The ISBGroup Blog

On October 26, 2006 ~ 10 years ago! – Gunnar Cedersund defended his PhD thesis. This was the start of the process of creating the reseach group isbgroup.eu. To celebrate the 10-year mark, we have decided to arrange on January 14th 2017 a reunion party for anyone who has been involved so far!

Event: Reunion

Location: Linköping (Specifics tbd)

Date: 14th January 2017

If you’re interested in the current projects or how your past project has fared, you’ll get the chance to catch up during the evening. We are also very interested in the path you’ve chosen, so if you can, please do join us and tell us what you have been up to since we last saw you in the group. The day will consist of some lectures and general social interactions with the chance to see what has happened with everyone in the day, and a BIG party in the evening.

In case your invitation got lost in the Internet, please contact us at reunion@isbgroup.eu to reserve. We hope to see you in Linköping in January!

Regards,
The reunion committee

Gunnar Cedersund
Elin Nyman
Rikard Johansson
Karin Lundengård
Fredrik Eklund
Johanna Fridberger

PS. Below are some group pictures to help you reminisce.

Gunnar defending his thesis 2006.

ISB group 2010.

ISB group 2012.

ISB group 2015.

The travel period is now just completed, and I am looking forward to writing a report and summary of some of the most important discoveries and news discovered at these meetings in a later blog. Our field – modelling of biological systems – is in a very intensive phase of expansion right now, with a wide variety of events and simultaneous developments going on. Apart from the 4 meetings I/we have attended in the last 3-4 weeks, there are several other important events that we will miss, that are happening now (FOSBE, Magdeburg, Oct 9-12, on Foundations of Systems biology in Engineering) and within the next few weeks (e.g. the first conf of the European Association of Systems Medicine, Berlin, Oct 26-28, Berlin, link).

Figure 1: Me giving a lecture, before recieving the award Nytänkaren from the Swedish Fund for Research without Animal Experiments.

For now, however, I just want to say that one of the developments that is especially interesting to me right now is that concerning modelling in drug and device certifications, and how modelling there can be used in a 3R context, i.e. to replace, reduce and refine usage of animal experiments. I have previously won the first edition of a newly instated prize on this topic – Nytänkaren, Fig 1 – and several of the workshops and conferences I have just attended have had important news on the topic. For instance, I learned that the Food and Drug Administration in the US just have finalized a new report on how to use modelling in device certifications (Fig 2). I also learned interesting details of a second report, due to be published in 2017, which together with the first report will consitute a first complete guide to how modelling should be used in biomedical device certifications. In practice, the same guide will be used as a proxy also for certifications of new drugs, since the principles behind a sound usage of modelling are quite general. In fact, these guides have drawn quite a lot from similar guides already developed and established by NASA, where simulations already are considered mainstream in the development and certification of new space products. In short, I am quite impressed by these guidelines, and think that they have identified more sound principles than are being used in much of today’s research. In other words, these are really important developments!Figure 2: The new report just issued by FDA. Note the date, it was published a few weeks ago!

As a follow-up to these developments, and to me recieving the 3R award Nytänkaren, I have been invited to give a keynote lecture at a workshop at Karolinska Institutet, which takes place today. This workshop is arranged by Swetox and has the overall title “Replace animal use and increase scientific impact”. My talk is entitled “When laying the puzzle instead of just generating new pieces, animal experiments become increasingly irrelevant”. In this talk, I will give a more detailed report on some of the developments above, of my own research on the topic, and about some of the most important showcases that already are existing in the field. If you cannot attend the meeting yourself, you can still check out much of the material: some slides on the FDA developments are available here and a recent overview of our own research including an already FDA-approved glucose simulation model is found here. Finally, a two-sentence summary of the main statement given in the title of my talk is as follows: “if you want to test hypotheses regarding human mechanisms on the systems level, and create a systems-level understanding for the human system, you need data from a single system: humans. Thus, when science switches its focus to this more important endeavor, instead of just generating new hypotheses and pieces of knowledge that never are forged together and tested in a systems-level context, then animal experiments will become increasingly irrelevant”.

Figure 3: First page of my keynote presentation today. It is one of the first times, perhaps even the first time, that I am giving a lecture denoted as keynote, so I am looking much forward to it!

