# Presentation: 157384

## Analysis and engineering of cellular circuitry

- Courtesy of IBM

- From: Wasserman Lab, Loyola

- _Asynchronous Digital Telephone Switching Circuit_
- Full knowledge of parts list
- Full knowledge of  “device physics”
- Full knowledge of  interactions
**No one fully understands how this circuit works!!**
- Its just too complicated.
- Designed and prototyped on a computer (SPICE analysis)
- Experimental implementation fault tested on computer

- _Asynchronous Analog Biological Switching Circuit_
- Partial knowledge of parts list
- Partial knowledge of  “device physics”
- Partial knowledge of  interactions
**No one fully understands how this circuit works!!**
- Its just too complicated.
- We ***need*** a SPICE-like analysis for biological systems

**Notes:**
On the left is a telephone switching circuit from IBM circa 1994. On the right is the lamp-brush chromosome isolated from a frog cell during oogenesis. The point of this is that these are both asychronous switching circuits (The blebbed out regions around the bright central core of the chromosomes are regions that are switched on.) Engineers need spice tools for the left circuit. Biologists are in more need of “Spice” tools because of the more complex nature of the circuitry.  

## A foundation for cell network analysis

- Compile a list of parts that
-  make up your system.

- Group subsets of parts based
- on function, physics and
- interactions

- Determine “device physics”
- for the parts and their
- interactions.

- Create databases of the
- above and the “circuits”
- that are to be considered
- for analysis

- Create quantitative
- simulations and analyses of
- the dynamics of
- complex networks.

**Notes:**
These are the tasks that both biologists and engineers have in common. 

## The challenge of understanding cell function

**Notes:**
This images are of a sporulating bacterium of industrial and biowarfare interest. (This particular strain makes laundry detergent and antibiotics). The diagram bellow is approximately 1/3 of the genetic circuitry it uses to make the decision whether to sporulate or do other things. (Under stress  only about 66% of the bacteria sporulate, the rest variously take in DNA, dividide, become motile, and other things). The stack to the left is a hierarchy of levels at which we wish to model the organism and at which data is available. Our goal is to build from the bottom up using formal abstractions to get to the top of the stack.

The point is-- this is too complicate to understand qualitatively. You need a “Spice like tool”, models and attendant databases to approach this problem.

## Where are the data on these processes?

** ****mRNA expression data**

**Protein expression**

**Macromolecular Structure data**

**Molecular interaction data**

**Molecular concentration data**

**Temporal concentration data**

**Spatiotemporal imaging data**

**Mutation data**

**Kinetic/mechanistic data**

**Gross Phenotypic data**

** ****Gene lengths/organization**

- Data are:
- 1) Qualitative-->Quantitative
- 2) Collected at many levels
- 3) Of heterogeneous structure
- 4) Of heterogeneous availability
- Challenge:
- Optimal use of available data to make predictions about cell function and failure.

**Notes:**
This is just a list of the data we have access to.

## Tools for “multilevel” analysis

- Finding Parts

- Physical properties

- Cellular networks

**Notes:**
The wall of computation. Computational tools have been applied to each of the areas on the wall. The purpleness of the box is related to how developed tools are in that field. Bio/Spice addresses the light purple in the upper third of the box. Computation is cheaper, faster and less reliable than experiment. We’re working to fix that last part.

## SPICE: Simulation Program for Integrated Circuit Evaluation

- Parts
- database

- From
- subcircuit
- database

- Integrated
- circuit
- database

- Automated
- fault
- diagnosis

**Notes:**
A simple example of theSPICE simulation tool.

## BIO/SPICE: Databasing, simulation and analysis

***Bio/Spice: ***A Web-Servable, Biologist-Friendly,  database, analysis and simulation interface was developed into a true beta product.
- Interfaces to ReactDB, MechDB, and ParamDB.
- With Kernel, performs basic:
- flux-balance analysis,
- stochastic and deterministic kinetics,
- Scientific Visualization of results.
- Notebook/Kernel design optimized for distributed computing.

**Notes:**
Introduction to Bio/Spice

This is the network cartoon tool. This allows biologists to rapidly cartoon their networks or bring them up from the data base (this is part of the sporulation circuit shown on the previous slide). These cartoons may be associated with data and mathematical models. These are combined into a mathematical description of model dynamics that may be analyzed by an engineering, symbolic analysis kernels or numerical kernels. 

The tools allow hypothesis formation and checking.

## Components of Bio/Spice

- Local Kernel

- Local Data

- ReactDB

- MechDB

- Center Data

- Remote Kernel

- Remote Kernel

- Remote Kernel

- Center
- Tool
- Repository

- ParamDB

- BIO/SPICE

- Internet

- Internet

- Local

**Notes:**
Conceptual architecture of the Bio/Spice tool. Universal Java front end, special kernels and remote and local databases. Everything is sent through SSL to maintain some modicum of security.

