Systems biology employs a variety of modelling techniques to understand, predict, and manipulate the complex interactions within biological systems. These models differ in terms of their mathematical frameworks, levels of abstraction, and suitability for different types of biological questions. Below is an overview of major modelling approaches, their strengths and limitations, followed by guidance on how to choose the right model for specific research aims.
1. Deterministic Models
➤ Description:
These models use ordinary differential equations (ODEs) to represent the continuous change in concentrations of biological molecules over time.
➤ Strengths:
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Provide precise, time-dependent dynamics.
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Suitable when component numbers are large and fluctuations are negligible.
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Well-established analytical and numerical tools exist.
➤ Limitations:
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Require detailed kinetic parameters (which may be unavailable).
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Cannot capture stochastic (random) behavior or noise.
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Assume spatial homogeneity.
➤ Example:
Modelling the MAPK signalling cascade using ODEs to study how signal strength and duration are controlled.
➤ When to Use:
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When you have detailed kinetic data.
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When molecule numbers are high and deterministic behavior dominates.
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For studying time-course dynamics.
2. Stochastic Models
➤ Description:
These models incorporate randomness and are often implemented using algorithms like Gillespie’s stochastic simulation algorithm (SSA).
➤ Strengths:
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Capture intrinsic noise and random fluctuations.
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Crucial for systems with low molecule numbers (e.g., gene expression in a single cell).
➤ Limitations:
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Computationally intensive.
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Less intuitive than deterministic models.
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Require probabilistic rate constants.
➤ Example:
Modelling transcriptional bursting in gene expression.
➤ When to Use:
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When dealing with low-abundance species.
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When exploring how noise influences cell fate or phenotypic variability.
3. Boolean/Logical Models
➤ Description:
These models simplify biological interactions into binary states (on/off) and use logical rules (AND, OR, NOT) to simulate behavior.
➤ Strengths:
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Require minimal quantitative data.
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Easy to construct and analyze.
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Useful for large, qualitative network analysis.
➤ Limitations:
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Oversimplify dynamic behavior.
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Lack time-scale resolution.
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Cannot model concentrations or precise dynamics.
➤ Example:
Modelling the cell cycle control network in yeast using Boolean logic.
➤ When to Use:
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When kinetic data are unavailable.
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To understand network structure and essential control logic.
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For hypothesis generation and early model development.
4. Agent-Based Models (ABMs)
➤ Description:
These simulate the actions and interactions of individual agents (e.g., cells, molecules) within an environment.
➤ Strengths:
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Can capture spatial heterogeneity.
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Incorporate individual-level behavior and emergent system properties.
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Ideal for multi-scale or tissue-level models.
➤ Limitations:
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Computationally expensive.
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Hard to validate and calibrate.
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Require detailed rule sets and assumptions.
➤ Example:
Modelling tumour growth where each cell is an agent interacting with its microenvironment.
➤ When to Use:
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For spatially complex or multi-cellular systems.
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When studying emergent behavior or heterogeneity.
5. Constraint-Based Models (e.g., Flux Balance Analysis – FBA)
➤ Description:
Used primarily in metabolic network modelling; these optimize a biological objective (e.g., biomass production) under stoichiometric and thermodynamic constraints.
➤ Strengths:
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Do not require kinetic parameters.
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Scalable to genome-wide models.
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Efficient for predicting growth rates, metabolic fluxes.
➤ Limitations:
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Assumes steady state.
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Does not model dynamics or regulation.
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Results depend heavily on choice of objective function.
➤ Example:
Genome-scale FBA of E. coli metabolism to predict flux distributions under different nutrient conditions.
➤ When to Use:
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When studying metabolism at steady state.
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For genome-scale predictions.
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When kinetic data are scarce but stoichiometry is known.
6. Spatial Models
➤ Description:
Use partial differential equations (PDEs) or cellular automata to simulate spatial variation in biological systems.
➤ Strengths:
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Capture diffusion, transport, and spatial gradients.
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Important for tissue-level or developmental biology studies.
➤ Limitations:
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Mathematically and computationally intensive.
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Require detailed spatial data and parameters.
➤ Example:
Modelling morphogen gradients during embryonic development.
➤ When to Use:
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When spatial effects are critical (e.g., pattern formation, cell migration).
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When studying diffusion or morphogenesis.
Choosing the Right Model: Summary Table
| Biological Question | Recommended Model | Reason |
|---|---|---|
| How does a signalling pathway respond over time? | ODE model | Captures continuous, dynamic responses |
| How does noise affect gene expression in a single cell? | Stochastic model | Captures intrinsic randomness |
| What are the key regulatory components of a genetic network? | Boolean model | Useful with limited data |
| How does a tissue grow or heal? | Agent-Based or Spatial model | Captures cell-cell interactions and spatial effects |
| What are the metabolic capabilities of an organism? | Constraint-based model (FBA) | Scalable, no kinetic parameters needed |
Final Considerations
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Data availability is a major driver of model choice. Quantitative data enables dynamic models; qualitative data leans toward logical or FBA models.
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Computational resources may limit use of stochastic or agent-based models at scale.
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Model complexity vs interpretability is a trade-off — simple models may miss key behaviors; complex models may be hard to interpret or validate.
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