Jonathan Karr karr@mssm.edu July 17, 2019 Join us at Mount Sinai! - - PowerPoint PPT Presentation

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Jonathan Karr karr@mssm.edu July 17, 2019 Join us at Mount Sinai! - - PowerPoint PPT Presentation

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Toward WC models for predicting cellular phenotypes Jonathan Karr karr@mssm.edu July 17, 2019 Join us at Mount Sinai! karr@mssm.edu KarrLab.org Acknowledgements Yassmine Chebaro Yin Hoon Chew Arthur Goldberg Zhouyang Lian Yosef Roth John

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Jonathan Karr

karr@mssm.edu

July 17, 2019

Toward WC models for predicting cellular phenotypes

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Join us at Mount Sinai! karr@mssm.edu KarrLab.org

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Zhouyang Lian Arthur Goldberg Yin Hoon Chew Yassmine Chebaro Bilal Shaikh Balazs Szigeti Yosef Roth

Acknowledgements

John Sekar

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Outline

Genotype to cellular phenotype

– What is a WC model? – Why do we need WC models? – Challenges & feasibility – Foundational principles and state of the art – Progress toward comprehensive models

Tips for modeling complex systems

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What is a WC model?

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Goals of WC modeling

Whole cell Dynamic Whole genome Stochastic Whole cell cycle Species-specific Mechanistic

AGTC

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Motivation

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Synthetic biology requires WC models

Tissue engineering Biosensors Biofactories

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Example: drug biosynthesis

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Example: drug biosynthesis

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Example: drug biosynthesis

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Example: drug biosynthesis

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Example: drug biosynthesis

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Example: drug biosynthesis

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Example: drug biosynthesis

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Precision medicine requires WC models

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Challenges

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Challenge: explain diverse chemistry

Metabolism FBA Signaling ODE, SSA Transcriptional regulation Logical

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Time Length

Replication Growth Transcription Metabolism

Challenge: explain multiple scales

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Challenge: capture chemical complexity

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Single-cell variation Microscopy Transcription RNA-seq Protein expression Mass-spec, Western blot

Challenge: heterogeneous data

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Challenge: incomplete data

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Feasibility

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Feasibility: Extensive data

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Feasibility: Rule-based modeling

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Feasibility: Multi-algorithm simulation

27 Uptake FBA Metabolism FBA Transcription Stochastic events Translation Stochastic events Replication Chemical kinetics

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WC modeling is becoming feasible

Genomic and biochemical data Pathway submodels Rule-based modeling Multi-algorithmic simulation

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Workflow

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Workflow

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  • E. coli
  • M. genitalium

Genome 4700 kb 580 kb Genes 4461 525 Size 2 μm × 0.5 μm 0.2-0.3 μm

  • 1. Focus on simple cells

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  • 2. Aggregate and integrate data

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  • 3. Model each process

Metabolism Signaling Transcriptional regulation

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Detail Scope

ODE Shuler, 1970’s FBA Palsson, 1990’s Boolean Bolouri, 2000’s Gillespie Luthey-Schulten, 2011 PDE

  • 3. Model each process

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  • 3. Model each process

Metabolism Species and reactions

ADP + Pi + 4 H+[p] ↓ ATP + H2O + 3 H+[c] ATPase 𝑤 = kcat ATPase [ADP] 𝐿𝑛 + [ADP] ATPase = 3 * AtpA 1 * AtpB 1 * AtpC 3 * AtpD 10 * AtpE 2 * AtpF 1 * AtpG 1 * AtpH

Catalysis Kinetics

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Uptake FBA Composition Metabolism FBA Composition Transcription Stochastic events Gene expression Translation Stochastic events Gene expression Replication Chemical kinetics DNA sequence

Submodels States

Mass, shape Metabolite, RNA, protein counts Mammalian host Transcript, polypeptide sequences DNA polymerization, proteins, modifications FtsZ ring

  • 4. Merge models into a single model

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1 s

Uptake Metabolism Transcription Translation Replication

Cell states Cell states

Uptake Metabolism Transcription Translation Replication

Cell states

Uptake Metabolism Transcription Translation Replication

  • 5. Co-simulate models

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  • 6. Verify model

Matches training data

Cell mass, volume Biomass composition RNA, protein expression, half- lives Superhelicity

Matches published data

Metabolite concentrations DNA-bound protein density Gene essentiality

Matches theory

Mass conservation Central dogma Cell theory Evolution

No obvious errors

Plot model predictions Manually inspect data Compare to known biology

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State of the art

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WC models provide novel insights

v v

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WC models help design cells

lacI

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WC models help purpose drugs

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Limitations of the Mycoplasma model

  • Represents one of the smallest bacteria
  • Ignores several processes
  • Mispredicts several phenotypes
  • Methods were ad hoc
  • Hard to understand, reuse, and expand
  • Time-consuming to build

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Toward more comprehensive and more accurate models

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Goal: design precise therapy

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  • Karyotypically normal
  • Autonomous
  • Well-characterized