So, now
vacation time is over here in Sweden – and work is back with its full
hustle-bustle frenzy. I (Gunnar) started working on Wednesday last week, and
since then I have already submitted one application (to SSF’s industrial Ph.D. programme together with AstraZeneca,
on the creation of a new systems pharmacology platform, based on modelling which is used to
bridge between pre-clinical organs-on-a-chip data and human/clinical
studies), had two supervision days, changed office (my main office is now at
the Department of Biomedical Engineering, and not Clinical and Experimental
Medicine), did the final preparations for the half ironman race this weekend, and went to
Gothenburg to give a lecture in the group of Patrik Rorsman and Charlotta
Olofsson. It is about this last topic, my presentation, that I want to say a
few extra words also here.

Figure 1: Front page of my presentation earlier today.

The title
of my presentation was “Doing biology without modelling is like driving without
a safety belt – it might work, but it might also go really, really wrong”. The
talk was a salespitch to an essentially all experimentalist-audience, and it is
based on an image that came to my mind just a few days ago. The image is a response
to some of the most common forms of critique I usually hear against the usage
of modelling: “I could have said that without using the model”, “I don’t believe
that a model can do everything, I think there is still too much that we don’t
understand”, and “the model only seems to provide an extra degree of confidence
on a conclusion I would have said anyway”.

And, the
thing is that I actually – to a large extent – agree with all of those
statements. Modelling cannot do everything, and should not be oversold – but it can do some things, and those things should be properly appreciated. Similarly, many modelling results are conclusions one could have drawn without
the usage of modelling, and what the modelling does is therefore in many respects primarily
to put and extra degree of confidence on that conclusions, if that was the conclusion you anyway would have made. And just like for a safety
belt, when driving: often you don’t need it, you would be fine anyway, but it
is more secure to have it there, to bring an extra degree of security and
confidence to the current situation. In other words, if a model agrees with
your conclusion, you can be more sure that you are correct. However, in the
presentation, I also gave several examples of cases where the modelling
provides a conclusion that seems perfectly reasonable once you see it, but
where the prevailing conclusion before the model-analysis was done, actually
was a very different one. Apart from our main diabetes examples, I pointed to three probably less known such stories:

Figure 2: The difference between the new way of calculating EC50 values (black) with the old one (red).

1) Our EC50
story, published in FEBS J last year. There we showed that a simple model-analysis could detect a problem with
a previous way of calculating EC50 values: that the steady-state was not
reached in-between the changes in stimuli. In other words, the resulting curve was not an increase in steady-state values, but a long transient overlaying of overshoot responses. That
new interpretation of the data had some predictions, which we verified in independent
experiments, supporting our new interpretation of the data. Furthermore, it was
experimentally not possible to modify the protocol, to wait as long as one should to reach steady-state. Therefore,
the only way for this system to get correct EC50 values (corresponding to steady-state values) is to do what we
propose: to fit the model to a transient responses like the one already existing, and
then use the model to simulate the experiment as it ideally should have been done to start with. As you see in Figure 2 above, the new, more correct EC50 value is almost completely non-overlapping with the old one.

2) An
earlier story of muscle metabolism, where we showed that a seeming
contradiction and missing link – which had been investigated for 25 years – in fact
was not a contradiction at all, but merely a mis-interpretation of data. And
that re-interpreted version sounds very reasonable once you see it. In other
words, for 25 years, people had believed that a not yet discovered regulator of
glycolysis was active in anaerobic muscle recovery, but our modelling showed
that no search for such an unknown regulator is necessary: a correct analysis
of the data shows that the conventional regulators are sufficient to explain
the observations. This story is not yet published, but anyway available as
chapter 11 in my Ph.D. thesis.

3) Another
recent story on interpretation of data for the IL1beta analog Anakinra (Palmér et al, CPT Pharmacometrics Syst Pharm, 2014). In this
story, we had a look at data that seemed to be too good to be true, and
therefore were disbelieved by many: that Anakinra could have lasting positive
effects on the diabetes readout HbA1c as long tie as 1 year after the start of
the treatment, even though the treatment itself only lasted for 3 months. We
showed that a simple model based on pre-clinical data alone did actually
produce that clinical output as well. In other words, we showed that the
initial response was that the initial disbelief in the clinical data was unnecessary: they were perfectly aligned with the pre-clinical data.

Figure 3: Last page in the presentation, summing up the main advantages of doing modelling, i.e. pointing out some of the drawbacks of not using it.

These are three examples that show that
modelling in principle does not do anything that one anyway does: analysis
of experimental data to draw conclusions and suggest new experiments. However, modelling does these things in a more reliable fashion, and it is easy to go wrong otherwise. In other words, to do biological data
analysis without modelling, is just like to driving without a safety belt: it
might work, but it might also go really really bad – throwing away 25 years
of your life.

