# Mathematical Modelling for Synthetic Biology aGEM Workshop on Mathematical Modelling July 22, 2012, Lethbridge, AB Brian Ingalls Department of Applied.

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Mathematical Modelling for Synthetic Biology aGEM Workshop on Mathematical Modelling July 22, 2012, Lethbridge, AB Brian Ingalls Department of Applied Mathematics University of Waterloo Waterloo, ON

Workshop Outline Introduction to mathematical modelling of biochemical reaction networks Modelling of gene regulatory networks Lab I: simulation of kinetic models Tools for model analysis Lab II: model-based design of gene regulatory networks

Models in Science and Engineering Models are abstractions of reality

Models in Science and Engineering Models are abstractions of reality Models can be physical Ball-and-stick model of molecular structure http://mariovalle.name/ChemViz/representations/index.htmlhttp://srxawordonhealth.com/2012/04/ Mouse model of obesity

Models in Science and Engineering Models are abstractions of reality Models can be physical or conceptual http://www.nature.com/scitable/topicpage/gpcr-14047471 Interaction diagram model of G-protein signalling Kinetic model of bacterial chemotaxis signalling pathway

Models in Science and Engineering Models are abstractions of reality Models can be physical or conceptual Mathematical models are mechanistic (based on physico-chemical laws) and predictive (allow inferences beyond the data used for their construction)

How are mathematical models used in molecular biology? Models summarize data Models allow of falsification of hypotheses Models allow exploration of system behaviour (in silico experiments) Model-based design allows easy exploration of design space

Model Construction

Modelling Chemical Reaction Networks Chemical reaction: Rate constant: Law of mass action:

Using derivatives to describe rates of change Ex: decay reaction: rate of change of [A] at time t rate of reaction at time t differential equation model

Solution of the differential equation model Model simulation = in silico experiment

Model Simulation

Numerical simulation of differential equation models Approximate derivative by a difference quotient Rearrange to yield an update rule:

Implemented in MATLAB, XPPAUT, Copasi, Mathematica, Maple, … Tutorials in notes for XPPAUT (freeware, simulation and analysis of differential equation models) MATLAB (licensed, general-use computational software) Repeated application of the update rule (starting from known initial concentration):

Example network model networ k simulation model

Model Analysis

Separation of time-scales Every model is formulated around a specific time-scale  Processes occurring on a slower time-scale are treated as frozen in time  Processes occurring on a faster time-scale are presumed to occur instantaneously

Treating rapid processes Rapid equilibrium approximation: presume at all times. Quasi-steady-state approximation: presume [A] is in steady-state with respect to [B] at all times.

Sensitivity analysis Measure sensitivity of steady-state species concentrations to changes in model parameters

Applications of Sensitivity analysis Identification of optimal drug targets (steps with high sensitivities) Bakker et al. 1999, ‘What controls glycolysis in bloodstream form Trypanosoma brucei? JBC 274.

Applications of Sensitivity analysis Interpretation of regulation schemes: role of negative feedback

Biochemical Kinetics

Saturation: Michaelis-Menten kinetics Rates of enzyme-catalysed reactions exhibit saturation: Michaelis-Menten kinetics

Cooperativity: Hill function kinetics Processes involving multiple interacting components can exhibit sigmoidal activity (e.g. cooperative binding of O 2 to hemoglobin) Hill function (empirical fit)

Gene Regulatory Networks

Modelling constitutive gene expression mRNA dynamics: protein dynamics:

Modelling constitutive gene expression If mRNA dynamics are fast (compared to protein dynamics): Treat mRNA in ‘quasi-steady-state’ Reduced model only describes protein concentration :

Regulated gene expression Constitutive expression Repressed expression

Regulated gene expression Constitutive expression Activated expression

Modelling regulated expression

Regulation by multiple transcription factors Distribution of states: If A=B=P, with cooperative binding:

Natural Gene Regulatory Networks

Autoregulating genes Autorepressor: (regulation enhances robustness and response timing) Autoactivator: (bistable ON/OFF switching behaviour)

Gene switch: lac operon Gene autoactivates in response to lactose

Gene switch: lysis/lysogeny decision in phage lambda cro cI Double negative feedback locks in one of two states

Oscillatory gene network: the Goodwin oscillator Delayed negative feedback leads to sustained oscillations

Oscillatory gene network: circadian rhythm generator Delayed negative feedback leads to sustained oscillations Model: Goldbeter, 1996

Developmental gene networks Endomesoderm specification in purple sea urchin (Davidson, Bolouri et al.) Segmentation in Drosophila

Engineered Gene Circuits

The Collins Toggle Switch Gardner, Cantor, and Collins, Nature, 2000 Double repression locks in one of two possible states. Inducers allow transitions

Collins toggle switch: implementation

The Repressilator Elowitz and Leibler, Nature, 2000 Three-step repression ring generates delayed negative feedback: sustained oscillations

Repressilator: Implementation progeny single cell

Improved oscillator design: relaxation oscillator Interplay of positive and negative feedback lead to robust sustained rise-and-crash oscillations

Stricker/Hasty oscillator Stricker et al., Nature, 2008 Interplay of positive and negative feedback lead to robust sustained oscillations

Stricker/Hasty oscillator implementation

Lab I Goals: 1)Simulate a differential equation model in XPPAUT 2)Determine sensitivity coefficients for a simple network model 3)Explore system behaviour in models of gene regulatory networks: the Collins toggle switch, the repressilator, the Hasty/Stricker oscillator

