rename notes -> lectures

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diff --git a/lectures/lec01_intro b/lectures/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/lectures/lec02_matlab1 b/lectures/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/lectures/lec03_matlab2 b/lectures/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/lectures/lec04_matlab3 b/lectures/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/lectures/lec05_matlab4 b/lectures/lec05_matlab4
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+
+matlab has globals!
diff --git a/lectures/lec06_intro1 b/lectures/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/lectures/lec07_intro2_eulers b/lectures/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/lectures/lec08_intro3 b/lectures/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/lectures/lec09_intro4_stability b/lectures/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