model parameters (9)

elife-27038-v2.xml

10.7554/eLife.27038.020

Table 1.Model Parameters.

	Parameter	Unit	Description	Acceptable range		Default
	Parameter	Unit	Description	Max.	Min.	Default
BOR	ϵB	-	Time constant for transporter regulation	-	-	1
	αB	μm s^-2	Production rate of transporter activity	4.9×102	3.7×10-9	2.0×10-1
	ξB	s^-1	Basal degradation rate		1.6×10-6	7.6×10-2
	kB	μM	Boron concentration for half-maximum in Hill’s function	1000	1	600
	dB	-	Amplitude of increased degradation rate by boron	100	0	50
	nB	-	Hill’s coefficient	-	-	2
	cB	μM	Boron concentration at which the flux reaches its half-maximum value	∞	500	1000
NIP	ϵN	-	Time constant for transporter regulation	-	-	1
	αN	μm s^-2	Production rate of transporter activity	4.9×102	3.7×10-9	2.0×10-1
	ξN	s^-1	Basal degradation rate		1.6×10-6	7.6×10-2
	kN	μM	Boron concentration for half-maximum in Hill’s function	1000	1	20
	nN	-	Hill’s coefficient	-	-	2
Cell size	l⁢c	μm	Cell width	20	5	10
	l⁢w	μm	Cell wall width	2	0.2	0.5
	h⁢c	μm	Cell height	150	5	20
Other	p	μm s^-1	Membrane background permeability of boron	8×10-2	2.3×10-3	3×10-2
	a	μm s^-1	Xylem loading rate (in the last cell)	500	0	0.5
	c0	μm	Boron concentration in medium	5000	0	300

elife-27430-v2.xml

10.7554/eLife.27430.005

Table 1.Model parameters.

Subject’s behavior on the first free choice of each session is described by 13 free parameters. Three of these parameters (R0, α1 and α∞) describe the learning process and do not vary with horizon or uncertainty condition. Ten of these parameters (A, B and σ in the different horizon and information conditions) describe the decision process. All parameters are estimated for each subject in each stimulation condition and the key analysis asks whether parameters change between vertex and RFPC stimulation.

Parameter	Horizon dependent?	Uncertainty dependent?	TMS dependent?
prior mean, R0	no	no	yes
initial learning rate, α1	no	no	yes
asymptotic learning rate, α∞	no	no	yes
information bonus, A	yes	n/a	yes
spatial bias, B	yes	yes	yes
decision noise, σ	yes	yes	yes

elife-27694-v1.xml

10.7554/eLife.27694.024

Appendix 1—table 1.Model parameters.

Parameter	Description
pT	Standard SICCT Sensitivity. Probability of positive tuberculin test for R,I individuals at standard definition.
1−pFP	Standard SICCT Specificity. Probability of negative tuberculin test for S,O,R,I individuals at standard definition (1 - probability of a false positive pFP).
pT′	Severe SICCT Sensitivity. Probability of positive tuberculin test for R,I individuals at severe definition.
1−pFP′	Severe SICCT Specificity. Probability of negative tuberculin test for S,O,R,I individuals at severe definition. (1− probability of false positive pFP′).
pRI	Slaughterhouse detection. Relative sensitivity of finding lesioned or culture positive animals (O,OV1,OV2,R,RV,I,IV status) under routine inspection compared to reactor inspection.
TO	Occult Period. Mean length of time that animals are undetectable (occult) to SICCT test.
TR	Reactive Period. Mean length of time between infection and animals becoming infectious.
β	Transmission parameter associated with density dependence (rate per day, dimensions change with q).
q	Transmission parameter measuring the strength of density dependence (range 0–1)
χ1	Transmission parameter measuring infectious pressure per susceptible per year in PTI 1
χ2	Transmission parameter measuring infectious pressure per susceptible per year in PTI 2
χ4	Transmission parameter measuring infectious pressure per susceptible per year in PTI 4
H	Herd size. Sampled from empirical distribution and maintained constant through individual simulations
HM=165	Constant equal to mid-point of range of herd sizes within study population. Used to transform density dependence of force of infection.
pD	DIVA sensitivity Probability of positive DIVA test in infected vaccinates (OV1,OV2,RV,IV).
1−pDFP	DIVA specificity Probability of negative DIVA test for uninfected vaccinates V1,V2 (V1,V2 probability of a false positive pDFP)
εS	Vaccine Efficacy Reduction in risk of infection for susceptible vaccinates (status V1)
εI	Vaccine Efficacy Reduction in risk of infectiousness for infected vaccinates (RV,IV) for SORI model, and in addition (OV1,OV2) for SOR model.
TV	Protective Period. Mean length of time that animals are protected by BCG vaccination (status V1)

