BIOLOGY 624 Fall 2009
DR. VICTORIA BAUTCH
Lecture #6:
Thurs 9/24/09
DEVELOPMENTAL GENETICS
Computational Models in Developmental Biology
Reading: Tomlin and Axelrod, 2007; Thorne et al., 2008 (see end of
lecture for complete references
(Click here to access the Powerpoint)
Paper discussion (Thurs 9/24): Sivak J, Petersen L, Amaya E. (2005).
FGF signal interpretation is directed by Sprouty and Spred proteins
during mesoderm formation. Dev Cell 8, 689-701.
Paper discussion (Tues 9/29): Kragl M, Knapp D, Nacu E, Khattak S,
Maden M, Epperlein HH, Tanaka EM (2009). Cells keep a memory of
their tissue origin during axolotl limb regeneration. Nature 460, 60-67.
A. Why should dev biologists become familiar with computational
models? (Slide 2)
1. approach to understand how mechanisms at one level of biological
scale (ie cell-level) interact to produce higher level phenomena (ie tissuelevel) (Slide 3)
a. cell-cell and cell-matrix interactions lead to pattern in ways we can’t
comprehend by focus on one small input
b. signaling pathways and inputs interact to inform cell behavior in ways
that require understanding of networks
2. provides testable hypotheses for experimentation – accelerate the
discovery process (Slide 4)
3. this is the time to enhance the use of this approach in dev biology –
models are better (more robust) and experimental foundation of many
developmental processes known in enough detail to make modeling useful
4. you don’t need to be a mathematician to do modeling – in fact there are
lots of modelers with the tools who love to collaborate with biologists
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B. What is a Computational Model? (Slide 5)
1. Uses experimental data that computers can understand and
assumptions of scientists to predict outcomes
2. Based on the concept of “simulations” – run the data through time
and/or space and get an outcome/outcomes
3. Best models toggle from simulation outcomes to experimental
outcomes and provide experimentally testable hypotheses
4. Do NOT make bad data turn into good data – experiments very
important in the process
5. Help scientists better understand processes by emphasis on
modularity, randomness, non-randomness, feedback loops, etc.
C. Examples of biological processes that have been modeled:
1. Diffusion (Slide 6)
--morphogen gradients, bicoid in fly oocyte
--more complex gradients where matrix/receptor interactions are important
(Lander et al, 2002) (Slide 7)
2. Pattern formation in Drosophila (Slide 8)
--segment polarity genes – they get input from pair-rule genes but also
from each other (Tomlin and Axelrod, 2007 review)
3. The movements of intercalation to thin the blastocoel roof during
gastrulation (Slide 9-12) (Longo et al, 2004).
4. More! We’ll mention models as we discuss epithelial morphogenesis
and blood vessel formation later on.
D. Modeling Types and Approaches
1. Top-down vs. bottom-up
a. Top down models (Slide 13):
--represent high-level attributes as coarse functional modules to reveal
overarching control mechanisms
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--governing rule sets are potential functional relationships that are loosely
derived from qualitative experiments
--goal is to deduce a minimal rule set to predict observed phenomena at a
coarse level to reveal systems-level control mechanisms
b. Bottom up modeling (Slide 14):
--explicitly accounts for fine processes that assemble to predict higherlevel processes
--rules derived from quantitative empirical data
--goal is, given what is known about individual cell behavior and
interactions, can emergent phenomena be predicted at systems level?
Example: if “rules” for each cell in a group are set, does the behavior of
the group follow or predict reality?
2. Continuum vs. Agent-based Models
a. Continuum - aka “classical” (Slide 15)
--this type of model is based on kinetic parameters of – on rates, off rates,
binding affinity, concentrations of specific pathway components; lots of
math and partial differential equations
--models the environment very well, but does not account as well for
individual responses to input – ie cell to cell differences; responses are
averaged so spatial differences important to processes not modeled
--important to model changes in environment/input over time
--can be very predictive but are not intuitive
b. Agent-based models (Slide 16)
--in these models “agents” are capable of behaviors based on “rules” –
agents are often cells, and rules are the experimental input
--these models allow for random or stochastic behavior to an input
--are more intuitive as individual cells can be modeled
--does not account for precise concentrations and molecular interactions; a
little more “rough” at that level
c. Many processes are being tackled by combinations of each type of
model these days (Slide 17) – the best of both worlds with precise
molecular components and inputs modeled via continuum and the
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response and interactions of cells that form tissues are via agent-based
modeling. For an example see Robertson et al, 2007.
E. How do models work? (Slide 18)
1. a data set is used to set up the model
2. simulations using the parameters and/or rules determine how well the
model mimics reality
--good congruence means that essential components are in place and
have appropriate importance
--poor congruence means that important components/parameters are
either missing or not at appropriate level of influence
3. Either of the above tells scientist more about the process
4. Ultimate test – is the model predictive?
--this means that can it predict outcomes or behaviors that were not part of
the input for model construction.
--predicted outcomes are tested, and model is refined.
Optional reading:
Reviews:
Thorne BC, Bailey AM, DeSimone DW, Peirce SM. (2007). Agent-based
modeling of multicell morphogenic processes during development. Birth
Defects Res 81, 344-353.
Tomlin CJ, Axelrod JD. (2007). Biology by numbers: mathematical
modeling in developmental biology. Nat Rev 8, 331-340.
Thorne BC, Bailey AM, Peirce SM. (2007). Combining experiments with
multi-cell agent based modeling to study biological tissue patterning.
Briefings Bioinform 8, 245-257.
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Meinhardt H. (2008). Models of biological pattern formation: from
elementary steps to the organization of embryonic axes. Curr Topics Dev
Biol 81, 1-63.
Meinhardt H. (2007). Computational modeling of epithelial patterning. Curr
Opin Genet Dev 17, 272-280.
Articles:
Longo D, Peirce SM, Skalak TC, Davidson L, Marsden M, Dzamba B,
DeSimone DW. (2004). Multicellular computer simulation of
morphogenesis: blastocoel roof thinning and matrix assembly in Xenopus
laevis. Dev Biol 271, 210-222.
Grant MR, Mostov KE, Tlsty TD, Hunt CA. (2006). Simulating properties of
in vitro epithelial cell morphogenesis. PLoS Comput Biol 2, e129.
Engelberg JA, Kim M, Mostov KE, Hunt CA. (2008). In silico simulation of
epithelial cell tubulogenesis. IEEE, 1036-1039.
Lander AD, Nie Q, Wan FYM. (2002). Do morphogen gradients arise by
diffusion? Dev Cell 2, 785-796.
Robertson SH, Smith CK, Langhans AL, McLinden SE, Oberhardt MA,
Jakab KR, Dzamba B, DeSimone DW, Papin JA, Peirce SM. (2007).
Multiscale computational analysis of Xenopus laevis morphogenesis
reveals key insights of systems-level behavior. BMC Systems Biol 1, 46.
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