From Hypothesis to Data-driven Research Or The End of the Age of Science and the Dawn of the Age of Systemic

Slide 42

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From Hypothesis to Data-driven Research

Or

The End of the Age of Science and the Dawn of the Age of Systemics

By

Gerd Moe-Behrens, PhD

Director and Founder of the Leukippos Institute

2011

Slide 1

Hi, I am Gerd Moe-Behrens, director and founder of the Leukippos Institute for Synthetic Biology. Today I will talk about the revolution that is going on in science.

So let’s start.

From Hypothesis to Data-driven Research.

Slide 2

Or

a little bit more provocative:

The End of the Age of Science, and the Dawn of the Age of Systemics

Slide 3

We will cover the ongoing revolution in science. Everything will change:

In Part 1 of the talk we will explore the philosophy and logic behind the traditional hypothesis driven science.

The crisis of this method, we will discuss in Part 2.

We will look in Part 3 at the paradigm changes; changes that will lead to the end of science as we know it.

In Part 4 we will see how data driven systemics becomes the new research paradigm, and what this means for our everyday life as scientists.

Slide 4

Part 1 Hypothesis driven science

Slide 5

About 2500 years ago there was a glorious awakening. Greek philosophers argued that the universe is knowable. Why? Their reasoning: It is ordered. About 600 BC we meet the pioneers of science. Mysticism was obsolete. The world could be explained with the help of what they called logos. They started asking the question of how nature is built. They figured out that the base for all understanding of nature is the question: What is reality?

Some believed that all reality has a mental origin. This led to the concept of idealism, which we will come back to later.

Others saw matter as a base of reality. This resulted in the concept of materialism, which has been the leading paradigm of science for the past decades. This concept means: We have an object - let us say DNA. We are able to measure this DNA, with methods like x-ray crystallography and other. This leads to objective data, to facts. Objective in this context means, the data are true, independent of the scientists who collected them. Thus our picture of the DNA is identical with the object DNA, which exists independent of us.

Slide 6

Materialism is the key concept underlying hypothesis driven research. Objective data, facts are the main argument, in order to test the truth of a hypothesis.

So - How does this concept work?

The scientific process begins with guesswork about things we cannot observe, the hypothesis. A hypothesis needs to be simple. We use reductionism and search for single correlations: This means guessing how A and B are connected. We do not make a hypothesis involving many components A, B, C, D, E, F, G, H, K, L and so on. Complex systems are difficult to test, and testing is key. The testing of a hypothesis is done by the deduction of an empirical consequence. An empirical consequence is a statement, which follows logical from the hypothesis. It should be possible with the help of observations and data to decide, if this statement is true or false.

In the next step we do our experiment and collect data. If the data are equal to the deduced empirical consequence, we conclude that the probability of the hypothesis increases. In the other case where the data are different from the empirical consequence we conclude, that the probability of the hypothesis decreases.

If we watch this concept critically we see that we do not have a pure materialistic concept. We deduced the EC with the help of logic. Logic is a concept of the brain, a fundament of idealism. This we will come back to later.

Slide 7

Let us have a closer examination of the logical forms found in the hypothesis driven research.

What is a logical form?

Let us start with the example sentence: If it is raining so the streets are wet. If and so are called logical words. These are kept in a logical form. The rest is substituted with symbol signs. So instead of it is raining we use p and instead of the street are wet we use q. So we get: If p so q.

The first logical form we find in the hypothesis driven method is called modus tollens. If p, so q. We observe - not q and conclude not p. To make this easier to understand we will use our example: We know: If it is raining, so the streets are wet. We observe, that the streets are not wet and conclude that it is not raining. This reasoning is correct, because it is not possible to find a contradictory example. For our hypothesis method this means: If hypothesis so we can deduce an empirical consequence. We do not observe data corresponding to the empirical consequence and conclude that the hypothesis is not true. This is correct concluded, if the data are correct measured.

The second logical form is called confirmed consequence. If p, so q. We observe q, and conclude that p is correct. This conclusion is false, as we will see in our example. We know: If it is raining, so the streets are wet. We observe, that the streets are wet and conclude that it is raining. This reasoning is false, because we easily can find contradictory examples. There are many possibilities, why the streets could be wet. Somebody has washed the streets, a tube was broken, and so on. So there are many reasons why the streets are wet. If we observe wet streets, we cannot conclude, that it is raining.

