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 control Transcription 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

The (Really) Big Picture Ion Channels Receptors Transcription Factors Ligands ELECTROPHYSIOLOGY Extracellular space Cytoplasm Nucleus Translation + processing cis sites Intracellular Signaling Genetic Regulatory Network mRNA

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

Self-Assembled Algorithms --------------------------- 1010110001011010 ATGCCAGTACTGG TACGGTCATGACC 0101001110100101 ---------------------------

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

Goals of Network Modelling Representation Analysis Communication Molecular interaction networks Molecular interaction networks Molecular interaction networks

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

Diagram Proposal by A.Funashi & H.Kitano ERK ERK Ras PDK-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 P P P P P P P P P Process Diagram

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 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 A

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)

Symbols / Conventions used in eMIMs A B A B C Ph’tase A A X Y Protein A and B can bind to each other The node represents the A:B complex Multimolecular complex: x is A:B; y is (A:B):C Endless 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 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.

Microarrays And Models Reliable Microarray Measurements Predictive Models Model Validation Experiments Hypothesis Biology Engineering Delaware Biotech Institute

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 CellML TM 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 ="">  < 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

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/

Digital Cells

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Digital Cells