System Dynamics based on multi-omics data II - A biologist-centric - - PowerPoint PPT Presentation

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System Dynamics based on multi-omics data II - A biologist-centric - - PowerPoint PPT Presentation

Jun 15, 2023 903 likes •1.17k views

System Dynamics based on multi-omics data II - A biologist-centric perspective - Outline What a biologist hopes to get What a biologist typically gets Where a biologist gets stuck How a biologist is trying to overcome

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System Dynamics based on multi-omics data II

  • A biologist-centric perspective -
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Outline

  • What a biologist hopes to get…
  • What a biologist typically gets…
  • Where a biologist gets stuck…
  • How a biologist is trying to overcome limitations…
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Sysbio in a nutshell

Biological insights (components, interactions, kinetic laws, parameters,…) OMICS Genes, RNAs, Proteins & PTMs, Metabolites Lipids PHENOTYPE Morphology Physiology Fitness Pathology “classical reductionistic techniques” Top-down: „Find components & interactions from data!“ Bottom-up: „Find the simplest model(s) to predict system’s behavior!“ in vivo in vitro in vivo

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Typical biologist’s question:

What’s going on?

translation:

What causes the phenotype?

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A thought experiment Ein Gedankenexperiment

Typical biologist’s question: What causes the phenotype? Let’s assume you can measure ALL RNAs|proteins|metabolites in a cell… how do you address the question?

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Task #1: what caused this fingerprint?

red = higher in mutant, green = lower in mutant, size = p-value

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Task #2: what caused this scenario?

red = higher in mutant, green = lower in mutant, size = p-value

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Example: Response of skin cells to H2O2

Q: Which enzyme(s) cause the observed metabolic patterns?

after 5 min 20 min 60 min

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Is multi-omics the solution?

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My very personal take after 10 years SysBio (and > 500’000 analyses)

It’s simple to find differences

Biological events ‘omics changes

It’s difficult to unravel the causes

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Fundamental challenges

Human intuition doesn’t scale. It’s hard to measure the right thing, i.e. activity and interactions.

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Biological insights (components, interactions) Data mining Prediction

We can measure components efficiently, but what about the rest?

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What about measuring activity?

Sauer, Heinemann & Zamboni, Science 2007

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Open questions

  • How to map interactions in vivo?
  • How to identify the interactions that determine a

given phenotype?

  • How do we predict phenotypes for novel

conditions/perturbations?

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How do we generate testable hypotheses from omics profiles?

1. Include network information in data mining Different levels of sophistication:

  • Graph (directed or not)
  • Thermodynamics, kinetics
  • Prior information on regulatory links
  • …

2. Use time-resolved experiments

  • Time imposes a hierarchy on events
  • Time allows to resolve fast from slow events
  • 3. Reference metabolomics sets
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Reference datasets

Systematic ‘omics analysis upon known perturbations

  • E. coli single gene knockout library (35’000, completed)
  • Druggable genome siRNA library (32’000, in progress)
  • Yeast TFs, kinases, proteases (> 2000, completed)
  • 200 (cancer) cell lines (1500, completed)
  • Drugs (…)

Non-targeted reannotation of ORFs

  • E. coli proteome (>22’000 , completed)
  • Yeast proteome (>35’000 , completed)
  • Human proteome (...
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Example: Metabolic segmentation by Random Markov Fields

Q: Which enzyme(s) likely caused the observed metabolic patterns?

GAPDH G6PDH

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Time in cellular processes

seconds minutes hours days Characterize temporal organization during metabolic transients

  • Adaptation
  • Development
  • Cell cycle
  • Circadian rythm

Resolve translational responses from direct effects, e.g.

  • allosteric regulation of metabolites
  • n enzymes
  • Kinome, PTMs
  • xenobiotics (drugs)

Time scale

DNA mRNA Proteins Metabolites

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Real-time metabolomics

Seconds

Discover small molecule – enzyme interactions Determine reaction mechanisms and kinetic parameters Investigate in vivo drug metabolism (on-target and off-target)

50 100 150 2 2.5 3 3.5 4 4.5 5 5.5

glucose hexose-6-P PEP pyruvate

Hours Differentiation Adaptation Transcriptional responses Cell cycle …

0.27 0.33 0.4 0.47 0.53 0.6 0.67 0.73 0.8 0.87 0.93 1 1.07 1.13 1.2 1.27 1.33 1.4 1.47 1.53 1.6 1.67 1.73 1.8 1.87 1.93 2 2.07 2.13 2.2 2.27 2.33 2.4 2.47 2.53 2.6 2.67 2.73 2.8 2.87 2.93 3 3.07 3.13 3.2 3.27 3.33 3.4 3.47 3.53 3.6 3.67 3.73 3.8 3.87 3.93 4 4.07 4.13 4.2 4.27 4.33 4.4 4.47 4.53 4.6 4.67 4.73 4.8 4.87 4.93 5 5.07 5.13 5.2 5.27 5.33 5.4 5.47 5.53 5.6 5.67 5.73 5.8 5.87 5.93 6 6.07 6.13 6.2 6.27 6.33 6.4 6.47 6.53 6.6 6.67 6.73 6.8 6.87 6.93 7 7.07 7.13 7.2 7.27 7.33 7.4 7.47

M G1 S M G1 S M

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Ensemble (mass) modeling allows to identify key regulatory interactions

Link et al, Curr Opin Biotechnol, 2014

Metabolite Fold-changes

Küpfer et al, Nature Biotech 2007 > SIGNALING Link et al, Nature Biotech 2013 > ENZYME – METABOLITE INTERACTIONS Link et al, Nature Methods 2015 > ALLOSTERIC REGULATION

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Ensemble modeling for analysis of cell signaling dynamics

Küpfer et al, Nature Biotech 2007

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Example II: In vivo discovery or allosteric regulation

Several key enzymes in glycolysis are known to be potentially activate/inhibited by metabolites.

Q: In condition XYZ, which allosteric links are actively regulating glycolysis?

Link et al, Nature Biotech 2013, 31(4):357

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Test for all network topologies that can mimick metabolite dynamics

  • ODE model of metabolism and

putative allosteric links

  • Perform time-resolved metabolomic

experiment

  • Ensemble modeling

Test which models can reproduce the data. In this case, 3’600 variants with 0-2 allosteric interactions exist.

  • Count how frequently an interaction

leads to a good fit

  • Biochemical assay in vitro

Link et al, Nature Biotech 2013, 31(4):357

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Recap

Challenges:

  • How to generate testable hypotheses from omics data?

(OMICS > targeted, ad-hoc assays)

  • How to map interactions in vivo?
  • How to identify the key interactions that determine a given

phenotype?

  • How do we predict phenotypes for novel

conditions/perturbations? It’s a combined computational/analytical effort.