Digital Cells

Digital Cells

Foothill College

Nanotechnology

Image by John Alsop

Outline

• Concept of a gene (extended)• Gene Regulatory Networks (GRN)• GRN and cells as an information system• Creating molecular interaction maps• The goal and process of digital cells• Using e-cell / SBML to model a cell• Bio-nano-info convergence

Central Dogma in Biology

DNA (sequence, expression)

RNA (sequence, structure)

Protein (sequence, structure)

Protein feedback

Transcription controlTranscription

Translation

Concept of a Gene

• Why do we separate proteins from (DNA) in our definition of genes?

• One is seen as “distinct” from the other– As if they had separate lives

• Proteins are the tactical or “execution” side of a gene – the field of Proteomics

• Nucleotides are the strategic or “planning” side of a gene - Genomics

Fundamental Interactions

There are three ‘semi-distinct’ layers of process and information space inside a cell – connected through molecular networks

IonChannels

Receptors

TranscriptionFactors

Ligands

ELECTROPHYSIOLOGY

Extracellularspace

Cytoplasm

NucleusTranslation +

processing

cis sites

IntracellularSignaling

GeneticRegulatory

NetworkmRNA

The (Really) Big Picture

Proteins and Pathways

Cellular Operating System

• Genes are interchangeable parts, but must be ‘tuned’ for synchronization, collaboration, workflow, messaging, etc.

• They are ‘metabolic.dlls’ – part of a cellular operating system. They are the most basal autonomous code in the cellular OS.

• Protein services must also ‘boot’ with the OS, and regulate how OS interacts with the metabolome, and other signaling proteins.

Genomic Decision Networks

Simplified version of the phage decision network that determines whether an infected E. coli cell follows the lytic or lysogenic pathway. Dashed arrows indicate the direction of transcription, and bold arrows indicate regulatory interactions between a gene product and particular DNA region.

Oscillating Networks

• Need to think about oscillating reactions– (protein formation / life-time) inside a cell.

• Gene regulatory networks create inverters (digital inverter networks)

• Inverters create ‘joined’ oscillating reactions with a lag time– Timing from transcription to translation is

critical, as is the half-life of the protein

Strategy of Genes

• When?• Where?• How much?• Who with?• Gene circuits

– Regulatory / inhibition– Promoters– Co-expression

Mechanics of Transcription

Genes rely on several molecular signals and processes to manifest a solution, which is part of a larger decision network

Genes are just Solutions

• Successful molecular solutions involving aminoacyls required templates to execute– When, orchestrated (how), and how much – Executed in time, space, and abundance

• Genes today are complex solutions– Most “genes” code for complex proteins

• Entire genomes orchestrate a symphony– Organisms are autonomous collectives

Genetic Algorithms

Genetic Algorithms

Self-Assembled Algorithms

--------------------------- 1010110001011010ATGCCAGTACTGGTACGGTCATGACC0101001110100101---------------------------

Information vs. Processing

Just as in a computer, data bits and processing bits are made from the same material, 0 or 1, or A, T, C, G, or U in biology

Basic GRN Circuits

Gross anatomy of a minimal gene regulatory network (GRN) embedded in a regulatory network. A regulatory network can be viewed as a cellular input-output device. http://doegenomestolife.org/

http://doegenomestolife.org/

Gene regulatory networks ‘interface’ with cellular processes

Goal of Digital Cells

• Simulate a Gene Regulatory Network– Goal of e-cell, CellML, and SBML projects

• Test microarray data for biological model– Run expression data through GRN functions

• Create biological cells with new functions– Splice in promoters to control expression– Create oscillating networks using operons

Digital Cells

• Bio-logic gates

• Inverters, oscillators

• Creating genomic circuitry

• Promoters, operons and genes

• Multi-genic oscillating solutions

Digital Cells

http://www.ee.princeton.edu/people/Weiss.php

Digital Cell Circuit (1)

INVERSE LOGIC. A digital inverter that consists of a gene encoding the instructions for protein B and containing a region (P) to which protein A binds. When A is absent (left)—a situation representing the input bit 0—the gene is active. and B is formed—corresponding to an output bit 1. When A is produced (right)—making the input bit 1—it binds to P and blocks the action of the gene—preventing B from being formed and making the output bit 0. Weiss http://www.ee.princeton.edu/people/Weiss.php