For our hypothesis method this means: If H so EC. We make our experiments and our data conform to the empirical consequence. We believe that we can conclude, that the hypothesis is true. However, this conclusion is false. Another hypothesis, different from our, could have resulted in our empirical consequence. In the best case we can say that our data tell us that the statistical probability of our hypothesis increased, but nothing more. However, this last method is the chosen one for our publications, due to our problems of publishing negative data.

Slide 8

So we are right into the crisis of the hypothesis driven science.

Slide 9

As we discussed: Positive data are not useful for a logical valid argumentation. Negative data could lead to a logical valid statement, but these data do not support our career. No real proof of the hypothesis is possible and we only end up with statistical probabilities.

Slide 10

Hypothesis driven science has, beside the described logical problems, also conceptual problems:

The hypothesis method can deal with simple correlations between A and B.

But the method fails if the problem becomes more complex with many factors, eg A to I or even more. This is too complex for deducing an empirical consequence.

Slide 11

A picture by the Japanese artist Hokusai provides us with a nice illustration of the problem of reductionism versus holism. We see blind men exploring an elephant. One blind researcher is studying the back, another the neck, one blind researcher explores the tail, the next the front part of the tooth and so on. Nobody gets the whole picture.

Contemporary research has come to a point where we change the perspective from reduction to holism. The reductionistic approach has successfully identified many components and interactions, but does not provide convincing explanation for how a complex system, such as a living being work. Today we are in the situation that we have huge amounts of data from high throughput experiments, but we do not understand the whole picture.Huge amounts of data and facts available mean lesser need for models. The number speaks for itself.

Holistic thinking means beginning with the understanding of the highest abstraction level. Our scientific approach should maybe do the same. First understand the whole picture, then the details.

Slide 12

So we face the problem: How to understand the whole picture? How to understand huge data amounts and complex systems?

Slide 13

We can observe a paradigm change in science.

Slide 14

2 movements lead to that paradigm change:

The first movement is characterized by a new social reality, as shown in this slide.

The second movement is due to a novel theoretical base for science as shown in the next slide.

It is primarily the computer, which has changed our social reality. Especially two computer developments are responsible for the wind of change. The first is the enormous storage capacity, which emerged in the cloud. We talk about a magnitude in the petabyte scale. One petabyte is equal to one quadrillion bytes or 1000 tetrabytes. This is 10exp15 bytes. Google processes about 24 petabytes per day.

The second important development is that a huge number of computers are now connected via the Internet. We now have access to the huge data- amount from many locations. In addition: We have twitter, facebook and many other social networks, which give a context for the enormous data-amount.

We have a data flooding. This is a problem normal science cannot solve. The structures of the science world were designed to fit a pre-computer age. Established power structures do not easily adopt. As a consequence of the developments of the computer and its networks a revolutionary science develops. In line with this we can see do it yourself (DIY) and open science projects on the Internet. A novel data driven research concept is under development.

Slide 15

On the theoretical side new theories for dealing with huge data-amounts and complex systems have emerged:

We can follow this development back to the Greek idealistic philosopher Plato (360-347). He introduced in his dialogue Philebus a concept called

System. A system is according to Plato a model for thinking about how complex structures are developed: System meant total, crowd, union.

Another idealistic philosopher, Kant, introduced, in 1790, in his Critique of Judgment the concept of self-organizing. Kant stated: There exists an entity whose parts or organs are simultaneously end and means.

Between the 1950s and 60s three groundbreaking works were published:

1948, Norbert Wiener publishes Cybernetics or Control and communication in the animal and machine.

In 1955 William Ross Ashby’s Introduction to cybernetics came out.

1968, Ludwig Bertalanffy published General System theory: Foundations, Development, Applications.

Bertalanaffy defined the concept of systems. Cybernetics explains complex systems that exist of a large number of interacting and interrelated parts. Wiener and Ashby pioneered the use of mathematics to study systems.

