Modelling Biochemical Dynamics using Time-Varying S-Systems (BioSysBio 2009)

W-H Huang et al. (presented by F-S Wang)
National Chung Cheng University

Almost no lit has so far talked about modelling power-law models with time-varying parameters. In their model formulation, they used a time varying S-Sytem model. The rate coefficients and the kinetic orders are the time-varying parameters. Several basic functions such as block pulse functions, Lagrange polynomials, and orthogonal polynomials can be used to estimate the time-varying parameters. The model parameters for each time scale are constants.

There are two main challenges to parameter estimation: ODE solving and optimization. They developed a modified collocation approach, which is similar to the conventional method except for the approximation technique. Evolutionary algorithms can be applied to overcome drawbacks to optimization using gradient-based methods. They propose a global-local search method. They also describe Hybrid differential evolution (HDE). They did both wet and in silico experiments. For the latter, they found that the time-varying model fits the experiments very closely, much better than the time-invariant models. The wet-lab study was a kinetic model of ethanol fermentation using mixed sugars. The same conclusion, that the time-invariant model did not closely follow the experiment, while the time-varying one did, was found for this experiment.

DEs including constant parameters are commonly used to model biochemical systems. Such a time-invariant model cannot cover all dynamic behaviour – a time-varying S-system model has been developed by them to overcome these limitations. This model is a close fit with experimental data.

Tuesday Session 1
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Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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Using Control Theory to Elucidate Biological Signalling Networks (BioSysBio 2009)

M A J Roberts et al.
University of Oxford

Personal comment: I think the title might have changed, but I was too slow on the title slide to get it.

His research focuses on chemotaxis pathways. In E.coli chemotaxis, the signal is sensed by MCP and changes the rate of CheA autophosphorylation. CheA can phosphotransfer to CheY and CheB. CheY-P interacts with motor leading to motor switching and direction changing of the bacterium. CheB-P demethylates MCP resulting in adaption (memory). CheR methylates the receptor. The pathway is less well understood in other bacteria. There are often multiple homologues and therefore have a higher complexity. One example is R.sphaeroides, which has two main chemotaxis operons: CheOp2, and CheOp3. But they don't know how the pathway works or how it is connected. He's working on figuring this out without doing all possible interactions in vitro to work it out. He'll do this by creating models for all possible connections and then invalidate some of them.

This chemotaxis system is useful experimentally as you can measure the live output from cells using cell tethering. They constructed sensing models where the ligand is able to directly or indirectly interact with both MCPs and Tlps. There are a number of parameters that aren't known, and were estimated by fitting wild-type data. You can vary the experimental parameters by 10% you still get models that fit WT data pretty well.

The next part of the model is transduction. They experimentally determined the parameters. Finally is the motor binding step, where a simple mechanism for binding is assumed. So they have a set of models which can all represent WT data. They want to maximize the magnitude of the difference between the model outputs in order to discriminate "best" betwen the two models. This is achieved using linearized model equations around the steady state.

The differences between the models under the initial conditions were quite small. So they simulated these in silico to try to get large differences in expression. For example, overexpressing CheY4 has little effect on the WT, so only choose those models that behave similarly. Further tests are performed to get down to a final model. Of course, other un-modelled reactions may also be correct, so they're looking at extending the approach to find other possibilities.

Monday Session 2
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Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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Analyzing Genome-Scale Metabolic Networks (BioSysBio 2009)

D Fell
University of Oxford

Steve Oliver mentioned that David Fell has invented a number of control analysis coefficients. However, more recently has been working on the structure of networks. You get from genes to metabolic reactions via proteins, protein complexes, enzymes (and therefore EC numbers). He'll concentrate on talking about how to navigate from the genotype to the phenotype. Where does the data come from when building genome-scale metabolic networks? BioCyc, KEGG, IntEnz, EXPASY Enzyme, or Brenda. Alternatively, you can use an annotation tool such as RPS-Blast with PRIAM signatures. In principle, this creates a list of the reeactions encoded in the genome sequence.