Figure 4: On the train on the way back, I happened to be seated right next to a very nice systems biology colleague of mine: Adil Mardinoglu. He told me that he and some colleagues of him had read my last blog post, and wanted to contact me about it. So that is actually the reason why I got inspired to write a new one as well!

A couple of weeks ago, our M.Sc. student William Lövfors presented his M.Sc. thesis entitled: “A first phosphoproteomewide mechanistic model of insulin signaling”. As the title indicates, this thesis presents the – to our knowledge – first ever presented version of a dynamic mechanistic model (based on ordinary differential equations and realistic assumptions of protein-protein interactions) that describes the entire phosphoproteome, i.e. all phosphorylations in all proteins that are relevant in a certain scenario. The scenario that we study here is the scenario that has been studied the most in our group, insulin signalling, and this systems-wide model is therefore an extension of our previous models. In fact, our most well-developed previous model appears as a sub-model – as the “core” model – in this new systems-wide model, which has been constructed in an iterative fashion, where we add protein after protein, in layer after layer, where each interaction is taken from suggestions in databases containing known or suggested interactions, and where all phosphorylated states in the model can describe dynamic mass spectrometry data. This work will be continued and experimentally validated and refined in various ways, and hopefully published during 2017. If you want to see more of how far we have gotten up until now in this latest status-report, here is a link to William’s thesis.

A graphical depiction of the model of all added proteins. The old model we had previously developed is depicted in yellow, all proteins that are connected by high-confidence interactions (3 or more references) are depicted in dark blue, and all proteins reached using one or several low-confidence interactions are depicted in light blue. All proteins can describe time-course data.

A final comment can be made about the academic journey of William so far. He started his journey, as so many others, by doing our project course in the year 3 of the engineering biology programme (TB), whereafter he did a ~9 months intership combined between ISBgroup and Mika Gustafsson’s group (then a sub-group of Mikael Benson’s group). His project then was devoted to a related project, where we created a systems-wide ODE model for gene-gene interactions (submitted to PLoS Comp Biol). Thereafter, he stayed in contact and in projects with us during the remainder of his M.Sc. studies. The initial project was financed as a scholarship-based research preparatory course, and he has been funded by some occasional months here and there for work in projects and for supervision in our project course, but mostly just by the fact that William has enjoyed working in these projects. During this time he has published one scientific paper (showing that you sometimes need mathematical modelling for something as basic as a correct calculation of EC50 values, link here), has done an oral presentations in international workshop (ISGSB 2014), and is scheduled to be co-author of two additional papers: one soon-to-be-submitted on adiponectin secretion, and one based on the systems-wide insulin signalling study already started in his thesis.

All in all, such an impressive CV is unusual for somebody who has just a few weeks ago finished his M.Sc. thesis, and we are therefore proud to say that William will stay on in our group as a proper employee during the next 6 months, during which we plan to convert his position to a proper Ph.D. position.

Picture of William, also used on his personal home page, here at ISBgroup.

Spara

In 2016/05/31, students who take the course TBMT19/33/37 and some members in ISB group went to AstraZeneca for a study visit. Four groups of students gave the final presentations for their project work. The presentations were interesting and successful. The topics of those projects vary from T2D cell level: signalling in fat and brain cells; the dynamics of glucose, insulin, fatty acid in whole level with relative drug simulation; the combination model of T-cell, mechanistic core, Lasso-omics and the study on brain activity by using fMRI.

After the students’ presentations, the project leader in AstraZeneca provided an lively introduction to their work, going through all the necessary steps and digging into details such as time cost and potential problems. That presentation really gave all the people a straight forward look at the working flow of real pharmacy modelling simulation.

Linnea Bergenholm and David Janzén, also some students who did their thesis in ISBgroup, who were students from Liu and now working in AstraZeneca shared their experience with attendants later. The topics included study period in Liu, further education and research and current work. The speech was inspiring since their past experience could be the tomorrow for the students.

During the lunch time, all people had the chance to sit together and exchange information with each other in a relaxing way, not only the students but also some staff who work in AstraZeneca.

Except for the presentations, students also made posters for their project which were used later in the afternoon for everyone to read. Meanwhile, many questions were raised from the people who attending the presentation. Every student in relative groups was trying to answer those questions from different perspectives which gives the audience a better understanding of the project. Opposition part was also held in the afternoon, where one group of student opposed against one other group. The opposition went really well and it was helpful for the students to find the drawback of their work.

For every group that attended the presentation, a logo is required for their project and the ‘best logo’ winner who got a cup was voted by the audience. And this year, group IntraCell won the honer.

