biology 624 - developmental genetics lecture #6 – modeling in development

Biology 624 - Developmental Genetics

Lecture #6 – Modeling in Development

Why Developmental Biology Needs Models 1. understand how mechanisms at one level of scale (ie

cell-level) interact to produce higher level phenomena (ie tissue-level)

  2. provides testable hypotheses for experimentation 3. this is the time to enhance the use of this approach in

developmental biology

4.  you don’t need to be a mathematician to do modeling

Cell Behavior to Tissue Integration

Robertson et al, 2007

Provides Testable Hypotheses - Predictions

Thorne et al, 2007

What is a Computational Model? 1. Uses experimental data computers can understand and assumptions of scientists to predict outcomes 2. Concept of “simulations” – run data through time and/or space to produce outcomes 3. Toggle from simulation outcomes to experimental outcomes

4. Do NOT make bad data turn into good data – experiments important 5. Help scientists better understand processes by emphasis on modularity, randomness, non-randomness, feedback loops, etc.

Modeling Diffusion in Fly Embryos

Tomlin and Axelrod, 2007

Diffusion of Morphogens in Fields with Receptors

Lander et al, 2002

Modeling Fly Stripes

Tomlin and Axelrod, 2007Von Dassow et al, 2000

Wg = greenEn = red

Modeling Intercalation during Frog Gastrulation

Longo et al, 2004Stain = Fibronectin

Cell Behavior is Governed by Rules in the Simulation

Longo et al, 2004

Time Sequence of BCR Thinning

Longo et al, 2004

Model Predicts Lateral Movement of Implanted Cells

Longo et al, 2004

Modeling Approaches: Top-Down

--aims to reveal overarching control mechanisms

--high-level attributes, ie “these cells die”

--governing rule set are potential relationships loosely derived from qualitative experiments

--GOAL: deduce a minimal rule set to reveal systems level controls

Modeling Approaches: Bottom-Up

--explicitly accounts for fine processes

--assembles these processes to predict higherlevel processes

--rules derived from quantitative empirical data

--GOAL: deduce emergent phenomena at a higherlevel from interactions at level below

Model Types - Continuum

--based on kinetic parameters--uses partial differential equations a lot--models environmental changes precisely--does NOT model spatial heterogeneity well--not very intuitive

Model Types – Agent Based Models

--agents (ie cells) behave according to rules--allow for spatial heterogeneity--allow for random or stochastic response--does NOT account for precise concentrations,etc--intuitive

Model Types – Combinations

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How Models Work

Thorne et al, 2007