introduction of Quantitative biology

introduction of Quantitative biology

https://www.ibiology.org/professional-development/quantitative-biology/

Lodon cholera problem.

Tony Hui, UCSD

Schaechter, J gen Microbio 1958, faction of proteins (R-proteins) ~ growth rate

Bacterial growth laws



Three modes of growth limitation:

C-limitation -| catabolic section
A-lim  --| anabolic section
R-lim  ---| polymerization section

Mass spec -> changes of groups of proteins

Proteome partition model
 Scott, Science 2010,

  You, Nature 2013

6 Coarse grained proteome sections.

Spencer, qbio, prolieferation-quiscence decision

Spencer, Tobias Meyer, Cell, in press

Live-cell sensor for CDK2 activity,


G1/G0,
CDK2-Venus green in nucleus

S, G2/M
CDK2-Venus in cytosol

p21 levels are different in CDK2-inc and CDD2-low cells.
  p21---| CDK2


CDK2-low is in transient G0 state.

Benjamin Machta, Princeton, information fow in plasma membrane.

Membrane is nearly critical.

Phase transition in synthetic lipid membran
 2nd order critical point

Allosteric regulation

General anesthesia -> membrane involvement? However, proteins are unquestionably involved.

Lippincott-Schwartz, Imaging

Photoactivable fluorescent proteins

Patternson, Lippincott-Schwartz 2002, Science.

Fitting a 2D Gaussian centroid to photoactivated signal to find the source--> better resolution.

This method can be applied to photobleaching and improves resolution of conventional fluorescence imaging.

Assaf Rotem, Harvard, drop-based microfluidics of single-cell assay

Chewing on biology, one bit at a time -- high throughput assays at single-cell resolution using Drop-Based Microfluidics reveal novel variations in heterogeneous populations, Assaf Rotem, Harvard University

 CHIP-Seq for every cell

Rob De Boer, Utrecht University

Heterogeneous Differentiation Patterns of Individual CD8+ T Cells,
Rob De Boer, Utrecht University
http://theory.bio.uu.nl/rdb/
http://theory.bio.uu.nl/rdb/publications.html


Bar coding of cells using retroviral library. Deep sequencing to find distribution of cells.

50% of total immune responses derived from 5% of the cells

50% of the families contain less than 200 daughter cells.
Disparity in family sizes is established early.

Zaida Luthey-Schulten, stochastic simulations of cellular processes

UIUC, center of physics of living cells.

Noise contributions in an inducible genetic switch: A whole cell simulation study,

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002010

http://q-bio.org/w/images/d/d2/Talk_ZanLutheySchulten.pdf

Choi, Cai, Xie, 2008, Science, stochastic single molecular event triggers phenotypic switching of a bacterial cell.
http://www.ncbi.nlm.nih.gov/pubmed/18927393

Tang, Earnest, Wang, Xie, zhung, Science 2011, nucleoid packing

Wolfgang Baumeister, Elizabeth Villa, yeast nuclear structure.

ZLS emphasizes that crowding makes a difference.

Taniguichi, ... Xie, 2010, Science
Labhsetwar, Cole, ZLS, PNAS in press

BRENDA Database, www.brenda-enzymes.org/

Comparative analysis of metabolic robustness: E coli and Synechocytis Chintan Joshi, Ashok Prasad

Comparative analysis of metabolic robustness: E coli and Synechocytis
Chintan Joshi, Ashok Prasad


Power-laws leads to less robust systems.

http://q-bio.org/w/images/b/bc/Talk_Prasad_Ashok.pdf

Ali Tabei, 2013, PNAS

Ali Tabei, 2013, PNAS

random walks

glass properties,

S. M. A. Tabei, S. Burov, H. Y. Kim, A. Kuznetsov, T. Huynh, J.

Jureller, L. H. Philipson, A. R. Dinner, and N. F. Scherer, Proc.

Natl.

