# Presentation: 453792

## Motifs, Modules, and Games in Stressed Out Bacteria

**Dynamics/Design?**
- How does the network act as environmental sensor, signal processor, computer, and factory? What design features are responsible for phenotype switching and non-genetic diversity? How can we control this network?
**Topology?**
- What is the structure of the ubernetwork? Is it modular?  If so, in what sense? Evolutionary vs. topological vs. dynamic modularity?
**Evolution?**
- How did these networks evolve? What parts of the network are conserved in other species with identical phenotypes? What parts are different?  Why?
**The ‘why’s’ of behavior**
- Why are stress responses controlled as they are? Why non-genetic diversity?

** ****Denise M. Wolf****1****, Amoolya Singh****4****, Jay Keasling****1,5****, and Adam P. Arkin****1,2,3**
- 1Physical Biosciences Division, Lawrence Berkeley National Lab; 2Howard Hughes Medical Institute
- 3Bioengineering,  4Computer Science, and 5Chemical Engineering Departments, University of California – Berkeley,

**Who wins?**

**Project Goals**

**Towards modular biology?**

**Switching and non-genetic diversity in sporulating cultures**

**Diversification strategies as game**
**and mutual fund**

***B. subtilis*****:**** Stress ubernetwork dynamics; graph theoretic analysis; quantification of  environment to diversification map; cross-species microarray data analysis; games.**
**DOE GTL species:**** apply testbed tools and concepts to analyzing for network dynamics, organization, and evolution on global scale.  Optimize bioremediation.**** **

**Motifs, Modules, and Games in ****Stressed Out Bacteria**

- With missing or faulty environmental sensors (unobservable environmental transitions), a stochastically *time varying *environment selects for randomly phase varying phenotype expression, *if* different environmental states select for different cell states.
- If a population’s sensors can detect environmental transitions, but have poor precision in identifying new environments, the ESS strategy is to probabilistically diversify the population into different cell-state subpopulations upon entry into a new environment, a *sensor-based, mixed* strategy that looks very much like heterogeneous stress response deployment in *B. subtilis*. .

**The game**

**Future work**

**Regulatory themes**

**Our approach**

- .

**Evolutionary modules?**

- Evolutionary game theory can help explore why cells behave as they do and elucidate the design principles of the regulatory circuits controlling cellular behaviors. In this project we investigate diversification strategies among microbes.  Our formulation poses cellular behavior as an evolutionarily stable strategy (ESS) in a game pitting cell against cell and cell against nature.

**Hypotheses**
- Sequence features conserved across niches = core function.
- Sequence features conserved within niche but not across niche are necessary for adaptation to that niche.
- Features not even conserved within a niche are there for competition, commensalisms or are spandrels.

**Motifs**

*(See Pathway Evolution poster for details)*

**Agonist/antagonist operon pairs**
**(& KipIA,Rap/Phr,Soj/Spo0J)**

**  ****Biphasic motif**

**Cascade w/feedback**
**(phosphorelay, MAPK)**

*An emerging paradigm?**  Motifs function in modules designed by evolution to play *
* **games of survival pitting cell against cell and cell against nature.*

-                         Wolf DM, Arkin AP. Motifs, modules and games in bacteria. *Curr. Opin. Microbiol*. 2003