elife-28683-v2.xml

10.7554/eLife.28683.021

Table 1.Model parameters

Parameter	Abbreviation	Value
Size of each pool of plants^*	Pool	Variable (1–1000)
Number of replicates^*	Rep	Variable (5 or 100)
Length of each cycle^*	Days	Variable (14-49)
Number of cycles^*	Cyc	Variable (4 or 10)
Initial proportion of Fix⁺ cells^*	x	Variable (1 or 0.1)
Maximum number of new nodules/plant/day^†	λ_max	0.44
Coefficient for the auto-regulation of nodulation in nodulation kinetics^†	a₁	0.03
Coefficient for time-decay in nodulation kinetics^†	a₂	0.006
Lag for time-decay in nodulation kinetics^†	a₃	2
Growth rate of bacteria within nodule^†	r	1.95
Fitness cost of nitrogen fixation^‡	c	0
Sanctions for Fix^-‡	s	1.65
Day at which additional sanctions begin^‡	ds	17
Nodule carrying capacity^‡	K	1.4 × 10⁸

^*parameters varied in the simulations

^† experimentally measured parameters

^‡parameters inferred from experimental data

elife-30743-v2.xml

10.7554/eLife.30743.007

Table 2.Model parameters.

The mean value and standard deviation within the parameter ensemble (where applicable) are indicated for each parameter.

Parameter	Description	Main model	Parameter values from Corson and Siggia (2012)	Model with EGF/Notch coupling (Figure 7)	Alternate models of Figure 2—figure supplements 3 and 4
Model parameters
m→0	Bias towards the default 3° fate	0.47 ± 0.03	0.39 ± 0.03	0.51 ± 0.03	0.5
∠ m→0	Bias towards the default 3° fate	210 (fixed)	210 (fixed)	210 (fixed)	210
‖ m→1 ‖	Response to EGF*	3.87 ± 0.52	4.60 ± 0.98	3.86 ± 0.52	6
∠ m→1	Response to EGF*	−21 ± 8	−30 (fixed)	−9 ± 6	−30
‖ m→2 ‖	Response to Notch*	6.25 ± 1.28	5.97 ± 1.07	7.72 ± 1.31	6
∠ m→2	Response to Notch*	73 ± 3	90 (fixed)	73 ± 5	90
1τ	Characteristic time scale of dynamics	2.02 ± 0.16	2.18 ± 0.30	2.23 ± 0.25	2
2D	Noise level	0.12 ± 0.02	0.12 ± 0.03	0.14 ± 0.02	0.14
γ	Shape of EGF gradient	0.23 ± 0.02	0.16 ± 0.03	0.22 ± 0.02	0.2
α	Relative strength of autocrine signaling	1.08 ± 0.11	1.14 ± 0.17	1.24 ± 0.22	1/2 (Figure 2—figure supplement 3) or 0 (Figure 2—figure supplement 4)
n0	Threshold for lateral signaling	−1.20 ± 0.09	−1.23 ± 0.10	−1.29 ± 0.11	0.25
∠ n→1^†	Threshold for lateral signaling	−46 ± 3	−48 ± 3	−46 ± 3	−45
Signal levels for experiments in the training set
	Ectopic EGF level in the lin-15 mutant	0.42 ± 0.11	0.55 ± 0.25	0.51 ± 0.20	N/A
	EGF level in the JU1100 overexpression line	4.18 ± 0.49	7.39 ± 1.36	4.84 ± 0.67	N/A
	EGF level in the lin-3(e1417) hypomorph	0.28 ± 0.03	N/A	0.28 ± 0.03	N/A
	Ectopic Notch activity in the JU2064 line	0.05 ± 0.03	N/A	0.04 ± 0.02	N/A