You can represent a networks as a matrix with the rows for the metabolites and columns for the changes in states. If a metabolic network is at a steady state, it satisfies the relationship N.v = 0, where N is the stoichiometry matrix. Cannot solve the equation for unique values of v (the rate), but can find out some things about it – there is partial information there, e.g. whether or not reactions can have nonzero values for the reaction rate.

In the analysis approach, it is assumed that: the reaction list is available that has been turned into a stoichiometry matrix; the external metablites – nutrients, waste products, and biomass precursors for growing cells – have been identified; and a third that I, unfortunately, missed. Some quality checks are performed to ensure that the given reactions can actually exist at a steady state. There are problems if, for example, there are reactants with no source (orphan metabolites). The second quality check is to: prune dead reactions, orphan metabolites – or fix them; then check for unemployed enzymes; check that individual reactions are stoichiometrically consistent; check the stoichiometric consistency of the model. More information at Gevorgyan et al Bioinformatics 24, 2245-2251 (2008). He says it helps to recognize that reactions are statements about the composition of compounds, irrespective of whether or not you know the atomic composition.

If you take the KEGG database (either full or subset of it), almost 7% of the reactions are unbalanced. Applications of structural analysis are numerous. He specifically mentions: null space for potentially active or definitely inactive reactions; elementary modes for finding all routes through a network; linear programming; damage analysis; enzyme subsets (functional modules); sets of minimal nutrients that would allow an organism to produce all of its biomass precursors. This is even if we cannot get information about all reaction rates.

They're working on Arabidopsis metabolism. They have extracted 1646 metabolites and 1742 reactions from the AraCyc annotation. Then they removed problematic reactions, leaving 1281 metabolites and 1433 reactions. Then orphans and dead reactions are removed, making 611 / 878. this brings it to the size of the working core of the E.coli model. This core is able to account for the synthesis of the major biomass precursors. Minimal solutions accounting for the growth of heterotrophic culture cells on glucose contain fewer than 230 reactions. This number is quite similar to the minimal set of enzymes required in other organisms (for creating the biomass precursors).

To apply the model, they're doing three things. 1. carrying out a proteomics survey to determine the subset of enzymes expressed in the cells. 2. model suggests that variable ATP demands can be met with little alteration of the minimal set of enzymes. 3. Flux changes in response to variable ATP requirements are confined to a relatively small sub-group of reactions. They plan to theoretically and experimentally test this.

They've also been annotating the S.agalactiae genome. It's a gram-positive bacterium that can be fatal in mothers/newborns in cows. PRIAM often gives multiple predictions for a single gene, so you have to prune out surplus reactions. The results lead to a number of reactions, but not all enzymes in this case are "employed". To optimize the metabolic reconstruction, they aimed to enable proline and lactose metabolism in the model. Solutions were found by simulated annealing approach, which produced optimized models that synthesized proline and consumed lactose. The outcome for proline found that 1.5.1.12 was a missing enzyme. Adding it created 6 more reactions in the model.

They then looked at some transcript arrays that have been done on this bacterium, and found two leading candidates for the missing proline enzyme and one clear candidate for the following step, out of the six genes that might have been involved.

Tools are available to analyze genome-scale models, but there are shortcomings in the current knowledge of metabolism and its representation in databases. Functinal assessment of predicted networks can complement bioinformatic approaches.

He also mentioned a Systems Biochemistry meeting at the University of York, March 22-24 2010. It will cover the systems analysis of metabolism, signallilng and control from a systems perspective, and systems approaches to health and disease.

Personal Comments: He had a very nice breakdown of the types of unbalanced reactions in KEGG in a table in his slides. It was quite surprising and enlightening – I didn't realize any such reactions would get through into KEGG. Thanks! A very good invited talk: well paced, clearly explained.