Challenge: H1 hESCs

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Bottlenecks

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Bottlenecks

  • Data aggregation: Hard to find relevant data

– Data is incomplete, scattered, and insufficient annotated

  • Model design: Hard to capture multiple scales and

describe models modularly

– Insufficient abstraction and metadata

  • Simulation: Hard to simulate multiple scales

– Simulators are only support individual formalisms and are slow

  • Verification: Little formalism or standardization
  • Collaboration: Difficult to describe the data,

assumptions, and decisions that underlie modeling

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Data needed for WC modeling

𝑤 = 𝑙cat [enzyme] substrate substrate + 𝐿m

Metabolite concentrations Enzyme concentrations Reaction kinetics

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Data needed for WC modeling

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Datanator: data integration & discovery

Aggregate Find Reduce Review

7.3 10-4 mM

  • Species
  • Environment

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Datanator: data aggregation

Metabolites

  • ChEBI
  • ECMDB, YMDB
  • PubChem

DNA

  • GenBank

RNA

  • Array Express
  • MODOMICS
  • RNALocate
  • RNA MOD

Protein

  • COMPARTMENTS
  • CORUM
  • Human Protein Ref. DB
  • Pax-DB
  • PDB
  • PSORTdb
  • RESID
  • UniProt

Pathways

  • KEGG
  • Pathway

Commons

  • Reactome
  • WikiPathways

Rates

  • BRENDA
  • SABIO-RK

Interactions

  • BioCyc
  • DBTBS
  • DrugBank
  • JASPAR
  • KEGG
  • SuperTarget

Taxonomy

  • NCBI

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Measured entity/property Measured value, uncertainty, units Genotype

– Taxon – Genetic variant – Cell, tissue type

Environment

– Temperature – pH – Growth media

Data generation process

– Experimental design – Measurement method

Data analysis process

– Software – Version

Metadata

– Authors – Curator – Date – Citation

Datanator: actionable metadata

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Datanator: Finding relevant data

Chemical similarity

– Tanimoto index – Sequence similarity

Genetic similarity

– Whole-genome similarity – Taxonomic distance

Environmental similarity

– Temperature – pH

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WC-Lang: scalable model descriptions

  • Concretely describe composite multi-

algorithmic models

  • Concrete descriptions of every model element
  • Capture data and assumptions underlying

models

  • Explicit descriptions of mixed granularity /

lumping

  • Structured description of initial conditions
  • User interfaces suited to large models

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WC-Lang: scalable model descriptions

RNA(i, 0) + NTP(i, 1)  RNA(i, 1) + PPi RNA(i, 1) + NTP(i, 2)  RNA(i, 2) + PPi RNA(i, 2) + NTP(i, 3)  RNA(i, 3) + PPi RNA(i, 3) + NTP(i, 4)  RNA(i, 4) + PPi …

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WC-Lang: scalable model descriptions

RNA(i, l) + NTP(i, l+1)  RNA(i, l+1) + PPi RNA(I, l) + H2O  RNA(i, l-1) + NMP(i, l) Protein(i, l) + AA(i, l+1)  Protein(i, l+1) + H2O Protein(i, l) + H2O  Protein(i, l-1) + AA(i, l)

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WC-Lang: scalable model descriptions

Initiation Elongation Termination SBML 1 per RNA 335 1 per base ~500k 1 per RNA 335 Rules 1 1 1 1 1 1

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WC-Sim: scalable co-simulation

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H1-hESC model

Recon 2.2 H1 model

  • Kinetic data

(SABIO-RK)

  • Protein

abundance (Phanstiel et al., 2011; PaxDB)

Composable model

  • H1

transcriptomics data (ENCODE)

  • Cell composition
  • Media

composition

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Summary

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Availability

  • Code: code.karrlab.org (GitHub, PyPI)
  • Data: data.karrlab.org (Quilt)
  • Images: DockerHub
  • Primer and docs: docs.karrlab.org
  • Tutorials: sandbox.karrlab.org

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Summary

Bioengineering and medicine needs WC models WC modeling is becoming feasible New technologies will enable WC modeling Pilot models will show the feasibility of bacteria and human models

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Tips & tricks

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Challenges to g2p2pop

  • Build models from imperfect data
  • Capture complexity within and between

scales

  • Systematically link scales
  • Scalably simulate multiple scales
  • Collaborate
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Stretch goals inspire innovation

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Integration enables great scope and depth

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  • Data aggregation
  • Model composition
  • Multi-algorithmic co-simulation
  • Modular methods and software
  • Interdisciplinary collaboration
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Frameworks enable scalable integration

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Common languages enable frameworks

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Agent-based modeling can capture complexity

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Collaboration enables solutions

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Modularity enables collaboration

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Sharing promotes collaboration

  • Quilt: data
  • GitHub: code
  • PyPI: packaged code
  • Docker: computing environments
  • Google Docs, Overleaf: written documents
  • Google Drive: other files
  • GitHub issues: tasks

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Common practices ease collaboration

  • Interfaces between modules
  • Coarse-graining
  • Package organization
  • Coding, documentation styles
  • Software libraries

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QC inspires trust among collaborators

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Data integration enables modelers to drive science

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Summary

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Integration is enabling WC modeling

Genomic and biochemical data Pathway models Rule-based modeling Multi-algorithmic simulation

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