*While the vectors m→1 and m→2 are parameterized by their Cartesian coordinates, with Gaussian priors, the mean ±SD of their norm and orientation are shown for convenience. Angles (symbolized by ∠) are relative to the horizontal axis of the fate plane and in degrees.

^†As in (Corson and Siggia, 2012), the sharpness of the threshold for lateral signaling was set to a default value, ‖ n→1 ‖=3, so that its orientation alone is fit; a similar value for the norm of n→1 is recovered when it is not imposed in the fit.

elife-33752-v1.xml

10.7554/eLife.33752.032

Appendix 1—table 1.Model Parameters.

Top to bottom: α, β sigmoid parameters; φ connection gains; Φ constants subtracted from given weight matrices (e.g. PC to PC connections) to yield global inhibition; bath parameters; range thresholds for object encoding; l learning rates for simulation 5; S sparseness of connections for reservoir PCs; σ_ρ, σ_ϑ spatial dispersion of the rate function for BVCs. The additive constant (σ_ρ = (r + 8) * σ₀) corresponds to half the range of BVC grid and prevents σ_ρ from converging to zero close to the agent. N_i population sizes. Products of numbers reflect geometric and functional aspects. E.g. receptive fields of PCs tile 2 × 2 m arena with 44 × 44 cells. Polar grids are given by 16 radial distance units (see A.2) and 51 angular distance units. For the transformation circuit this number is multiplied by the number of transformation sublayers, that is 20.

α	5
β	0.1
α_IP	50
β_IP	0.1
φ_PWb-TR	50
φ_TR-PWb	35
φ_TR-BVC	30
φ_BVC-TR	45
φ_HD-HD	15
φ_HD-IP	10
φ_HD-TR	15
φ_HDrot	2
φ_IP-TR	90
φ_PC-PC	25
φ_PC-BVC	1100
φ_PC-PRb	6000
φ_BVC-PC	440
φ_BVC-PRb	75
φ_PRb-PC	25
φ_PRb-BVC	1
φ_GC-PC	3
φ_PWo-TR	60
φ_TR-PWo	30
φ_TR-OVC	60
φ_OVC-TR	30
φ_PC-OVC	1.7
φ_PRo-OVC	6
φ_PC-PRo	1
φ_OVC-PC	5
φ_OVC-oPR	5
φ_PRo-PC	100
φ_PRo-PRo	115
φ_inh-PC	0.4
φ_inh-BVC	0.2
φ_inh-PRb	9
φ_inh-PRo	1
φ_inh-HD	0.4
φ_inh-TR	0.075
φ_inh-TRo	0.1
φ_inh-PW	0.1
φ_inh-OVC	0.5
φ_inh-PWo	1
Φ_PC-PC	0.4
Φ_BVC-BVC	0.2
Φ_PR-PR	9
Φ_HD-HD	0.4
Φ_OVC-OVC	0.5
Φ_PRo-PRo	01
PW_bath	0.1
PW_bath	0.2
TR_bath	0.088
Object enc. threshold	18 cm
Object enc. Threshold (3.1)	36 cm
l_GC-resPC	0.65*10^−5
l_resPC-BVC	0.65*10^−5
l_BVC-resPC	0.65*10^−5
S_GC-resPC	3%
S_resPC-resPC	6%
σ_ρ	(r + 8) * σ₀
σ₀	0.08
σ_ϑ	0.2236
N_PC	44 × 44
N_BVC	16 × 51
N_TRb/o	20 × 16×51
N_OVC	16 × 51
N_PRb/o	Dependent on simulation environment
N_PWb/o	16 × 51
N_IP	1
N_HD	100
N_GC	100 per module
N_reservoir	437

elife-42646-v2.xml

10.7554/eLife.42646.047

Appendix 1—table 1.Model parameters.

