rename notes -> lectures

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diff --git a/notes/lec01_intro b/notes/lec01_intro
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-
-top down approach:
-    start with data, do stats, make predictions
-    see also Ma'ayan coursera course, "Network Analysis in Systems Bio"
-
-bottom up approach:
-    start with a model, run simulations to make predictions
-    scope: ODE, dynamics
-    beyond: parameter estimations, PDE, stochastics
-
-7 weeks, 25 lectures, 20 minutes each, 3-4 a week
-questions at the end of lectures, "just for self"
-5x homework assignments, required
-
-will discuss neuron firing a bit
-
-impression: will be very motivated
diff --git a/notes/lec02_matlab1 b/notes/lec02_matlab1
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-
-'format compact' => less syntax
-
-self assess:
-    C) 13x4
-    E) vertcat; different columns
-
-### julia notes:
-
-built-in types seem to default to just an array instead of, eg, 3x1 or 1x3.
-
-can transform with "'"; A'' != A?
-
-to vert/horiz cat, need to explicitly 
-
-julia> size([1,2,3])
-(3,)
-
-julia> size([1,2,3]')
-(1,3)
-
-julia> size([1,2,3]'')
-(3,1)
-
diff --git a/notes/lec03_matlab2 b/notes/lec03_matlab2
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-
-### julia notes
-
-for plotting, 'winston' is simple and matlab-like
-              'gadfly' is nice/correct, using design paradigms
-              'pyplot' is matplotlib
-
-for this class, at least to start, i'm going to try winston.
-
-Pkg.add("Winston")
-using Winston
-plot( cumsum(randn(1000)) )
-
-in winston, fplot() is nice for plotting functions over ranges
diff --git a/notes/lec04_matlab3 b/notes/lec04_matlab3
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-
-
-mean()
-std()
-exp(x) -> e^x
-
-
-
-### julia notes
-
-ref: http://julia.readthedocs.org/en/latest/manual/noteworthy-differences/
-ref: http://sveme.org/julia-for-matlab-users-i.html
-
-use maximum() instead of max().
-doesn't return multiple values like matlab. use 'findmax' instead.
-
-anonymous function syntax:
-
-    find(x-> x > 2, [1,2,3,4,5])
-
-unpack using parens (tuples?), not brackets
-
-writedlm, readdlm instead of dlmwrite, dlmread
-read/write instead of save/load?
-
-some built-ins like {sum, prod, max} are deep, not shallow like in matlab.
diff --git a/notes/lec05_matlab4 b/notes/lec05_matlab4
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-
-matlab has globals!
diff --git a/notes/lec06_intro1 b/notes/lec06_intro1
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-
-Background:
-
-Ligands are little molecules (which could be proteins or chemicals or whatever)
-which bind to a larger biomolecule (eg, a protein or DNA) called the receptor.
-"Receptor/ligand" binding affinity refers to how strongly different ligands
-want to attach to different receptors. Both binding (association) and
-un-binding (dissociation) is happening all the time, so you get a (dynamic, or
-possibly steady state) distribution of binding probability.
-
-ref: https://en.wikipedia.org/wiki/Ligand_(biochemistry)
-
-ODEs (ordinary differential equations) are those involving only a single
-independent variable; eg, solving for x in terms of t, only having derivatives
-dx/dt, (d^2 x / d x^2), etc. the order of the ODE is the highest order of
-derivative.
-
-PDEs (partial differential equations) are those involving multiple independent
-variables, and thus partial derivatives. Eg, x in terms of t and r, having
-derivatives del x / del t, del x / del r, and del^2 x / (del t * del r).
-
-ref: https://en.wikipedia.org/wiki/Differential_equation#Ordinary_and_partial
----------
-
-Law of mass action: rate of a reaction involving two quantities is proportional
-to the product of the densities of both.
-
-Michaelis-Menten: approximation to solution of enzyme-catalyzed reaction
-equation:
-
-    d [S] / dt = (max reaction rate) * [S] / (Km + [S])
-
-    [S] is concentration of substrate S
-    Km is Michaelis constant, which is a specific substrate concentration
-
-    (max reaction rate) =~ k_2 [E]_total
-    Km =~ (k_-1 + k_2) / (k_1)
-
-    all assuming that enzyme E catalizes S into P with rates k_n:
-
-                    -> k_1
-        [E] + [S]            [ES]    -> k_2  [E] + [P] 
-                    <- k_-1
-
diff --git a/notes/lec07_intro2_eulers b/notes/lec07_intro2_eulers
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-
-euler's method:
-
-dx/dt =~ ( x(t + Dt) - x(t) ) / Dt, for very small Dt
-
-so, x(t + Dt) = x(t) + f(x) * Dt, which is how to integrate the system
-
-"if Dt is too large, becomes highly unstable" (duh)
-
-scary MATLAB advice:
-- watch out of totally crazy values (very high)
-- non-negative values go negative
-
-MATLAB built-in ODE solvers: ode23, ode15s
-
-runge-kutta (use dxdt from between t_n and t_(n+1) )
-variable time-step methods
-
-summary: Euler's method sucks, news at 11.
-
-### misc julia notes
-
-the only place to find history (?!?!!) is in ~/.julia_history
-
-### codes
-
-using Winston
-
-a=20
-b=2
-c=5
-dt = 0.05
-tlast = 2
-
-iterations = int( round(tlast/dt) )
-xall = zeros(iterations, 1)
-x = c
-
-for i = 1:iterations
-    xall[i] = x
-    dxdt = a - b*x
-    x = x + dxdt*dt
-end
-
-time = dt * [0:iterations-1]'
-plot(time,xall)
diff --git a/notes/lec08_intro3 b/notes/lec08_intro3
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-
-One method for determining stability of a system:
-
-    plot derivative of value (eg, energy?) as a function of the value itself.
-
-Another: 2D phase plane analysis
-
-    "stable limit cycle"
-    "stable fixed point"
-
-Short lecture, just old concepts.
diff --git a/notes/lec09_intro4_stability b/notes/lec09_intro4_stability
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-
-nullclines: set of points in phase space where one derivative is zero.
-    can often be derived analytically.
-    the intersections of nullclines are even more interesting; an intersection
-    in a 2-D phase space is a fixed point (equilibria)
-
-vectors in a vector field area point in the same direction (quadrant) until a
-nullcline is crossed. the area is a "discrete region".
-
-"stable-limit cycle" is when there is a stable closed curve in phase space (as
-oopposed to, eg, a fixed point)
-
-how to find if stable vs unstable?
-
-take the jacobian, and find the eigenvalues of the jacobian at the limit point.
-if real parts are all positive, then unstable. if all negative, then stable. if
-complex eigenvalues have positive real parts, then there is a stable limit
-cycle.
-
-the 'bier_stability.m' script calculates eigenvalues numerically.
-
-PROJECT: re-write this script in julia