modelling tumor growth - dual guides

problem/goal/basics steepness log(size_99th) - log(size_95th) the percentiles are parameters, these are the defaults, they were used in the article steepness and hill is highly correlated why isn't lkb1 the best? steepness will be high if there is a high probability of a big tumor we saw on the CDFs that lkb1 has a "bump" (dúsulás) so we expect lkb1 to have a high steepness value => is it because of the parameters? is there correlation between the fitting error and the steepness? (default and new parameters) we expect correlation (with "good" parameters) because we saw that all the badly fitting ones have that "bump"  original size cdf: (red is lkb1, green is pten) pten's high steepness: 99th percentile size is the highest 95th percentile is not that different from the others => log(size_99th) - log(size_95th) will be big lkb1's "low" steepness 99th percentile size is high 95th percentile is the highest by a big margin! => lo...

 algoritmus roviden (az a resz amit en ertek es kell ehhez) parameterek initial cells (1) hany sejtrol indul initial time (0) melyik idopillanatrol indul max time (1000) mennyi ideig fut a szimulacio k production rate? (minel nagyobb a szam annal nagyobb tumorok lesznek) gamma degradation term (minel kisebb a szam annal nagyobb tumorok lesznek) iteraciok (addig fut amig time < max time) rates: k, gamma * current cells k nem szorzodik, gamma igen! rate sum: sum(rates) eldontjuk hogy mi tortenik veszunk egy random szamot 0 es 1 kozott (random) random < (k)/(rate sum) -> osztodik (current cells:=current cells + 1) random > (k)/(rate sum) -> meghal (current cells:=current cells - 1) delta time := exponential(1/rate sum) (exponencialis eloszlasbol huzunk, varhato erteke 1/ratesum) delta time egyre kisebb lesz (egyre tobb sejtunk van, egyre surubben lesznek a folyamatok) time := time + delta time egyeb dolgok mi a varhato meret? akkor all meg a novekedes ha kb egyenlo esel...