Parameters for the force balance calculation of the biomechanical model are identical to previous work (Sütterlin et al., 2017) and are not listed.

Description	Parameter	Value	Reference/Explanation
Biomechanical model parameters
Biomechanical calculation step.	Δt	36 s	(Sütterlin et al., 2017)
Seconds per simulation step.	tsimstep	3600 s [simstep]−1	(Sütterlin et al., 2017)
Optimal overlap for obstacle cells.	δolobstacleCells	0.5	Determined by parameter scan to create a tight barrier to cell movement.
Optimal overlap for retinal cells.	δolmax	0.85	(Sütterlin et al., 2017)
Initial distance between daughter cells.		0.005μm	(Sütterlin et al., 2017)
Initial condition parameters
Initial radius of eye globe.	Rinit	100μm	Estimated from preparations of hatchling eyes.
Minimal displacement threshold.	μ	0.2μm	Determined by parameter scan to generate even initial cell distribution.
Simulation parameters
Retinal cell radius.	r	3.5μm	Estimated from histological sections.
Width of the stem cell domain.	w	25μm	Estimated from histological sections.
Overlap threshold beyond which cell cycle is arrested.	δol\_threshold	0.4	Value for inducer growth mode. Estimated from parameter scan to minimize density-dependent cell cycle arrest.
		0.2	Value for responder growth mode. Estimated from parameter scan to maximize density-dependent cell cycle arrest without completely suppressing division.
Minimal cell cycle length.	tcellCycle	24 h	Chosen to produce a plausible biological growth rate.
Probability of cell division.	pdivision	126 h−1	Chosen to produce a plausible biological growth rate.
		152 h−1	Value for ventral lineages with differential behavior.
Growth rate of the eye radius (only in responder growth mode).	cR	6.94⋅10-5μm s−1	Chosen as a small value to ensure quasi-static growth within the biologically plausible growth rate range.

elife-48478-v1.xml

10.7554/eLife.48478.003

Table 1.Model Parameters.

Parameter	Description	Value
γ	Tension	2 pN µm
ϵ	Width of phase field	2 µm
BS	Cell area conservation strength	10 pN/µm²
ξ	Friction coefficient	10 pN s/µm²
n	Hill coefficient of protrusive force	3
ka	Activation rate	10 s^-1
Ka	Activation threshold	1 µM
b	Basal activation rate	0.1 s^-1
At	Total activator concentration	2 µM
d1	Basal degradation rate	1 s^-1
d2	Degradation rate from inhibitor	1 µM^-1s^-1
c1	Inhibitor degradation coeffecient	1
c2	Inhibitor activation coefficient	15
τ	Time scale of negative feedback	10 s
DA	Activator diffusion coefficient	0.5 µm²/s
DR	Inhibitor diffusion coefficient	0.5 µm²/s
σ	Noise intensity	0.01 µM²/µm²/s
Δ⁢t	Time step	0.001 s
n,m	Space grid size	256,256
Lx,y	Space size	50,50 µm

elife-50963-v2.xml

10.7554/eLife.50963.031

Appendix 1—table 1.Model parameters.

kinetic parameter	value	reference
k₃	11 μM^-1s^-1	Pollard, 1986 and this work
k_-3	1 s^-1	Pollard, 1986
	0.58 s^-1	this work
k₄	40 μM^-1s^-1	this work
k_-4	0.75 s^-1	this work
k₁	11 μM^-1s^-1	Courtemanche and Pollard, 2013
k_-1	50 s^-1	Courtemanche and Pollard, 2013
	5 s^-1	Pernier et al., 2016