Digital Cell Circuit (2)

In this biological AND gate, the input proteins X and Y bind to and deactivate different copies of the gene that encodes protein R. This protein, in turn, deactivates the gene for protein Z, the output protein. If X and Y are both present, making both input bits 1, then R is not built but Z is, making the output bit 1. In the absence of X or Y or both, at least one of the genes on the left actively builds R, which goes on to block the construction of Z, making the output bit 0. Weiss

http://www.ee.princeton.edu/people/Weiss.php

Gene Regulatory Network

Goals of Network ModellingMolecular interaction

networks

Molecular interaction networks

Molecular interaction networks

• Representation

• Analysis

• Communication

Different Network Types

• Gene regulation networks (gene networks)– Describing transcriptional relationchips

• Biochemical networks– Describing interaction between proteins, enzymes

and other participants in cellular functions– e.g. cell cycel regulation and signal transduction

• Metabolic networks– Describing interactions of metabolites

Advantages of Graphical Representation

• Graphical representation of biochemical networks is two dimensional

• Therefore greater flexibility in describing biochemical networks than in verbal description– e.g. imagine, describing a street-map

ERKERK

RasPDK-1

ERK

ERK

ERK

RSK

RSK

RSK

RSK RSK

CREB

c-Myc

c-Myc

Raf

Ras

Raf

Raf

MEK

MEK

ERK

ERK

CREB

P

P

P

P

*

*

P

P

P

P

P

P

P

P

PPP

PPP

PP

P

Process Diagram

Diagram Proposal by A.Funashi & H.Kitano

Process Diagram

• Is essentially a state transition diagram – like in engineering or software developing

• Following states can be represented:– phosphorylation– acetylation– ubiquitination– allosteric change

• Increasing need to use these diagrams to extract gene regulatory relationships to overlay with gene expression micro-array data

Notation of the Process Diagram

A

A

State transition – changes the state of modification rather than activation

Activation

Inhibition

Translocation of module

Dashes line indicates active state of a molecule

Specific state of molecular species

Gene Regulatory Networks

• Post transcriptional interactions should be invisible

• Only gene regulatory network shall be extracted

activation or inhibition (instead of state transition

& indicates ‘AND’ - relationship

Molecular Interaction Maps (M.Aladjem, K.Kohn)

• Features:– MIM depict biochemical components of bioregulatory networks

in a standard graphical notation (like “wiring diagrams” in electronics)

– More detailed and explicit than commonly used graphical representations

– Unambiguous– Ability to view all interactions a molecule can be involved– Depicts competing interactions as well– Ready access to annotations– Retrieval of further information from external resources– Represents consequences of interactions (e.g. enzyme modifies

another enzyme)• Allows tracing of pathways within the network• Increases the utility of MIMs as aids to computer simulation

Molecular Interaction Maps (MIM)

• Characteristics:– Each molecule shown only in one location

• All interactions and modifications can be traced from one point

• Molecules can be located from an index of map coordinates

– In “Cell Cycle eMIMs” (interactive MIMs) molecules serve as links to additional sources of information (PubMed, Gene Cards, MedMiner)

A B

A B

C

Ph’tase

A

A

X

Y

Protein A and B can bind to each otherThe node represents the A:B complex

Multimolecular complex: x is A:B; y is (A:B):CEndless extendable

Reactions:

P

P

A B

Covalent modification of protein A. A can exist in a phosphorylated state.

Cleavage of a covalent bond: dephosphorylation of A by a phosphatase.

Stoichiometric conversion of A to B.

Symbols / Conventions used in eMIMs

Symbols / Conventions used in eMIMs

A

A

Reactions:

Cytosol Nucleus

Contingencies:

Transport of A from cytosol to nucleus. The dot represents A after transport to the nucleus.

Formation of homodimer. Dot on the right represents copy of A. Dot on line represents the homodimer A:A

Enzymatic stimulation of a reaction

Enzymatic of a reaction in trans.

Stimulation of a process. Bar indicates necessity.

