mandlik1

ia1_bind_forward

ia1_bind_forward

IPTG + ia1_ActiveTF > ia1_InactiveTF

ia1_bind_reverse

ia1_bind_reverse

ia1_InactiveTF > IPTG + ia1_ActiveTF

pp1_v1

pp1_v1

∅ > proSLS1

pp1_v2

pp1_v2

proSLS1 > ∅

pp2_v1

pp2_v1

∅ > proAUR1

pp2_v2

pp2_v2

proAUR1 > ∅

pp3_v1

pp3_v1

∅ > proSLS4

pp3_v2

pp3_v2

proSLS4 > ∅

pp4_v1

pp4_v1

∅ > proLACI

pp4_v2

pp4_v2

proLACI > ∅

pp5_v1

pp5_v1

∅ > proLAMDAR

pp5_v2

pp5_v2

proLAMDAR > ∅

pp6_v1

pp6_v1

∅ > proTETR

pp6_v2

pp6_v2

proTETR > ∅

Global parameters

Assignment rules

rs6 = 1.0 / (1.0 + pow(proTETR / tr4_Kd, tr4_h))

rs4 = 1.0 / (1.0 + pow(proLAMDAR / tr5_Kd, tr5_h))

AUR1 = pAUR1_strength * 1.0 / (1.0 + pow(proSLS1 / tr1_Kd, tr1_h))

LACI = p2_strength * 1.0 / (1.0 + pow(proLAMDAR / tr5_Kd, tr5_h))

SLS4 = pSLS4_strength * 1.0 / (1.0 + pow(proAUR1 / tr2_Kd, tr2_h))

rs5 = 1.0 / (1.0 + pow(proLACI / tr3_Kd, tr3_h))

rs1 = 1.0 / (1.0 + pow(proSLS1 / tr1_Kd, tr1_h))

rs2 = 1.0 / (1.0 + pow(proAUR1 / tr2_Kd, tr2_h))

ope1 = 1.0 / (1.0 + pow(ia1_ActiveTF / ia1_repression_Kd, ia1_repression_h))

SLS1 = pSLS1_strength * (1.0 + pow(proSLS4 / ta1_Kd, ta1_h) - 1.0) / (1.0 + pow(proSLS4 / ta1_Kd, ta1_h)) * 1.0 / (1.0 + pow(ia1_ActiveTF / ia1_repression_Kd, ia1_repression_h)) * 1.0 / (1.0 + pow(proLACI / tr6_Kd, tr6_h))

as1 = (1.0 + pow(proSLS4 / ta1_Kd, ta1_h) - 1.0) / (1.0 + pow(proSLS4 / ta1_Kd, ta1_h))

TETR = p3_strength * 1.0 / (1.0 + pow(proLACI / tr3_Kd, tr3_h))

LAMDAR = p1_strength * 1.0 / (1.0 + pow(proTETR / tr4_Kd, tr4_h))

rs3 = 1.0 / (1.0 + pow(proLACI / tr6_Kd, tr6_h))

Function definitions

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


Species:

Reactions:


Middle-click: pin/unpin nodes
Shift-click: pool/unpool species
Right-click: context menu

Apply alternate model layout to overlapping elements in current model:

log scales

y-axis min/max

x-axis min/max

Regulatory dynamics of network architecture and function in tristable genetic circuit of Leishmania: a mathematical biology approach.

  • Vineetha Mandlik
  • Mayuri Gurav
  • Shailza Singh
J. Biomol. Struct. Dyn. 2015; 33 (12): 2554-2562
Abstract
The emerging field of synthetic biology has led to the design of tailor-made synthetic circuits for several therapeutic applications. Biological networks can be reprogramed by designing synthetic circuits that modulate the expression of target proteins. IPCS (inositol phosphorylceramide synthase) has been an attractive target in the sphingolipid metabolism of the parasite Leishmania. In this study, we have constructed a tristable circuit for the IPCS protein. The circuit has been validated and its long-term behavior has been assessed. The robustness and evolvability of the circuit has been estimated using evolutionary algorithms. The tristable synthetic circuit has been specifically designed to improve the rate of production of phosphatidylcholine: ceramide cholinephosphotransferase 4 (SLS4 protein). Site-specific delivery of the circuit into the parasite-infected macrophages could serve as a possible therapeutic intervention of the infectious disease 'Leishmaniasis'.
The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000584) Biomodels notes: Figure 5 of the reference publication has been reproduced using SBML odeSolver. The plots were generated using Gnuplot. JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. No additional changes were made.