Identification of optimal strategies for state transition of complex biological networks

2017 ◽  
Vol 45 (4) ◽  
pp. 1015-1024 ◽  
Author(s):  
Meichen Yuan ◽  
Weirong Hong ◽  
Pu Li
Keyword(s):  
Biological Networks ◽  
State Transition ◽  
Optimal Strategies ◽  
Optimization Method ◽  
State Transitions ◽  
Decision Variable ◽  
Parameter Perturbation ◽  
Time Optimal ◽  
Method Optimization ◽  
Complex Biological Networks

Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.

Target controllability with minimal mediators in complex biological networks

Genomics ◽  
2020 ◽  
Vol 112 (6) ◽  
pp. 4938-4944 ◽  
Author(s):  
Ali Ebrahimi ◽  
Abbas Nowzari-Dalini ◽  
Mahdi Jalili ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Complex Biological Networks

scholarly journals Viscoelasticity and cell jamming state transition

2021 ◽  
Author(s):  
Ivana Pajic-Lijakovic ◽  
Milan Milivojevic
Keyword(s):  
Residual Stress ◽  
Wound Healing ◽  
Cell Migration ◽  
State Transition ◽  
State Transitions ◽  
Collective Cell Migration ◽  
Biological Processes ◽  
Cell Mobility ◽  
Controversial Topic ◽  
The Impact

Although collective cell migration (CCM) is a highly coordinated migratory mode, perturbations in the form of jamming state transitions and vice versa often occur even in 2D. These perturbations are involved in various biological processes, such as embryogenesis, wound healing and cancer invasion. CCM induces accumulation of cell residual stress which has a feedback impact to cell packing density. Density-mediated change of cell mobility influences the state of viscoelasticity of multicellular systems and on that base the jamming state transition. Although a good comprehension of how cells collectively migrate by following molecular rules has been generated, the impact of cellular rearrangements on cell viscoelasticity remains less understood. Thus, considering the density driven evolution of viscoelasticity caused by reduction of cell mobility could result in a powerful tool in order to address the contribution of cell jamming state transition in CCM and help to understand this important but still controversial topic. In addition, five viscoelastic states gained within three regimes: (1) convective regime, (2) conductive regime, and (3) damped-conductive regime was discussed based on the modeling consideration with special emphasis of jamming and unjamming states.

scholarly journals Optimization Algorithm for Lifetime of Mobile Wireless Sensor Networks Based on Grid

2019 ◽  
Vol 15 (01) ◽  
pp. 44
Author(s):  
Ren Song
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Control Method ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
Wireless Sensor ◽  
Premature Failure ◽  
Method Optimization ◽  
Mobile Wireless Sensor ◽  
Node Energy

To avoid premature failure due to excessive energy consumption of some nodes in the network, the node energy consumption problem was considered. Network life was maximized. For the problem of node energy consumption, multiple methods such as the shortest path method, optimization method, and power control method were used to solve the problem of optimization of the survival time of the wireless sensor network in different scenarios and improve the network lifetime. The results showed that the sub-gradient algorithm could balance the node energy consumption and the number of neighbor nodes and extend the maximum network lifetime. Therefore, under certain conditions, the algorithm is better than the algorithm using fixed transmission power.

Optimal strategies for discovering new species in continuous time

1989 ◽  
Vol 26 (04) ◽  
pp. 695-706
Author(s):  
Gerold Alsmeyer ◽  
Albrecht Irle
Keyword(s):  
New Species ◽  
Optimal Stopping ◽  
Continuous Time ◽  
Optimal Strategy ◽  
Stopping Time ◽  
Optimal Strategies ◽  
Distinct Species ◽  
Natural Generalization ◽  
General Point ◽  
Time Optimal

Consider a population of distinct species Sj , j∈J, members of which are selected at different time points T 1 , T 2,· ··, one at each time. Assume linear costs per unit of time and that a reward is earned at each discovery epoch of a new species. We treat the problem of finding a selection rule which maximizes the expected payoff. As the times between successive selections are supposed to be continuous random variables, we are dealing with a continuous-time optimal stopping problem which is the natural generalization of the one Rasmussen and Starr (1979) have investigated; namely, the corresponding problem with fixed times between successive selections. However, in contrast to their discrete-time setting the derivation of an optimal strategy appears to be much harder in our model as generally we are no longer in the monotone case. This note gives a general point process formulation for this problem, leading in particular to an equivalent stopping problem via stochastic intensities which is easier to handle. Then we present a formal derivation of the optimal stopping time under the stronger assumption of i.i.d. (X 1 , A 1) (X2, A2 ), · ·· where Xn gives the label (j for Sj ) of the species selected at Tn and An denotes the time between the nth and (n – 1)th selection, i.e. An = Tn – Tn– 1. In the case where even Xn and An are independent and An has an IFR (increasing failure rate) distribution, an explicit solution for the optimal strategy is derived as a simple consequence.

Research on Optimization of Ground-Coupled Heat Pump Systems under Specific Constraint Conditions

2014 ◽  
Vol 953-954 ◽  
pp. 673-679
Author(s):  
Yang Yang Wang ◽  
Ping Fang Hu ◽  
Fei Lei ◽  
Na Zhu ◽  
Tian Hua Wu ◽  
...  
Keyword(s):  
Heat Pump ◽  
Optimization Problem ◽  
Design Method ◽  
Optimization Method ◽  
Simulation Software ◽  
Design Parameters ◽  
Decision Variable ◽  
Economic Optimization ◽  
Ground Coupled Heat Pump

A design method for ground-coupled heat pump (GCHP) systems with specific constraint conditions is proposed. The total borehole number, borehole depth, borehole space and average velocity of fluid in the U-tube are considered as variables in the optimization problem. The optimization problem of four variables is transformed into that of single decision variable. A case study, which includes different schemes for designing GCHP systems of an office building and the corresponding economic analysis, is performed with the aid of simulation software. The result shows that optimal design parameters could be found in an economic optimization problem with specific constraint conditions. Additionally, design parameters may have a notable influence on the energy consumption of circulating pumps. The optimization method in this paper could be utilized by engineering designers for reference.

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