Inhibition

Transcriptional activation

Transcriptional inhibition

Molecular Interaction Map (eMIM)

KEGG

• KEGG – Kyoto Encyclopedia of Genes and Genomes• From a SWISS-PROT entry find the EC number for

COMT (EC: 2.1.1.6 - but this doesn’t link into KEGG)• Search H.sapiens database using DBGET (KEGG)

• Catechol O-methyltransferase, membrane-bound form (EC 2.1.1.6) (MB-COMT)

• Metabolism; Amino Acid Metabolism; Tyrosine metabolism [PATH:hsa00350]

• In the pathway maps (see next slide) click on the EC number or the substrate image for details.

Pathway Diagram in KEGG

Reliable Microarray

Measurements

PredictiveModels

Model Validation

Experiments

HypothesisBiology

Engineering

Delaware Biotech Institute

Microarrays And Models

Pathway Kinetics

BioSPICE – Open Source

http://biospice.lbl.gov/

BioCyc

• BioCyc Knowledge Library• The EcoCyc and MetaCyc databases are

highly curated databases whose content is derived principally from the biomedical literature

• PathoLogic - Computationally-Derived BioCyc Databases– The majority of databases in the BioCyc collection

were created by a program called PathoLogic

E-Cell

• E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells. The version 3 allows many components driven by multiple algorithms with different timescales to coexist

CellML

CellML.org The CellMLTM language is an XML-based markup language being developed by Physiome Sciences Inc. in Princeton, New Jersey, in conjunction with the Bioengineering Institute at the University of Auckland and affiliated research groups.

The purpose of CellML is to store and exchange computer-based biological models. CellML allows scientists to share models even if they are using different model-building software. It also enables them to reuse components from one model in another, thus accelerating model building.

CellML<model name="bi_egf_pathway_1999" cmeta:id="bi_egf_pathway_1999" xmlns="http://www.cellml.org/cellml/1.0#" xmlns:cellml="http://www.cellml.org/cellml/1.0#" xmlns:cmeta="http://www.cellml.org/metadata/1.0#" xmlns:mathml="http://www.w3.org/1998/Math/MathML">

<rdf:Description rdf:about=""><!-- The Human Readable Name metadata. --><dc:title>Epidermal growth factor stimulation of mitogen-associated protein kinase and activation of Ras</dc:title>

SBML

• Is one effort for machine readable representation of “MIN”

• SBML is an XML based modelling language that represents biochemical networks

• It enables exchange of biochemical network models between software-apps (e.g. CellDesigner)

http://sbml.org

Bio-Nano-Info

• Looking at bio through the eyes of nano– Physical properties of small systems

• Looking at nano through the eyes of bio– Self-assembly of nano-structures

• Interaction of information and molecules– Molecular assemblies as information and

operating systems - nano execution of IT

• The universe’s nanoscale properties affect the processing of three attributes – Energy– Mass– Information

• Biology leverages these to produce a cellular operating system, metabolism, and complex self-assembled structures

Three Dimensions of Nano

Self Assembly

• Follows statistical thermodynamics

• Seen in molecular monolayers

• Building process for viral caspids

• Use nature to guide manufacturing– Control and guide novel structures

Molecular Self Assembly

Figure1: 3D diagram of a lipid bilayer membrane - water molecules not represented for clarity

http://www.shu.ac.uk/schools/research/mri/model/micelles/micelles.htm

Figure 2: Different lipid model -top : multi-particles lipid molecule-bottom: single-particle lipid molecule

Viral Self-Assembly

http://www.virology.net/Big_Virology/BVunassignplant.html

Bio-Nano Convergence

Summary

• Cell as an information system

• Genome as a decision network

• Pathways and process diagrams

• Digital cells - insilico biology

• Bio-nano-info convergence– Biology as an ‘instance’ of nanotechnology– Nature as an information (processing) system

References

• http://www.ee.princeton.edu/people/Weiss.php• http://www.dbi.udel.edu/ • http://biospice.lbl.gov/ • http://www.systems-biology.org/ • http://www.e-cell.org/• http://sbml.org/ • http://biocyc.org/• http://www.sbi.uni-rostock.de/teaching/research/ • http://www.ipt.arc.nasa.gov/