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Deep Thinking in Immunology

Immunology is empowered by lots of data, with deep sequencing, cell population imaging, proteomics, high-throughput immune assays, or metabolomics as sources.  In the past one would draw a scheme with perhaps 20 players on the blackboard, with arrows indicating which influences which, ‘up’ or ‘down’.  Now there is many more (>75 000) molecular and cellular players, and it matters how much they go up or down, and when, and where.  With the advent of deep data, our traditional way of thinking falters: It does no justice to the new opportunities for identifying the immunology in action.   We may need something like ‘deep thinking’. 

But what is deep thinking?  The human brain is not fit for thinking rationally about thousands of molecules at the same time.  But it can invoke a little help from its friends.  It is wise to befriend computers handling virtually unlimited numbers of data.  They can analyze the data in many, many different ways, finding complex combinations of the most complex patterns.  Such data analysis has long been limited by focusing on a single level of cellular organization, e.g. the transcriptome, rather than on the dynamics of the entire, hierarchical network at stake. It has not been ‘deep’ data analysis, penetrating down through the various levels of cell action.

Computers can also empower mathematical models that simulate immunological reality.  For a long time already, mathematical modelling has played a big role in biology.  It has helped appreciate the role of self-organization in developmental biology, synchronization of cellular oscillations, fragilities in the performance of metabolic and signal transduction pathways, and emergence of immunologically most relevant cell populations.  That modelling however, has not been ‘deep’ modelling.   Most of those mathematical models focused on concept rather than realism, and were small rather than ‘deep’.    

With systems biology and functional genomics new types of both data analysis and modelling is developing, i.e. ones that try to integrate ‘all’ the relevant data into, thereby ‘deep’ (large), analytical and mathematical models of reality.  Married to the deep data, these models enhance understanding of reality rather than potentiality.  They help test hypotheses in hard rather than soft ways.  They upgrade the human mind, assisted by computers and systems biology, from traditional to ‘deep’ thinking.

This ‘deep thinking’ is not easy.  It constitutes a challenge.  First, one has to befriend computers and systems biology.  And then one then has to converse with them patiently, so as to ensure that computer programs make sense immunologically.

Aims and Objectives

The objectives of the workshops are to:

  • Acquaint immunologists with some of the easy-to-use modelling software, which requires very little mathematics
  • Make immunologists use existing systems biology models to address some paradoxes in cell biology and to experience how modelling can fortify the mind
  • Enable immunologists to access existing model repositories and interrogate many  more existing models
  • Enable immunologists to integrate deep data onto such models
  • Show immunologists how they could make and run their first model
  • Befriend immunologists with some modelers

Organization

What is the timeline of the workshops?

  • Each workshop is set for 180 minutes
  • Introductory lecture of 20 minutes; explanation of the logistics of the workshop; scientific introduction to the topic
  • Hands-on for 150 minutes, i.e. the participants will themselves model or analyze data on their own PC (pear or apple) on the basis of a ‘recipe’ for modelling, using software, models, and ‘Big Data’ that will be pre-supplied beforehand
  • Concluding lecture of 10 minutes; telling what participants can now do with this
  • Onsite assistants will be available to support participants during the whole time of the workshops

What do I need to bring to the workshops?

  • Laptop with USB port (apple or ear (=PC)), good mood and a profound interest in immunology and its complexities.
  • Participants will be asked to download the pre-supplied material onto their PC well before the ECI. They will obtain the necessary password and link upon pre-conference registration for the workshop.

How do I register for the workshops?

  • You can add a ticket for one of the hands-on workshops during the regular registration process.
  • Each participant may only register for one of the six workshops.
  • We will charge € 15 per workshop/ticket.
  • Please note that the number of participants is very limited and our experience is that spaces fill up quickly.

Workshop Schedule

Presenters:  Hans Westerhoff (Workshop A) and Yang Li (Workshop B)

Workshop A: Dynamic Modelling
M1: Dynamic Modelling (Beginners)Paradoxes in intracellular immune signallingSeptember 308:30-11:30
M2: Dynamic Modelling (Advanced)Immune cell networks: chronic versus acute inflammation September 414:45-17:45
M3: Dynamic Modelling (Advanced)Making sense of omics data using genome wide metabolic mapsSeptember 508:30-11:30
Workshop B: Analyzing of Big Data
D1: Analyzing Big Data (Beginners) Fully booked!Integrating time-dependent RNA-seq with public data September 314:45-17:45
D2: Analyzing Big Data (Advanced)Integrating multi-omics with immune parameters September 408:30-11:30
D3: Analyzing Big Data (Advanced)Integrating omics and immunological parameters with genetic variantsSeptember 512:00-15:00

Beginners:    People who are interested but have not yet practised
Advanced:    People who have essentially already done the "For Beginner" bit in their own practice