Acad. Sci. USA 110, 4911 (20

Mary Teruel, Stanford, Conrolling size of tissue with stochastic noises

Mary Teruel, Stanford, Conrolling size of tissue with stochastic noises

Spalinding 2008 nature, fat cells renew at 10% per year.

PPARG -> adipogenic factors --> change of preadipocytes to adipcytes.

Park et al, Cell Rep 2012, conversion of preadipocyte to adipocyte via a bistable switch. (All or none process)
[PPARgamma] ~ [C/EBPbeta] plot to show bi-modal distribution of two cell populations.

[PPARG] ~ Rosiglitzaone(uM) plot show hysteresis, in support of bistable switch and positive feedback.

Increased noises would lead to more switches, this can be easily shown by simulation.

Q: how to increase noises experimentally?

More noises lead to stronger de-differentiation.

Too little noise, hard to switch. Too much noise, cannot hold the states.

One-feedback loop model:
 R ->    PPARG(x0)    --> Terminal
         |->CEBPA(x1)-^
Two ODE equations.

Increased cooperativity helps, but does not solve the optimzation problem.

Multi-loop systems can solve the optimization problem in theory. How to prove this in experiments?

MT uses a triple-quadropole mass spectrometer, aiming to identify feedback loops.  This method requires a unique peptide sequence. Time course data were obtained. 7 feedback loop were concluded.

Fat cells -> nuclei -> proteins ---(typsin)---> peptides ---> mass spec

References:
http://q-bio.org/w/images/c/c3/Talk_TeruelMary.pdf

Alex Lang, Boston Univ, epigenetic landscapes

Alex Lang, Boston University (4th graduate student)

Epigenetic landscapes provide insight into cellular programming,

Takahashi, Yamanaka 2006, converted epithelia to stem cells.  Terminal cells can be converted to another terminal cell types without going through the 'stem' cell stages. 

Waddington landscape 1957, thinks cell types as dynamic attractors.  

Lang argued that Waddinton landscape is a emergent property of underling interaction networks. 

Lang: Cell type = high dimenstional Ising spin vector. 

Landscape construction: 

Each cell is a vector. Each cell is a basin of attraction. 

Project method neural network is a method to take vector and construct landscape. 

( Hopfiled PNAS 1982? )

Attractors due to correlation-based effective interaction matrix.

High demensions often give spurious attractors, an unavoidable for frustrated systems.
Lang claimed that partially reprogrammed cells are the spurious attractors.  
Lang developed a project method to match vectors to cell types.

Reprogramming facts: only final cell types matters, only one set of TFs in time.

URL: 

http://q-bio.org/w/images/8/89/Talk_LangAlex.pdf

Q-bio, Nancy Albritton, UNC, Chemistry

Nancy Albritton, UNC, Chemistry

Fluorescent substrates,

detection limits, 10^-21 moles

Sphigosine-FAM

Bodipy-PIP2,C4

Kinase/Protease substrates

Fast single-cell analyses

Microfluidic device, lyse cells by laser.

Sphigosine kinase:  Single breast cancer cells, Red blood cells

Subpopulations of clinic samples can be detected. Heterogeneity in cancer cells were emphasized. 

Fluorescent PIP2 reporter libraries.
FAM-PIP2
metabolism of PI3K
PIP2 --(PI3K) --> PIP3 ---(PTEN)-->PIP2 ... ...

Challenges: standardization between samples. Problems of cells lines.

URLs: 

http://www.chem.unc.edu/people/faculty/allbritton/
PubMed

Q-bio, Ilya Nemenman, tutorial on information processing in biological systems

Information processing in biological systems, Ilya Nemenman

IN use probabilistic model for information processing.

w --> P(r|w) --> r --
^----------------------|

Information is not context free.

IN uses variability S[w] in his model. 

$S$ seems to stand for entropy. 

With probabilistic assumptions, IN can write down models explicitly. 

References:
http://www.menem.com/~ilya/wiki/index.php/Stochastic_dynamics_on_biological_networks