Monday Session 2
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http://conferences.theiet.org/biosysbio

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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Quantitative Modeling of Transcription Initiation in Bacteria (BioSysBio 2009)

M Djordjevic
Arkansas State University and Arkansas Biosciences Institute

Starts witha nice introduction into RNA polymerase (RNAP). There are a number of stages of transcription by RNA polymerase. The first step in transcription initiation, which he is interested in, is the formation of the open complex. How is the open complex formed? Even after 20 years of research, this question still hasn’t been completely answered. Using their biophysical model, they want to identify some of the quantities related to transcription initiation that are optimized by the design of RNAP and genomic sequence.

Recent findings may help us understand what happens: firstly, a bioinformatics study shoes that the region of ~15bps immediately upstram of transcription start site is prone to to melting; secondly, single molecule experiments show that the promoter region is melted in at least one step. Why is the entire ~15bp region prone to melting? It could be an artifical consequence of the fact that only the upstream -10 region is prone to melting, while the rest of the bubble is not prone to melting – has the same melting energy as random DNA elements. Therefore in the first step, only the -10 region would be melted through thermal fluctuations facilitated by RNAP-ssDNA interactions. This first step has to be rate limiting (from the single-molecule experiment). The second step is where the bubble extends towards the transcription start site. There is very good agreement with experimental data. This is the first quantitative model of open complex formation. The results strongly support the qualitative hypothesis. The model allows the efficient analysis of kinetic properites of DNA sequences on a whole-genome scale.

Is RNAP kinetically trapped at many locations in the genome? That is, does it bind with high affinity but with a low rate of transcription initiation? Such promoters are called cryptic promoters. If not, how is the RNAP and the genomic sequence designed to prevent this? The existence of cryptic promoters has been mentioned as a major cause for false positives in both experimental and computational studies. There is no a priori reason for why binding affinity and the rate of transcription initiation should be related to each other.

The did an experiment with E.coli, which found that as they go to higher binding affinities, most (or all) of these strong binders correspond to functional promoters.  Good correlation between the binding affinity and the rate of transcription initiation is entirely dependent upon the level of RNAP protein domains. The good correlation is not due to the genome sequence. However, is this good correlation due to some generic properties of DNA binding domains? Subsitute specific binding domains with those of different DNA binding proteins. They find that interaction domains of RNAP are hardwired so as to ensure the evasion of crypic promoters.

Is RNAP and/or genomic sequences designed to maximize the rate of transcription from strong promoters? The calculated the difference between maximal transcription activity and average transcription activity for intergeneic sequences. This led to the conclusion that the maximization of rates of transcription for strong promoters is entirely at the level of protein-DNA binding domains, and not at the level of the DNA sequence.

They developed a quantitative model of open complex formation of RNAP, and used it to infer some of the design principles behind transcription initiation by bacterial RNAP.

Monday Session 1
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

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3 Bioinformatics Research Associate Positions: Newcastle University

There are three bioinformatics jobs (one in pure bioinformatics, one in network analysis, and another in modelling/mathematical biology) currently available within CISBAN, an interdisciplinary centre studying the systems biology of ageing and nutrition. The full particulars are posted both on Nature Jobs and on the Newcastle University Job Vacancies web pages.

Below are links to the various job advertisements, as well as summaries of the jobs themselves. This is a summary of the three Nature Jobs postings, put together on a single page for easy perusal. The closing date for all of these positions is 11 January 2008. This is a great opportunity, though I may be speaking from a biased perspective as I work at CISBAN and find it an interesting and challenging workplace.

1. Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health

   Research Positions

   Level F £25,134 – £32,796 p.a.
   Level G: £33,779 – £40,335 p.a.

   We seek scientists to join CISBAN, an exciting new research centre established following a major award (£6.4m) from BBSRC and EPSRC,
   to participate in studies of the mechanisms responsible for ageing and
   how they are affected by nutrition. Ageing is recognised
   internationally as a ‘grand challenge’ and is a field prioritised for
   growth. This post offer opportunities to work in an intensely
   multidisciplinary, world-class centre and contribute to the development
   and application of systems science.