This systems theory was further developed in the following years. Important contributions during the 70 and 80s were: The theory of second order-cybernetics, with prominent figures such as Heinz Foerster. He found out that an investigator of a system can never see how it works by standing outside, because the investigator who observes a system affects it and is affected by it. The role of the investigator as a part of the system, self-referencing, self-organizing, and the subject-object problem were in focus. Moreover to mention is the Nobel-prize winning work of Ilya Prigogine on self-organization and his systems theory concepts with thermodynamics. Furthermore: Mitchell Feigenbaums work on Chaos theory.

General systems theory is an important fundament for software. Thus, if we use the computer as a scientific tool this systems theory influences the scientific method. This means by using the computer as a scientific tool we are applying a theory based on idealism. The algorithms of our computer models are finally based on platonic idealism.

Contemporary application finds systems theory in bioscience in fields such as systems biology and its practical application synthetic biology. The term systems biology was created by Bertalanffy in 1928. Systems biology focuses on complex interactions in biological systems by applying a holistic perspective.

Slide 16

So we saw that the described philosophical movement has two components: Idealism and systems theory. Idealism will be explained in this slide, basic concepts of systems theory in the following slides.

Philosophical idealism has its ancient roots in the work of Plato who lived from 428/7 to 348/7 BC. A centerpiece of Plato’s work is his theory of forms, also called theory of ideas. Plato claimed that the material world of change, which we experience with our senses, do not possess the highest and most fundamental kind of reality. Only inferior things of this world, such as material objects, mirror images and shadows are knowable through our senses. This inferior world is object to constant change. On the contrary we have an eternal static world. This superior world consists of mathematical objects, terms and non-materialistic abstract forms (or ideas). These are the only true objects of study that can provide us with genuine knowledge. Forms are archetypes, blueprints, the essences of the various phenomena of the same thing. Moreover, Plato believed that forms are the fundamental building bricks of our world.

Cybernetics follows this line of thinking. The base of Cybernetics is the assumption that reality is not reflected but calculated by the human nervous system. This means for the example on the slide: Our brain calculates a model of an object such as DNA. The human reception is the basis of the scientific method. The key tools are mathematics and logic. The result of this calculation is a model of DNA, which is not identical to the object DNA. It is impossible to achieve knowledge about the world such as it exist independent of us.

Our human reception of reality is an invention of our brain. Heinz Foerster states in line with this that all scientific theories are based on such inventions, which he provocatively called funny and interesting stories. A scientific proof is impossible. Hypotheses are inventions of the brain. If there is a hole in the theory a new part is invented. Logic and mathematic are descriptive machines, which are used to deduce one statement from another. All statements are true, because all are deducible from each other. This is a chain without an end. Seen together: We do not have access to the reality of objects such as they exist outside of us.

Slide 17

The word science has it’s indo-european root in the word scy. This means separating. Scy belongs to a reductionistic world of thinking.

Heinz Foerster (1911-2002) saw an end to a 2000 years movement started with Aristotle. He stated:

If you take in account all system theory and systems researchers, which appear today in art and science I would not call this science any longer I would call it systemics. Contemporary science moved to a position, which sees thing together, short systemics. Thus: From science to systemics. This is how I see the steps today.

Slide 18

A system is a set of interacting or independent components forming an integrated whole.

Common characteristics of a system are: components, behaviors and interconnectivity.

Systems have a structure defined by components.

Systems behavior involves input, processing and output of data. Behavior can be described with terms such as self-organizing, dynamic, static, chaotic, strange attractor, adaptive.

Systems have interconnectivity. This means that the parts of the system have functional as well as structural relationships between each other.

The stipulated line marks the boundary of the system. Boundaries can be open or closed.

As a consequence of this boundary the system is distinguishable from its surroundings.

A system can be self-regulating by feedback. Such feedback can be given eg by scientific experiments or communication in computer networks.

It is important to be aware of the context of the system. In this respect a researcher can be described as a part of the system.

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To summarize: Computer cloud storage, networks and systems theory are the fundament of a new scientific method: Data driven systemics. We will in the following examine the concept of data driven science and the consequences of this concept for our everyday life as scientists.