Lab I 1)Simulate a differential equation model in XPPAUT a) Open XPPAUT with file Lab1.ode or generate your own file, content is simply: par k=1 x’=-k*x init x=1 done b) Select Initialconds|Go (I|G). Resize the window with Window/zoom|Fit (W|F). Then choose Initialconds|New (I|N) and run a simulation with initial value of x set to 0.5. c) Open the param window, change the value of k to 1. Re-run your simulations from x(0)=1 and x(0)=0.5. How has the behaviour changed? 2) Determine sensitivity coefficients for a simple network model a)Open XPPAUT with file Lab2.ode b)Select Initialconds|Go (I|G). Use the Data window to view all four species concentrations. Select Graphic stuff|Add curve (G|A) to add additional time-series to the plot. c)Open the param window. Explore the effect of changing the parameter values. Consider the sensitivities of the steady state of [A] with respect to (i) k1; (ii) k2; and (ii) k3. 3 ) Explore system behaviour in models of gene regulatory networks: the Collins toggle switch, the repressilator, the Hasty/Stricker oscillator a) Open XPPAUT with one of the files Lab_toggle.ode, Lab_repressilator.ode, or Lab_hastyosc.ode b) Select Initialconds|Go (I|G). Verify the system’s desired behaviour: oscillations or bistability (for bistability, modify the initial conditions). c) Explore the effect of modifying the model parameters (param window) on the desired behaviour

Tools for analysis of dynamic mathematical models

The phase plane

Example network: Time series: species concentrations plotted against time Phase portrait: species concentrations plotted against one another The phase plane

Time series: multiple simulations (range of initial concentrations) Phase portrait: multiple simulations reveal system behaviour

The phase plane Direction field Nullclines (turning points) Intersection of nullclines: steady state.

Stability

Example: symmetric antagonistic network: Case I: Non-symmetric inhibition strengths: unique long-time (steady-state) behaviour

Stability Example: symmetric antagonistic network: Case II: Symmetric inhibition strengths: two potential long-time (steady-state) behaviours

Stability Nullclines intersect three times. Intermediate steady- state is unstable, other two are stable.

Stability A bistable system

Linearized Stability Analysis Evaluate Jacobian at steady state: Model: Determine eigenvalues of Jacobian: If all eigenvalues have negative real part, then the steady state is stable

Oscillations

Oscillatory behavior Example: autocatalytic pathway: Case I: weak autocatalysis: damped oscillations (settling to steady state)

Oscillatory behavior Example: autocatalytic pathway: Case II: strong autocatalysis: sustained (limit cycle) oscillations Limit cycle

Bifurcation analysis

Bifurcation diagrams The location of steady states depends on model parameters Plot of steady-state concentration against parameter value: continuation diagram

Bifurcation diagram: bistability Parameter values at which the number or stability of steady states change are called bifurcation points nullclines vary with parameter values Bistable network

Bifurcation diagram: oscillations Autocatalytic network Damped (transient) oscillation persistent (limit cycle) oscillation

Model-based design

The Collins Toggle Switch repression Network: Goal: bistability

Model: Model-based design analysis Conclusions: need strong expression, highly nonlinear repression

The Repressilator Network: Goal: sustained oscillations

Conclusions: need low leak, strong non-linearity, short-lived proteins/long-lived mRNA Model: Model-based design analysis

Alternative Modelling Frameworks

Alternative modelling frameworks Stochastic modelling Compartmental modelling Boolean modelling Spatial Modelling (PDEs)

Lab II Goal: Model-based design analysis of the Collins toggle switch and the Hasty/Stricker oscillator

Lab II 1)Model-based design analysis of the Collins toggle switch a)Open XPPAUT with Lab_toggle.ode. b)Generate a phase plane (Viewaxis|2D -- select P1 and P2 for the axes, set the window to [0,4]x[0,4]). Select Initialconds|mIce (I|I) and generate multiple trajectories c)Generate the nullclines: Nullclines|New (N|N) d)Open the param window and change b to b=1. Reconstruct trajectories and nullclines. (Erase the previous phase portrait.) How has the behaviour changed? e)Explore a range of values for each parameter. What changes improve the robustness of the bistability? Which changes eliminate it? What design choices can you recommend? 2 ) Model-based design analysis of the Hasty/Stricker oscillator relaxation a)Open XPPAUT with Lab_hastyosc.ode b)Repeat (b) and (c) above. (Set the window to X,Y [0,4]x[0,4]). c)Open the param window and change alpha to alpha=5. Reconstruct trajectories and nullclines. (Erase the previous phase portrait.) How has the behaviour changed? d)Explore a range of values for each parameter. What changes eliminate the oscillations? What design choices can you recommend? 3 ) Stability analysis Determine the robustness of steady-state stability by determining the eigenvalues of the system Jacobian: select Sing. pts.|Mouse, click near an equilibrium, print eigenvalues (negative real part signifies stability) 4) Bifurcation analysis Generate a bifurcation diagram. Set parameters to their default values. Run a trajectory to steady state. Hit (I|L) a few times to ensure steady state has been reached. Open AUTO: F|A. Set the parameter of interest to Par1 in the Parameter window. Set the window size in the Axes window. Set the parameter value range (Par Min and Par Max) in the Numerics window. Tap Run.

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