TY - CHAP
T1 - Towards human cell simulation
AU - Spolaor, Simone
AU - Gribaudo, Marco
AU - Iacono, Mauro
AU - Kadavy, Tomas
AU - Komínková Oplatková, Zuzana
AU - Mauri, Giancarlo
AU - Pllana, Sabri
AU - Senkerik, Roman
AU - Stojanovic, Natalija
AU - Turunen, Esko
AU - Viktorin, Adam
AU - Vitabile, Salvatore
AU - Zamuda, Aleš
AU - Nobile, Marco S.
PY - 2019/3/31
Y1 - 2019/3/31
N2 - The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.
AB - The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.
KW - Agent-based simulation
KW - Big data
KW - Biochemical simulation
KW - Computational intelligence
KW - Constraint-based modeling
KW - Fuzzy logic
KW - High-performance computing
KW - Model reduction
KW - Multi-scale modeling
KW - Parameter estimation
KW - Reaction-based modeling
KW - Systems biology
UR - http://www.scopus.com/inward/record.url?scp=85063786885&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16272-6_8
DO - 10.1007/978-3-030-16272-6_8
M3 - Chapter
SN - 978-3-030-16271-9
T3 - Lecture Notes in Computer Science
SP - 221
EP - 249
BT - High-performance modelling and simulation for big data applications
A2 - Kołodziej , Joanna
A2 - González-Vélez, Horacio
PB - Springer
ER -