Slide 20

A data pool is the engine of the data driven research approach. Data pool is another word for the common database, which stores all information, which we can access via a network. A filter is an algorithm for a selector from the data pool. The filter determines, which subset of data will be chosen from the data pool. The filter process results in a pattern. A pattern is the system, which we examine in our research, e.g. the network of interacting proteins involved in the maintenance of pluripotency. This pattern is subject to a scientific evaluation for example by experiments. This pattern evaluation again results in new data, which are, in a feed back loop, placed back into the data pool.

Slide 21

This structure can for example be seen in the STRING software suite.

The data pool: STRING is a database of known and predicted direct or functional protein interactions. The database consists of 2590259 proteins and 630 organisms. The data are derived from genomic context, high - throughput experiments, co expression data, and previous knowledge.

Filter: One or several protein names or sequences can be placed in a search field. We can for example submit the pluripotency related protein POU5F1. This results in algorithm with a pattern as output.

Pattern: We will get POU5F1 interacting proteins, a picture of a pluripotency related protein network. We shall notice, that we only need to know the name of one protein in order to obtain a more complex pattern. If the protein-protein interactions are predicted and not known facts, we can validate these interactions in further experiments. The new data can then subsequently be submitted to the database. The next researcher, who will recall this pattern will get a more complete picture.

Slide 22

And now: A few words about the different components of the data driven research approach.

We start with the data pool.

Slide 23

Data are observed facts. They are not reasoned statement, not controversial and inter subjective. However, these facts are not always objective, because they depend on theory. The facts are connected by a fact specific connector to logical words. Logical words are if, so; if and only if; or; not; and; is; and all, some, none and is like. The data pool is the sum of all stored facts and their connection to logical words.

Slide 24

And now the filter:

Slide 25

Starting point for a filter is a scientific question. A filter can be based on facts or ideas. Thus materialistic or idealistic concepts can underlay a filter. These concepts are the basis for the algorithm, which will be used to define the subset from the data pool. This algorithm is, as the data, theory dependent. Since it is possible to set a filter after very different criteria we can get everything from a robotic to an art orientated science. As the data driven research approach becomes well defined it will be programmable. If we look at the example of STRING it will be possible to achieve a fully automated scientist. The computer can identify patterns and figure out which protein-protein interactions are based on facts and which are predicted. A robot measuring protein-protein interactions can then be used to measure the predicted interaction. The data can then be placed back in the pool. The whole process is fully automated. On the other hand a mind derived creative artistic idea can be used to set a filter. We get an art based filter algorithm.

It is interesting to notice, that one do not need a complete knowledge of the pattern. Due to the connectors between the facts, an incomplete filter will be able to extract a larger pattern.

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The pattern

Slide 27

A pattern is an open system. It contains facts. Logical words are bound via connectors specific to the facts. This gives a network of correlated facts. Since the system is open we will always have facts, which have an unknown correlation to this network. We have a complex system and not a mono causal. It is worth to notice, that the anatomy of the pattern is dynamic. The pattern depends on the number of feed back cycles. It depends on the new data, which we get from the pattern evaluation and which are put back into the data pool.

Slide 28

Pattern evaluation

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In a next step we need to evaluate, if the pattern is true and complete. Are for example all protein-protein interactions based on facts or are they predicted? Are we lacking unknown protein-protein interactions? If we lack data we need to perform further experiments in order to complete the pattern. If the pattern is true and complete we can concentrate on the application of the pattern. We can use the pattern to build new theories or we can use it for practical applications. In line with this we can design on the base of a pattern a novel synthetic biological pattern. All together, this will result in new data, which can be submitted back into the data pool.

One should notice the following interesting observation: An interesting moment is when we reach the point of pattern completion, that means at a point where we have huge data amounts in the data pool. At this point the practical applications such as synthetic biology will gain importance in this cycle.

Slide 30

Data feed back loop to the data pool

Slide 31

An important feature of the data driven research approach is the feedback loop to the data pool. The data pool is permanently connected to communicating computers and the people behind these computers. These communications put the data in a context. A constant evaluation of the pattern in these networks will dynamically fill the data pool and dynamically change the pattern anatomy. This process will be very fast, due to the large size of the network. A constant flow of data is observable.

Slide 32

In the following we have to examine the logical structure of data driven research in the same manner as we did for the hypothesis method.

