnitude and precision predicted the later resistance to extinction. Our results suggest that memory persistence depends on distributed representations; and specifically, aversive memories that are resistant to extinction are maintained by correlated amygdala-dACC activity
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Reference:
U Livneh et al.: Amygdala-prefrontal synchronization underlies resistance to extinction of aversive memories, in press (link will be posted later)
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by biomathsjctau on May 29, 2012  •  Permalink
Posted in comp neuro

Posted by biomathsjctau on May 29, 2012

http://biomathsjctau.wordpress.com/2012/05/29/136-what-makes-persistent-memory-persistent/

2/2 Cell communication, now in 3D!

When: Thursday,  2.2.2012  14:00

Where:  Katzir Club (Green Bldg)

Speaker: Alon Yaniv (Oleg Krichevsky Lab, BGU)

I will discuss the issue of cell communication within tissue and the possible effects of 3D clustering on the nature of signaling processes. Focusing on cytokine exchange within cells in the immune system I will try to make a case for the (often overlooked) importance of spatial ordering. I will offer a novel methodology of conducting cell signaling assays and show some promising preliminary results.

Reference:  AS Perelson and G Weisbuch, Immunology for physicists, Reviews of Modern Physics, 69 (4), 1997

For phenomenological jibber jabber: google T-cells

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by biomathsjctau on January 16, 2012  •  Permalink
Posted in cell communication

Posted by biomathsjctau on January 16, 2012

http://biomathsjctau.wordpress.com/2012/01/16/22-cell-communication-now-in-3d/

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