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How, what & why publish (or perish…)

How, what & why publish (or perish…). Outline 1.How to publish? 1.What to publish? 2.The rewards of publishing…

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How, what & why publish(or perish…)

Outline

1. How to publish?

2. What to publish?

3. The rewards of publishing…

1 .How to publish…

•Published Online October 11 2012Science 23 November 2012: Vol. 338 no. 6110 pp. 1065-1069 DOI: 10.1126/science.1227833

•Report•Flows of Research Manuscripts Among

Scientific Journals Reveal Hidden Submission Patterns

. V Calcagno et al

Fig. 1 The network of scientific journals as derived from manuscript submission flows.

V Calcagno et al. Science 2012;338:1065-1069

Published by AAAS

Fig. 2 Journal impact structures resubmission patterns.

V Calcagno et al. Science 2012;338:1065-1069

Published by AAAS

Fig. 3 High-impact journals publish proportionally fewer first-intent articles.

V Calcagno et al. Science 2012;338:1065-1069

Published by AAAS

Fig. 4 Submission history affects citation counts.

V Calcagno et al. Science 2012;338:1065-1069

Published by AAAS

2 .What to publish?

• Projects on the way out• Projects in the working• Future projects to discuss and prioritize

A. Projects finalized and on the way out:

• MTA & Aging (Keren)• PRIME (Keren)• TOX (Allon)• SUMEX (Matt & Raphy) • Homeo (Noa & Allon)• NAFLD (Livnat)• Second FH project (Livnat)• Proliferation signatures in cancer and normal cells (Yedael).

B. Projects we are currently working on(order is random)

• Modeling SNP effects in metabolism - Alik and Keren.• Studying Breast cancer metabolism across multiple omics levels (Livnat).• A large-scale study predicting metabolic symptoms in diseases and metabolic

drugs side-effects – (Itay & Keren)• Minenv and growing unculturable organisms (Matt & Raphy); • Promiscuity and antibiotic resistance; (Matt & Raphy)• Studying statin effects and predicting other anti-cholesterol drugs (Osher &

Keren).• Gut microbiome & glycans project (Omer & Raphy).• SNP signatures and co-morbidity (Yedael)• Metabolism in AD (Shiri)• Metabolism in Epilepsy (Nir Gonen)• Identifying SL pairs as a key for selective treatment in cancer (Livnat & Adam)

Projects we are currently working on(in continuation)

• A new approach for integrating expression data in metabolic modeling (Adam)

• The mechanisms behind and the oncogenic role of inverted IDH flux in breast cancer (Livnat)

• Identification of `true' bacterial growth media (Oren)• Selective enhancement of ROS production in cancer (Erez)• Studying the Warburg effect across the NCI60 cell-lines (Keren)• Involvement of TLM genes (human orthologs) in diseases (Yedael)• Plant metabolism; better models of Arabidopsis and corn; improved

yield… (Yoav & Raphy)• Brain metabolic evolution (Gal Chechik and his students)• Functional alignment of metabolic spaces across species (Arnon &

Roded).

C. Projects on hold:• Tuvik's three layered network robustness project (?)..• Csaba's ROS / antibiotics project (Keren)• The plasma metabolic network project (awaiting

Helsinki approval)

D. Administrative tasks:• Lab code repository; • Microme deliverables (Alik & Ariel)

What next? The future around the corner

A. Generic computational challenges:

• • 

• Utilizing widespread inferred network activation data to orient (provide direction to) reactions in the human model (Keren, Appnedix D)

• Go thermodynamics and biomarkers (Elad, Yoav, Keren, Allon). • Extending MTA to over expression; (Keren)• Finding exchange media - inferring human physiol media at different tissues - critical for model

building – the antibiotics for sepsis bacteria - using it later for inferring biomarkers of disease from expression data; (Keren)

• Building tissue models – estimation tissue specific: a. objective functions (the BOSS algorithm), b. media, and c. bounds, orienting reactions (Keren)

• Simulating multi-tissue metabolism; validation via increased fit to proteomics and biomarkers data.. • Using GSMMs to constrain the hypotheses space of gene association testing – use imat to find best

fit of metab state to data; then rank genes in accordance with confidence interval’s• Utilizing stochiometric couplings to better interpret metabolomic data measurements (Appendix C).• Using essentiality data to infer the metabolic state (Livnat (?))• Enriching and solving metabolic models with integrated vitamin metabolism (with focus on humans,

of course).