The study visit to AstraZeneca gave students a chance to present their working results to those professional people and undergo the atmosphere in a company which has a tight connection to their study.

Almedalen is Sweden’s without a doubt biggest socio-political event. All of Sweden’s top politician’s are there, as are all major media, and most activist groups, and many generally engaged citizen. We are therefore proud to announce that ISBgroup this year will feature at this event. Our participation happens between 9-10AM on July 4, in Kinbergs plats 8, when we will give one of the introductory backgrounds as one member in the expert panel on research without test animals. (more info here) I, Gunnar, who will be the one participating, am looking much forward to this chance to visit this exciting event for the first time, to be able to attend many other interesting lectures, to meet and discuss with interesting, engaged, and influential people, and to be a part of some interesting on-stage discussions following my lecture.

If you are in Almedalen at the 2016 edition, come check us out! 🙂
/Gunnar

Spara

We have been invited to give an oral presentation at the annual systems and synthetic biology conference BioSynSys, which this year is held in Bordeaux, June 27-29. The lecture of Gunnar Cedersund will be held on June 29, and will be on recent conceptual and methodological developments that help us to find more accurate and well-identified predictions and prediction uncertainties, especially in the case of unidentifiability of parameters and single-cell data. Full abstract and title is appended below, and the conference home page is found here.

TITLE AND ABSTRACT

Prediction
uncertainty in the case of unidentifiability and single-cell data – new
concepts and methods

Mathematical
modelling is an integral part of both systems and synthetic biology, because it
can more accurately deal with the complexity of biological data. However, to be
truly useful, the predictions of the model must be in the form of core
predictions, i.e. they must come with a correct uncertainty. In the last few
years, there have been important progress in this field, especially concerning
the important situations of unidentifiable parameters and single-cell data.
This presentation will give an overview of some of these developments.

In the case
of unidentifiable parameters, it has become clear that traditional approaches
based on sensitivity analyses, the Hessian of the cost function, and
sampling-based Monte Carlo approaches all give inaccurate results. In such
situations, one may instead use rediscovered and recently improved alternatives
based on the conditional profile of the likelihood function. Importantly, these
methods can now not only be used for assessing the uncertainty of parameter
values, but for the uncertainty of arbitrary model predictions.

For the
case of single-cell data, problems with unidentifiability are often more
severe: it is often not possible to generate enough data from a single cell,
and averages over many cells provide inaccurate results. In such cases, it is
instead better to use methods from nonlinear mixed-effects modelling (NLME),
which borrows information across the entire cell-population. Using simulated
data where the truth is known, and real data from individual yeast cells, I
will illustrate when, why, and how NLME is advantageous.

All in all,
these new and improved concepts and methods provide important tools for a sound
and correct model-based analysis of single-cell data.

In Linköping, we have a quite rare scientific conference: a conference that has been arranged for over 20 years exclusively by undergraduate students! This conference is called KVIT, and it is arranged by the students in the cognitive sciences programme. The overall focus of the conference is the fascinating mix that is cognitive science: neurophysiology, psychology, IT, artificial intelligence, decision-support, user-interfaces, etc. The specific theme of this year was “Quality of life”, and at this conference we had been invited to give an oral presentation. This presentation was a bit unique because it for the first time outlined some less known long-term plans of our group: to merge and extend our research on a multi-level systems-level understanding of the brain based on mathematical modelling, with fundamental research on the relationship between quantum mechanics and possibilities for free will, and with research on different states of consciousness, such as sleep, narcolepsy, and different types of meditation.

Abstract is appended below, and the entire programme and more information of the conference can be found at the conference home page.

Abstract
One of the big promises of the Information Age is that of systems medicine: that our rapidly growing biomedical datasets will be possible to analyze using
advanced mathematical models, to produce things like automated
diagnoses, personalized treatments, and an improved drug and medical
device development. In this talk, I will go through some recent
developments in this field, to show that this promise is not a
far-fetched, science fiction utopia, but a rapidly approaching reality.
Focusing essentially on my own research, I will show how such
mathematical models now can be used to e.g. replace test animals when
developing new treatments for diabetes, and be used to better unravel
the complexity of the human brain. Using such tools, we can therefore
start to obtain a new type of holistic understanding of the human
organism, into which peces of knowledge both can be examined more
correctly, and subsequently be integrated into a useful picture of the
whole. I will therefore end by a comparison of this increasingly
holistic understanding with such found in e.g. yoga traditions for
thousands of years. What are the similarities and differences, and what
will it take to one day merge such understandings?