Mathematical framework for human SLE Nephritis: disease dynamics and urine biomarkers

Table 2 Parameter estimates for s_i, s_id, and s_a, that correspond to the best fit of the model to the data.

	Patient 416			Patient 444			Patient 448			Patient 491
Time frame	s _i	s _id	s _a	s _i	s _id	s _a	s _i	s _id	s _a	s _i	s _id	s _a
[-6m, -4m]	0.002	0.005	0.05	6	1	3	0.2	0.26	0.4	0.5	0.6	1
[-4m, -2m]	0.001	0.003	0.1	2	0.5	4	0.2	0.26	0.4	0.5	0.6	1.2
[-2m, -2w]	0.005	0.015	0.05	2	0.5	2	0.02	0.06	0.4	0.5	0.6	1.2
[-2w, Flare]	0.005	0.015	0.05	5	2	2	4.5	3.8	0.4	0.5	0.6	1.2
[Flare, +2w]	0.001	0.002	0.3	0.5	0.5	7	0.06	0.02	5	0.1	0.1	2.4
[+2w, +6w]	0.001	0.002	0.3	0.5	0.5	7	0.06	0.02	10	0.1	0.1	2.4
[+6w, +2m]	0.001	0.002	0.3	0.5	0.5	7	0.06	0.02	2	0.1	0.1	2.4
[+2m, +4m]	0.005	0.012	0.1	1.5	0.5	7	0.06	0.02	2	0.1	0.1	4

Because parameters s_i, s_idand s_aare used to reflect therapy changes and/or therapy effects on the disease dynamics, they are in general time dependent. All these parameters may vary greatly during the flare cycle (changes in therapy, therapy failure or success), and can also vary greatly from patient to patient (stages of disease, general patient health).

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