Basically the pattern is a subset of the data pool. This means that the pattern is deduced from the data pool. Deduction is a logically true method, since it concludes from general to special.

Slide 33

Let us have a look at the logical form we can find in the data driven science.

The data pool, is our collection of facts. These facts can be either true, false or probable, depending on the quality of our measurements and our science. The data pool in its whole can be either complete or incomplete.

This pool is the starting point of our research. We apply our research method on this pool.

The logical form used in this method is called modus ponens.

We know: if p, so q: we observe p and we conclude q is correct. This conclusion is correct. As in our previous example: We know: If it is raining, so the streets are wet. We observe: it is raining. We conclude: The streets are wet. This is true, since it is impossible to find a contrary example. In the data driven approach we find: We define a filter and get as a result a pattern. Thus we know, if filter so pattern. We apply the filter and get a pattern. This is logically true.

In this concept the pattern is as true as the input data from the data pool. Novel measurement of data increases the truth of the pattern. The data feed back to the data pool increases pattern truth.

In conclusion the quality of the data in the data pool are the key. The pattern is dependent on the kind of filter we define. However the logical truth of the pattern is independent of the filter. All kinds of filters result in logically true patterns. It does not matter if the pattern is by a robot, if it is artistic, based on materialism or idealism or whatever.

Slide 34

The data driven science approach change the scientific method and results in a praxis called science 2.0

Slide 35

It is key for the data driven approach, that the data pool is as complete as possible. This demand is what basically changes the scientific praxis. We remember: The pattern is only true and complete if the data pool is so. We also have to consider that preliminary and negative data are important to build a correct pattern. Thus these data are also important to have in the data pool. Moreover, the data pool must be free accessible. Only data, which are free accessible will be part of the pattern.

To summarize: The input to the data pool must be multiple, fast and easy. The data pool needs to be complete, common, that means all data in one pool. Moreover the data pool needs to be standardized, in order to make the data computer readable. The output from the data pool must be open. This means an open access to the data pool.

Slide 36

So the data pool pattern interaction demands a change in the scientific praxis.

A multiple, fast and easy input demands:

Social networking is very important. Social network increase the size of the group of people participating in the scientific project. This increases the amount of input. Furthermore, we will see the end of the traditional scientific journal. The traditional publishing process is very slow. It often passes several years between the first results in a project and their publication. We will instead of the journal publishing get high speed direct publishing to the data pool. We will use open lab notebooks, blogs with unlimited length and micro formats of publishing such as tweets.

The demand for a complete, common and standardized data pool means: All kinds of results get published. Also negative and preliminary results obtain importance. Moreover, papers need to be written in a computer readable format in addition to the human readable format.

A demand for a data pool with open access results in open access publishing and in open source licensees.

Slide 37

Also the filter has its influence on the scientific method. As we saw the logical truth is unaffected by the kind of filter. This means, that different filters are possible. Thus Idealistic and materialistic concepts are possible. We get a range from robotic to art driven science. Also non-scientists can make valuable contributions to research projects.

Slide 38

The feedback to the data pool changes the scientific method and praxis.

The digital conversation gives context for data.

We work with complex systems with dynamic feedback. This results in an end of mono causal reasoning. We get dynamic publishing with preliminary results and post publication review instead of static papers. Moreover, we will get an unseen speed in science, since highly technical problems are more likely to be resolved by a larger group of people who cooperate.

Slide 39

Concerns about our career are what basically delay a lot of scientist to move to the world of science 2.0.

I will give a few examples of such concerns:

How is the intellectual property, the authorship assigned? What credit system shall be used? Are we looking stupid and unprofessional, if we publish uncompleted dynamic documents? How do we protect our data for plagiarism? How is quality assured? Does frequent publishing result in lower quality? How do we survive in an open environment in respect to money and career? Do we need protection from our competitors or can we change to be collaborators? How do we gain trust for our applications?

Slide 40

I will finish with the good news:

Faster and greater cooperation and collaboration will speed up the scientific discovery and the career, especially of the early adapters!

Slide 41

Thanks a lot for watching and your interest.

I hope you enjoyed the talk. I would appreciate your comments. If you have any questions, comments... Please contact us.