B. General basic research questions & clinically-oriented applications

• Metabolic alterations and tissue salvage after stroke or myocardial infarction• Using TOX to predict the toxicity of activating drug candidates in proliferating cells (Allon).• Extending MTA to identify metabolic gene targets of drugs (Appendix B) – (Keren).• Antibodies against metabolic enzymes and autoimmune disorders (-For example, in multiple-

sclerosis, there is an autoimmune response that harms the fatty myelin that surrounds the neurons; Livnat/Matej)

• obesity – white/brown adipose tissue; MTA reversal• Different `good' and `bad' ways to slow metabolism… slower metab in aging, but animals with

slow metab have longer life spans; is increased BMR required to counteract greater errors in proteins etc?

• Use MTA for comprehensive drug repurposing for metabolic disorders.• Generating a databases of tissue-specific biomarkers that can point to tissues specifically afflicted

in a specific disease. • In silico evolution of existing organisms.. (?!) – take a few extant bacterial species, construct their

common ancestors back in the tree, and then use the latter to try and evolve back the extant ones?! Allow gene addition/deletion, learn about likely objective functions and metabolic environments.

• Identify alterations in the production of key metabolites that serve as signaling molecules… • Same re. the production of neurotransmitters – after identifying potential targets – examine their

down regulation in disease gene expression data and test/validate vs co-mborbidity data!• The relation between the extent of drugs' side-effects and the level in which they cause a

deviation from the healthy or disease tissues states…

C. Studying cancer metabolism

• MTA in cancer - reversing the Warburg effect (Keren). • The metab of the naked mole rat vs the mouse/rat – Church's 2011 plos one paper; • Estimate the fitness of adapted resistant cancers after anti cancer metab

treatment.. use these estimates to compute optimal treatment strategies for keeping cancer in check..

• Identify drugs targets that can selectively inhibit estrogen production in Breast cancer (Livnat)

• Studying the metab alterations behind emerging resistance in cancer cells -• Studying alterations in key metabolites modulating the main signaling pathways

considered to be central in cancer; including Proliferative signaling, Energy metabolism & Growth suppressors

• Inducing autophagy as treatment in cancer and in neurodegenration (the science review by Josh Rabinovitz).

• Metabolic signatures of advanced tumors• Metabolic factors that determine tissue targets of metastases (Livnat).

D. Lower priority potential projects/open-comments•:• 

• Mitochondrial disorders – (the recent NEJM review, Orly Alpeleg).• Systems biology of nutrition – answering basic questions on the relations between metabolic subsystems and more (See

Appendix A). • Restoring dopamine metabolism in Parkinson (see recent posting in ideas dir); app. SN cells are most sensitive to energy

shortage? • http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002444• Cancer proteomics - the idea that during cancer progression there may be a change in the composition and production of

essential amino acids; ideas re. the Savaguah rules (Appendix C).• The metabolic state in Progeria (?)• The merging of bacterial and archeon metabolism (question posed by Uri)?• Characterize cell-cycle metab behavior.• Metformin and the risk of cancer… - http://www.sciencemag.org/content/335/6064/28.full• The s.aures project that Allon and Ori have started.. Barbasi's strains models;..• Augmentation of certain anticancer treatments by NSAIDs.. • * The yeast/human gene complementarities project;• The 2010 MSB paper from the Church lab on a bacteria specializing on cellulose degradation. • The buffering/longevity idea (1.2012, Keren – what happened with it?)• The definitive imat..: The idea is to perform an iterative search for a threshold that maximizes the over all imat fitness score..

- A further related idea is to score reactions by their global effect on determining the activity of other reactions in the network given typical expression data..

• Survey of gene transplant in humans; Predict life improving genes in humans… (Allon)

3 .Online dating and the rewards of publishing

Dating in a digital world Scientific American Mind, September 2012