TY - JOUR
T1 - In silico model-based inference
T2 - An emerging approach for inverse problems in engineering better medicines
AU - Klinke, David J.
AU - Birtwistle, Marc R.
N1 - Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/8/13
Y1 - 2015/8/13
N2 - Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.
AB - Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.
UR - http://www.scopus.com/inward/record.url?scp=84939232534&partnerID=8YFLogxK
U2 - 10.1016/j.coche.2015.07.006
DO - 10.1016/j.coche.2015.07.006
M3 - Review article
AN - SCOPUS:84939232534
SN - 2211-3398
VL - 10
SP - 14
EP - 24
JO - Current Opinion in Chemical Engineering
JF - Current Opinion in Chemical Engineering
ER -