   Research Associate (Bioinformation/Computing Scientist – Applications)

   To
   develop and maintain the computing software and hardware infrastructure
   for systems biology, including a central web portal integrating
   applications for data capture, storage and visualisation and high
   performance computing systems and databases, including a large Linux
   cluster.

   Job reference: A1091R

   Posts are tenable until 30 September 2010.

   Enquiries for the post may be directed to Dr Anil Wipat, School of Computing Science (email: anil.wipat@ncl.ac.uk)
   Further particulars for this post can be found on the University’s web page at http://www.ncl.ac.uk/vacancies/list.phtml?category=Research.

   Applications should be submitted by 11 January 2008 to Professor Tom Kirkwood, CISBAN Director,
   Institute for Ageing and Health, Henry Wellcome Laboratory for
   Biogerontology Research, Newcastle University, Newcastle upon Tyne NE4 6BE (email: tom.kirkwood@ncl.ac.uk).
   Committed to Equal Opportunities

2. Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health

   Research Positions

   Level F £25,134 – £32,796 p.a.
   Level G: £33,779 – £40,335 p.a.

   We seek scientists to join CISBAN, an exciting new research centre established following a major award (£6.4m) from BBSRC and EPSRC,
   to participate in studies of the mechanisms responsible for ageing and
   how they are affected by nutrition. Ageing is recognised
   internationally as a ‘grand challenge’ and is a field prioritised for
   growth. This post offer opportunities to work in an intensely
   multidisciplinary, world-class centre and contribute to the development
   and application of systems science.

   Research Associate (Bioinformatician – Network Analysis)

   To
   research and develop novel methods of representing and integrating
   molecular and cellular data as networks and apply this methodology to
   identify novel proteins and elucidate novel pathways involved in the
   process of cellular ageing and senescence.

   Job reference: A1090R

   Posts are tenable until 30 September 2010.

   Enquiries for the post may be directed to Dr Anil Wipat, School of Computing Science (email: anil.wipat@ncl.ac.uk)
   Further particulars for this post can be found on the University’s web page at http://www.ncl.ac.uk/vacancies/list.phtml?category=Research.

   Applications should be submitted by 11 January 2008 to Professor Tom Kirkwood, CISBAN Director,
   Institute for Ageing and Health, Henry Wellcome Laboratory for
   Biogerontology Research, Newcastle University, Newcastle upon Tyne NE4 6BE (email: tom.kirkwood@ncl.ac.uk).

   Committed to Equal Opportunities

3. Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health

   Research Positions

   Level F £25,134 – £32,796 p.a.
   Level G: £33,779 – £40,335 p.a.

   We seek scientists to join CISBAN, an exciting new research centre established following a major award (£6.4m) from BBSRC and EPSRC,
   to participate in studies of the mechanisms responsible for ageing and
   how they are affected by nutrition. Ageing is recognised
   internationally as a ‘grand challenge’ and is a field prioritised for
   growth. This post offer opportunities to work in an intensely
   multidisciplinary, world-class centre and contribute to the development
   and application of systems science.

   Research Associate (Modeller/Mathematical Biologist)

   To
   develop models of molecular and cellular mechanisms of ageing and to
   explore links between ageing, development and evolution from a
   life-course perspective. This post will also involve collaboration
   within the EU Network of Excellence LifeSpan, linking development and ageing.

   Job Ref: A1092R

   Posts are tenable until 30 September 2010.

   Enquiries for the post may be directed to to Professor Tom Kirkwood, Institute for Ageing and Health (email: tom.kirkwood@ncl.ac.uk) Further particulars for this post can be found on the University’s web page.

   Applications should be submitted by 11 January 2008 to Professor Tom Kirkwood, CISBAN Director,
   Institute for Ageing and Health, Henry Wellcome Laboratory for
   Biogerontology Research, Newcastle University, Newcastle upon Tyne NE4 6BE (email:* tom.kirkwood@ncl.ac.uk).

   Committed to Equal Opportunities

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