# LinearMetabolic

LinearMetabolic | |
---|---|

State dimension: | 1 |

Differential states: | 4 |

Continuous control functions: | 3 |

Path constraints: | 1 |

Interior point inequalities: | 0 |

Interior point equalities: | 5 |

The **Linear Metabolic problem** is a generalized inverse optimal control problem formulated and investigated in [Tsiantis2018]**Author: ** *Tsiantis, Nikolaos; Balsa-Canto, Eva; Banga, Julio R***Journal: ** *Bioinformatics***Number: ** *14***Pages: ** *2433-2440***Title: ** *Optimality and identification of dynamic models in systems biology: an inverse optimal control framework***Volume: ** *34***Year: ** *2018*

. It tries to identify an a priori unknown objective function from data.

The problem is a generalization of the one studied by de Hijas-Liste et al. (2014), where it was considered as a standard optimal control problem (OCP). Here, we take the solution reference of the inner problem as the multi-objective OCP described in de Hijas-Liste et al. (2014), selecting a specific point of the resulting Pareto front. This case study is interesting, because it includes path constraints on the states and the inputs.

A 3-step linear metabolic pathway with mass action kinetics is considered. The differential states are metabolite concentrations, the time-dependent control functions are enzyme concentrations, and the model parameters are kinetic parameters in mass action expressions. Candidate objective functionals are the final time and the time-integral of the intermediate metabolite concentrations. The measurement function is a map to the values of the differential states and comprises measurements of metabolite concentrations. The differential equations are assumed to be fully known as standard mass action kinetics. An inequality path constraint is present (but potentially unknown in such settings) on the inner level and critical from a biological point of view: limitations due to molecular crowding impose an upper bound on the maximum total concentration of enzymes (controls) at any given time. Boundary conditions are fixed initial and terminal values of the metabolite concentrations.

## Mathematical formulation

The gIOC is given by

subject to subject to

Here the four differential states and three control functions stand for the metabolite concentrations () and the enzyme concentrations (). The two objective function candidates are the sum of intermediate metabolite concentrations and the transition time. The three model parameters are kinetic parameters in mass action expressions.

## Data

The following table gives different values for , componentwise for all four entries of as time series, for different combinations of weights and noise levels that were used to produce the synthetic data via solution of forward optimal control problems.

weights | parameters | noise | number of time intervals | final time | data |

[0.232, 0.768] | [1,1,1] | 0 | 20 | 4.3 |
[[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0, 0.215, 0.4298, 0.6446, 0.8593, 1.074, 1.2885, 1.503, 1.6591, 1.3381, 1.0792, 0.8704, 0.7021, 0.5662, 0.4849, 0.4846, 0.4844, 0.4842, 0.484, 0.4838, 0.4836], [0, 0.0, 0.0002, 0.0004, 0.0007, 0.001, 0.0015, 0.002, 0.0391, 0.36, 0.6186, 0.827, 0.9948, 1.1301, 1.1405, 0.92, 0.7423, 0.5989, 0.4832, 0.3899, 0.3147], [0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0001, 0.0004, 0.0008, 0.0013, 0.0019, 0.0729, 0.2935, 0.4715, 0.6152, 0.731, 0.8245, 0.9]] |

[0.232, 0.768] | [1,1,1] | 0.2 | 20 | 4.3 |
[[1., 0.9590812,0.82266497,0.95877308,0.60614596,1.20781224, 0.92205471,1.0215116 ,0.89080449,1.05747735,1.04019676,1.06665347, 0.78545416,1.17084962,1.08658091,1.01467309,0.91098074,1.10349004, 0.83411596,0.73187308,0.70399766], [0.,0.37493181,0.10514573,0.79448823,0.44666767,1.19676033,1.14532119,1.64453389,1.93572019,1.37435635,0.92790967,1.11928728, 0.52061388,0.43837628,0.31518386,0.50300858,0.52891948,0.74255261, 0.07589526,0.64051491,0.47184952], [ 0., 0.23680072, 0.05047157,-0.03770305, 0.34603556,-0.45259701,0.25340369, 0.25177705, 0.16210031, 0.34367395, 0.96197961, 1.10224845, 1.21656021, 1.38442255, 1.02149302, 0.68818982, 0.7921558 , 0.32238544, 0.30229594, 0.43876605, 0.22073986], [ 0.,-0.01591557,-0.12432336,-0.17669569,-0.21325853,-0.2489822 , -0.22989873,-0.0564741 ,-0.19961696,-0.16036058,-0.04386598, 0.25004781, -0.07279584, 0.09573081,-0.12329308,-0.03451486, 0.0173815 , 0.79895333, 0.8178261 , 0.53498205, 1.1021996]] |

[0,1] | [1,1,1] | 0 | 20 | 4.226 |
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0.211317, 0.422635, 0.633952, 0.845269, 1.05659, 1.2679, 1.47922, 1.69054, 1.90186, 1.95601, 1.58342, 1.2818, 1.03764, 0.839981, 0.685458, 0.685458, 0.685458, 0.685458, 0.685458, 0.685458], [0, 3.40252e-10, 2.02262e-09, 6.04129e-09, 1.32631e-08, 2.47434e-08, 4.23248e-08, 6.98021e-08, 1.16723e-07, 2.20847e-07, 0.103509, 0.476095, 0.777716, 1.02188, 1.21954, 1.36367, 1.10391, 0.893635, 0.72341, 0.58561, 0.474061], [0, 2.79389e-19, 2.05361e-18, 7.26553e-18, 1.82666e-17, 3.77412e-17, 6.87107e-17, 1.14894e-16, 1.82155e-16, 2.86358e-16, 3.73778e-11, 7.31532e-10, 5.75723e-09, 2.28945e-08, 7.3467e-08, 0.0103927, 0.270148, 0.480425, 0.65065, 0.78845, 0.9]] |

[0,1] | [1,1,1] | 0.1 | 20 | 4.226 |
[[1.,0.98392517,1.02825092,1.0127091 ,1.21814838,1.03987996, 1.17367563,1.0721497 ,1.11254303,0.91560159,0.92664064,0.93069442, 1.01285111,1.10017347,0.9952073 ,1.05616016,1.12564585,1.02743444, 0.93306189,1.04808763,0.87659083], [0.,0.19112834,0.6016258 ,0.70894548,0.86077117,0.95316772, 1.12244906,1.51535841,1.48548905,1.87282215,2.13181979,1.48523121, 1.14624017,1.06839771,0.8838448 ,0.72466348,0.61379013,0.70096009, 0.73549769,0.62452413,0.61907767], [0., 0.04514029,-0.06510522,-0.00304812,-0.01620013, 0.006101, 0.04992798, 0.29317191, 0.11305888, 0.2285637 , 0.26142926, 0.50457228, 0.87829835, 0.90900075, 1.32279588, 1.42225272, 1.18086941, 0.67720058, 0.61562944, 0.6339817 , 0.36613472], [0.,-0.13882966, 0.18200648,-0.06629627,-0.01496019, 0.21349797,-0.10124824,-0.05505019, 0.23235014, 0.09448396, 0.06059911,-0.12137716, -0.00781841, 0.04355511, 0.1369212 ,-0.13011339, 0.2027646 , 0.51512985, 0.65331917, 0.78324169, 0.81304223]] |

## Examples

The plots show noise free data for different weights from forward optimal control problems.

## References

[Tsiantis2018] | Tsiantis, Nikolaos; Balsa-Canto, Eva; Banga, Julio R (2018): Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics, 34, 2433-2440 |