## Kernel overview

**Update Object Parameters**
**Operator combinatorial logic**
**Statistical and thermodynamic parameters**
**Instantaneous enzyme kinetics**

**Mesoscopic Regime**
**Stochastic object update procedures**
**Cumulate discrete reaction events to project signal levels**

**Macroscopic Regime**
**Deterministic object update procedures**
**Classical integration routines to project signal levels**

**Master Simulation Controller**
**Dynamically selects object level update procedures**
**Schedules object updates**
**Maintains global parameter and state variable database**
**Manages parameter and data I/O**

**Modeling procedure dynamically selected by master controller based on current  concentrations and other model parameters**

**Model Object-Level Chemistry**
**Model events internal to each object independently**
**Report changes in object’s output signal levels**

**Inputs**
**Network definition**
**User-defined new objects**
**Kinetic parameters**

**Notes:**
This is version 0.01 of the kernel...it has now been  extended to deal with hybrid stochastic and deterministic systems and is now being rewritten to deal with transit between these two regimes. With the Titanium team in Computer Science and Phil Coella at the National Lab we are embarking on an ambitious rewrite of the kernel to allow hybrid, multiresolution modeling of the entire cell.

## Bio/Spice data structure overview

- World

- Compartment

- Pathway

- Reactions

- Species

- Mechanisms

**Notes:**
A very conceptual overview of the database.

## Bio/Spice Simulation Objects

- Bio-Object1

- Bio-Object2

- Bio-Objects accept inputs in two stages:
- 	1) resolution
- 	2) parameterization.

- Resolution determines if the output from Bio-Object 1 is of a valid type.
- If it is, and there are more than one input of that type, then the user is prompted for which input they mean.
- Parameterization determines how the equations of **both** Bio-Object1 and Bio-Object2 are affected.
- The user is prompted to enter the physical data (e.g., kinetic constants) for the interaction or to look them up in a database

**Notes:**
The open architecture for pluggable biomodels requires careful design of the API and brings some modeling problems to the fore. Connecting object 1 to object 2 means testing to make sure such a connection is legal and, if so, which exact role this connection plays in the model. However, this type of process requires that the model of 1 not be dependent on the model hidden in 2. This is not always the case.

## Example of linking

- User is modeling a competitive inhibition reaction. Thus, the user creates a ***Reaction*** Bio-Object, and
- four ***Species*** Bio-Objects:

- E	ES	E+P
- EI

- S

- I

- v= kcat Etot KI[S]/(KSKI+KS[I]+KI[S])

- Linking of S to NCI-1 results in a prompt for whether or not the species
- object named “M1” plays the role of the internal variable, E, S, I or P.
- If user chooses ‘S’, then the program prompts for KS.
- If user chooses ‘E’ then the object prompts to user to enter or select all objects in
- the model that have an ‘M1’ molecule or complex within them.
- Currently, complex equilibria are resolved using mass action kinetics.

**Notes:**
For example, when trying to model the reaction in the upper right with the rate law shown, a number of assumption need to be made: 1) rapid equilibria of I and S with E, 2) The E exists only in free and EI, ES states. Thus is someone specifies that one of the species is the enzyme for the reaction, that species may no longer be used legally in any other reaction if assumption 2 is to be true.

We allow the connection to be made but an assumption checker throws a warning.

## Building models with Bio/Spice

- Towards a Bullet-Proof, User-Friendly G.U.I.

## Using Bio/Spice to analyze phage : Starting your notebook

- Using Bio/Spice to analyze phage : Starting your notebook

## Using Bio/Spice to analyze phage : Adding infrastructure 

## Using Bio/Spice to analyze phage : Adding the parts 

## Using Bio/Spice to analyze phage : Linking parts together

## Using Bio/Spice to analyze phage : Entering data 

## Lambda lysis/lysogeny decision circuit

- This diagram and its underlying data was derived from compiling more than 45 years of classical genetics and biochemistry on this organism from over 100 papers (and a few good lambdologists).

## Individuals at MOI=9

- Individuals *do not* track population average
- Race condition between PL and PR biases production of CII and CIII
- Coincidental production of CII and CIII leads to stable CII-CIII complex
- CII-CIII activates PRE

## Time-dependence of the probability of commitment 

- T=0 min

- T=17 min

- T=35 min

- The probability that an *E. coli* cell infected with 6  phage contains a particular pair of CI2 and Cro2 concentrations (nM) at time T after infection.
- The color of the surface indicates degree of commitment to lysis (cyan) or lysogeny (red) by cells that achieve those concentrations. (For example, cells with high CI dimer and low Cro dimer concentration tend towards lysogeny.)

- Time-dependence of the probability of commitment

## Time to Effectivity

**Dosage=4**

**Dimer Concentration (nanomolar)**

**Time (minutes)**

**Timing uncertainty reduced by:**
** ****Higher gene dosage**
** ****Strong promoter**
** ****Multiple promoters**
** ****Lower effectivity threshold**
** ****Slower cell growth**

## Signal Growth in Three Cells

**about**

**50 molecules**

**25 molecules**

**Monte Carlo simulation data**

**One gene**
**Growing cell, 45 minutes division time**
**Average ~60 seconds between transcripts**
**Average 10 proteins/transcript: **