NDLI: Kinetic modelling of large-scale metabolic networks

Please wait, while we are loading the content...

A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

HPC selection of models of DNA substitution

Toward real-time simulation of cardiac dynamics

Parameter inference for asynchronous logical networks using discrete time series

Modelling sperm behaviour in a 3D environment

Osteoporosis: a multiscale modeling viewpoint

Propagation models for computing biochemical reaction networks

Analysis of stochastic reaction networks with Markov reward models

Parallelized agent-based simulation on CPU and graphics hardware for spatial and stochastic models in biology

Computer simulation of Salmonella typhimurium accumulation within tumors

Reversible structures

Curvature analysis of cardiac excitation wavefronts

Multi-scale modelling of biological systems in process algebra with multi-way synchronisation

Kinetic modelling of large-scale metabolic networks

The singular power of the environment on stochastic nonlinear threshold Boolean automata networks

An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

Adapting rule-based model descriptions for simulating in continuous and hybrid space

A multiscale approach to modelling planar cell polarity in Drosophila wing using hierarchically coloured Petri nets

Logical modelling of haematopoietic cell fate reprogramming

Accelerating multiple target drug screening on GPUs

Coloured stochastic multilevel multiset rewriting

The phosphorylation of the heat shock factor as a modulator for the heat shock response

Evaluation of design strategies for time course experiments in genetic networks: the XlnR regulon in Aspergillus niger

Kinetic modelling of large-scale metabolic networks

Content Provider	ACM Digital Library
Author	Smallbone, Kieran Mendes, Pedro Stanford, Natalie J.
Abstract	A major result of the various genome programs has been an accumulation of complete genomic sequences and their associated annotation. These resources are extremely valuable to various fields of biology, not least metabolism and metabolic modelling. As these complete sequences have started appearing they have been used to derive lists of metabolic reactions that are catalysed by enzymes whose genes are identified in the genome sequence. These metabolic "reconstructions" are further interpreted as metabolic networks and several analyses can be derived from them. In the case of the popular model organism Saccharomyces cerevisiae the metabolic reconstruction is fairly advanced in terms of completeness and sophistication [2, 1]. On its own, a metabolic reconstruction can be analysed through a number of approaches: network analyses (clustering coefficients, betweeness centrality, etc.) provide metrics about the connectedness of the network; elementary flux mode analysis provides a unique decomposition of the network in minimal subsets that are capable of operating independently. By joining extra quantivative information about input and output fluxes, the network can also be studied using flux balance analysis [3]. These methods require little amount of molecular information, however they are only able to provide a restricted number of steady state properties of the system. In order to reveal the network's dynamic properties, kinetic models are required. Here we provide an account of our efforts towards costructing and analysing such large scale kinetic models of metabolism. Kinetic models describe the dynamic properties of reaction networks and are formulated based on the kinetic properties of the individual reactions/enzymes of the network. Traditionally the kinetics are determined through in vitro studies, which require purification of the enzymes involved. Studied in that way, each enzyme can be characterized by a detailed kinetic rate law --- each enzymatic reaction with a specific rate law. This is a problem because even for the best studied organisms, such as S. cerevisiae, the large majority of enzymes has never been studied, and so the rate laws and their parameter values are unknown. Systematic efforts to purify large numbers of enzymes and determine their precise kinetic properties are under way in our Centre, but this cannot scale all the way to the entire set of enzymes of the network. Nevertheless we have already determined such detailed kinetics for all enzymes of several individual metabolic pathways of yeast, and it is expected that many more will be produced in the future in our laboratory and others. Despite these experimental efforts it is clear that not all enzymes of an organism will be possible to assay in vitro (or even in vivo). This means that to create a full-genome metabolic kinetic model a different strategy must be applied. In order to overcome the obstacles described above, kinetic models of large-scale metabolic networks require the use of generic rate laws that can be applied for several different reaction types. These are empirical rate laws that should be able to describe the changes in rates of reaction in terms of the concentrations of its metabolites in an approximate way. Absolute precision is not possible, but the generic behaviour is expected to be captured in these rate laws. We have studied several types of these generic rate laws, such as mass action, lin-log, and convenience kinetics. The lin-log approach is the method that requires the least amount of effort and indeed it is feasible for this approach [4, 5] given a set of measurements of steady state concentrations of metabolites. However this method has poor extrapolation power away from the state used for calibration. Generic rate laws that display saturation are more likely to extrapolate to wider range of conditions and we have been using the convenience kinetics approach. The convenience kinetics rate law has a number of parameters that need to be estimated for each enzyme. An important class of parameters that need special consideration are the equilibrium constants of each reaction. These are constrained by the structure of the metabolic network: the overall equilibrium constants of two parallel metabolic routes (i.e. that start and end in common points, but that use different sets of reactions) must be the same. A special constrained optimisation approach allows us the entire set of equilibrium constants such that they are consisted with each other. By using the values of some equilibrium constants determined with precision (i.e. experimentally) for a few reactions, the other equilibrium constants can also be estimated. This then leaves two other classes of parameters: affinity constants (Michaelis constants, inhibition or activation constants) and limiting rates $(V_{m})$ which are estimated by a global fit using known values of fluxes and concentrations of metabolites. This approach produces a large-scale kinetic model of metabolism which should be seen as a hypothesis (or collection of hypotheses) about the dynamic behaviour of the metabolic network. It is important to recognize that such a model is very rough and has low "dynamic resolution" but is nevertheless an important starting point for further improvements. We have started to make our large-scale model of yeast more accurate by substituting the generic rate laws for precise rate laws for those enzymes that we have already studied in detail. Rounds of sensitivity analysis and experimentation are proposed to identify the enzymes that if studied in detail can best improved the accuracy of the model. The approximate low-accuracy model developed is therefore an important piece towards accurate large-scale metabolic models through this strategy proposed here.
Starting Page	5
Ending Page	6
Page Count	2
File Format	PDF
ISBN	9781450308175
DOI	10.1145/2037509.2037511
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-09-21
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in