# Presentation: 852162

## Other models to understand the cancer cell metabolic flux 

**Zoltan N. Oltvai**
- Department of Pathology
- University of Pittsburgh
- School of Medicine

## Metabolomics in diseases, such as cancer

- Disease: reorganization of physiological states on the cell, organ, and organism level

- Normal state:  ACB active
- Disease state: ADB active
- Consequence:
- Reorganized cell metabolism
- Appearance of signal ‘D’

**Notes:**
Diseases, such as cancer, can be viewed as a reorganization of physiological states on the cell, organ, and organism level;
For example. let’s assume that in a given cell type, in a healthy individual, substrate S is utilized through the ACB pathway

During the development of the disease state (e.g., premalignant to malignant cancer) there are various intracellular events taking place that result in a switch from the ACB to the ADB pathway.

This will have two important consequences:
The reorganization of the metabolism of the diseased cell
The appearance of a byproduct of the new pathway’s activity, which acts as a signal ‘D’ that can be detected as a marker and that may contribute to the initiation of alterations in the adjacent cells.

## The promise of cancer metabolomics

- Omics platform to understand pathophysiology
- Biomarkers: normal – precancerous – malignant
- Drug intervention points

## 
Reconstruction of human metabolic networks
			human genome and other ‘omics’ projects

Understanding their system-level organization
			systems biology of model organisms

- Reconstruction of human metabolic networks
- 			human genome and other ‘omics’ projects
- Understanding their system-level organization
- 			systems biology of model organisms

- Prerequisites of these goals

##     Physicochemical constraints in system-level metabolic organization: equilibrium of mass  (Flux balance analysis)

-     Physicochemical constraints in system-level metabolic organization: equilibrium of mass  (Flux balance analysis)

- Schilling & Palsson, P.N.A.S., 1998

##    Restriction of solution space by defining the desired output 	substrates and by optimizing for maximal growth

-    Restriction of solution space by defining the desired output 	substrates and by optimizing for maximal growth

##  Global flux organization in the Esherichia coli metabolic network

- Almaas et al, Nature, 2004

## The dominant in- and outgoing reaction fluxes define a high flux backbone (HFB) of E. coli metabolism

- The dominant in- and outgoing reaction fluxes define a high flux backbone (HFB) of *E. coli* metabolism

- Glutamate-rich growth media

- Succinate-rich growth media

## Type I (ON/ OFF) and Type II (variably ON) flux changes upon environmental changes predominantly affects the HFB 

## The number of always active metabolic reactions under optimal growth conditions

- Almaas et al, PLOS Comp. Bio., in press

## The metabolic core of E. coli

- The metabolic core of *E. coli*
- _Enzymes:_
- Red: essential   green: dispensable

## Correlation of flux rates within and outside of the E. coli metabolic core

- Red: essential enzymes
- green: core reactions

## Individual reactions with high flux display mono- or multi-modal flux distribution under different growth conditions

## The E. coli transcriptional regulatory network

## The specificity of signal recognition and the hierarchy of information flow define TR subnetworks (origons)

**Layer 0**

**Layer 1**

**Layer 2**

**Layer 3**

**Layer 4**

- Balazsi et al., P.N.A.S., 2005

## Generic topology of origons

- Regulators, e.g. metabolites, oxygen

**1**

**2**

**0**

**3**

##   Three-node subgraphs in origons of E. coli

| Subgraph | DIV | CNV | CAS | FFL |
| --- | --- | --- | --- | --- |
| Abundance in real network | 4777 | 227 | 160 | 42 |
| Abundance in randomized network | 4339.3 ± 132.0 | 231.92 ±  8.05 | 186.69 ± 7.08 | 9.50 ± 
4.17 |
| Z-score | 3.3149 | 0.6114
 | 3.7682 | 7.7909 |

## Types of origons and their topological relationship in the E. coli TR network

- Black circle: simple tree, Red circle: tree/FFL origons; green links: % shared nodes
**	****Convergence (CNV) subgraph – NEVER within origon!**

## Testing the origon concept with microarray data 

- E. coli microarray data were downloaded from: *https://asap.ahabs.wisc.edu/annotation/php/ASAP1.htm* *http://www.ou.edu/microarray/*
- The following data sets are available:
- 12+48=50 cDNA experiments
- 5+36=41 Affymetrix experiments
**3+38=41 aerobic-anaerobic shift experiments**
- 104 experiments in various conditions from Oklahoma University

##    Definition of node-signal covariance

## Distribution of covNS within origons

## Definition of node-node correlation

## Binary prediction of node-node correlations

## The quality of corNN predictions

**0.0377**

**0.0454**

**0.0693**

**0.3049**

**Predicted**

**Measured**

**Pred x Meas**

**fnr origon,**
**non‐ specific**
**stimulus**

**crp origon,**
**fnr‐specific stimulus**

**crp origon,**
**non‐specific**
**stimulus**

**fnr origon,**
**fnr‐specific stimulus**

**<Pred x Meas>**

## Topological relationship of origons in the TR network of S. cerevisiae

- Node size: ~ log # of genes in the origon
- Large differences between origon sizes
- Strong overlaps

- Data:  Harbison et al, Nature, 2004

## Biochemical steps of signal processing by a single-regulatory interaction (SRI)

**P****X**

**Notes:**
The signal activates the input TF, Px. Once activated, Px undergoes allosteric conformation change and binds to the operator region of gene Y and allows binding of RNA polymerase, Pr, to its promoter region. The polymerase-operator complex initiates transcription, resulting in the synthesis of an mRNA molecule Ry after a delay, which, bound by a ribosome, is translated into the output protein, Py, after a delay. 

## Mass-action kinetic modeling of the signal filtering properties of the SRI, FFL and CAS subgraphs

- Balazsi et al., P.N.A.S., 2005

**Notes:**
Time courses of output protein levels after a periodic perturbation for SRIs and DIVs (black line), CASs (blue line), and FFLs (red line). The  amplitude of fluctuations at the output of the FFL is nearly the same as the output of the SRI, although it is substantially reduced because of the stronger filtering properties of CAS.  

Frequency response of SRI and DIV (black circles), CAS (blue squares), and FFL (red triangles) showing the amplitude of flucturations at the output of subgraphs (vertical axis) versus the frequency of th signal applied to their input (horizontal axis).  All subgraphs are low-pass filters, but CAS has a much stronger filtering effect than either the SRI (DIV) or FFL. 

Needs experimental confirmation 

## Recognition and processing of complex environmental signals in TR networks

- Decomposition to elementary signals by signal-spec. recognition
- Parallel processing
- Integration of response (through CNV subgraphs)

- Balazsi & Oltvai, Science STKE, 2005

## X

- Y

- Z

- Sx

- Sy

- CAS

- aggregated FFL

- Y

- Z

- Sx

- Sy

- FFL

- CAS and FFL integrates related signals within origons (e.g., glucose OFF, maltose ON), but FFL eliminates the filtering effect of CAS, allowing rapid response to signal change

## Acknowledgements

- Laszlo Barabasi  (U. Notre Dame)
*Metabolic network utilization*
- 	Eivind Almaas (U. Notre Dame)
*Transcriptional regulatory network utilization*
- 	Gabor Balazsi (Northwestern U.)
- U.S. Department of Energy,
- National Institute of Health (NIGMS)