systems biology systems

Systems Biology Systems

Michael Hucka, Ph.D.Department of Computing + Mathematical Sciences

California Institute of TechnologyPasadena, CA, USA

Monash University, Australia, August 2013

Email: mhucka@caltech.edu Twitter: @mhucka

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The early days of Systems Biology

The SBW and SBML projects

In hindsight ...

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The early days of Systems Biology

The SBW and SBML projects

In hindsight ...

Thread #1: criticisms of molecular biology at the timeMolecular biology approach characterized as reductionist:

• Catalogue and characterize all the parts

• Expectation: knowledge of all parts ⇒ understanding the system

Some typical methods:

• Identification of proteins, sequencing genome

• Knock-out experiments

• Drawing diagrams

Dissatisfaction: too many questions left unanswered

• E.g.: have sequences, yet don’t know roles of most genes

(Not entirely accurate, nor fair)Many people understood it wouldn’t itself yield deep understanding

• And molecular biology does have history of integrative thinking

- 1950’s, 1960’s: feedback inhibition, lac operon, others

(And anyway, systems biology needed molecular biology)

Genomics science = systems biology?A scaling up of experimental approaches to whole genomes, made possible by high-throughput technologies

• Catalogue and characterize parts and interactions

The dawn of the many system-wide “omics”

• Transcriptomics, proteomics, metabolomics, ...

“Lee Hood brand of systems biology”

Thread #2: systems modelingEarly systems thinkers

• Bogdanov (1910-1920s?), Wiener (1950’s), Mesarovic (1960’s), von Bertalanffy (1960’s)

• Articulated the idea that understanding the system is critical

- “The whole is more than the sum of its parts”

• Model-centric view: build models to help understanding

But: much early work was too removed from real biology

• Engineers and physicists dabbling in biology

• Mainstream biology ignored it

Subsequent developments in systems theoryBiology:

• Early successes in application of mathematical modeling:

- Hodgkin & Huxley (1952): neuronal action potential

- Noble (1960): heart

• New theoretical approaches (1960s-1970s)

- Metabolic Control Analysis

- Biochemical Systems Theory

Engineering:

• Advances in control theory and dynamical systems theory

Common theme: complex systems are nonlinear, with feedback loops

Fast & cheap computing changed everythingEarly simulation work in biology in 1940-1960’s was difficult, limited

• E.g., Chance (on analog computers!), Garfinkel

Rapid advances in computing (1980-1990’s) revolutionized simulation

• Could simulate larger, more complex models, with nonlinearities and feedback mechanisms

• Computing environments became more sophisticated and friendly

Of course, the computing revolution also enabled high-throughput bio.

• ... which led to the need to interpret massive quantities of data

• ... which led to reexamination of engineering-based ideas

- Dynamical behavior, control systems, etc.

“Hiroaki Kitano brand of systems biology”

Systems biology is both threads

Early dichotomy gave way to realization that both are needed

And both need each other

• Data about components (via omics)are needed, but alone do not explain function and behavior

• Math/engineering concepts (control systems, feedback, etc.) only helpif applied in service of understanding the results of experiments

Together, the two threads can weave a tapestry of understanding

Of course, community-building is not quite that easyRequired active efforts, particularly on the part of Hiroaki Kitano

How did he achieve such influence?

• Timing

• Convincing other influential thinkers

• Building an identity

- Publishing influential papers

- Organizing conferences (ICSB)

- Founding an institute (SBI)

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The early days of Systems Biology

The SBW and SBML projects

In hindsight ...

2000: The year we made contact

One initial goal: get 8–10 software systems interacting (Gepasi, DBsolve, StochSim, ...)

John DoyleHiroaki Kitano

Hamid Bolouri

Andrew Finney Herbert Sauro

Mike Hucka

JST ERATO Kitano Symbiotic Systems Project

Existing software was not interoperable

SBML: a lingua fra

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for software

Format for representing computational models of biological processes

• Data structures + usage principles + serialization to XML

• (Mostly) Declarative, not procedural—not a scripting language

Neutral with respect to modeling framework

• E.g., ODE, stochastic systems, etc.

Important: software reads/writes SBML, not humans <Beginning of SBML model definition>

List of function definitionsList of unit definitionsList of compartments

List of molecular speciesList of parameters

List of rulesList of reactions

List of events<End of SBML model definition>

SBML = Systems Biology Markup Language

The raw SBML (as XML)

Many models can be encoded

• Metabolic network models

• Signaling pathway models

• Conductance-based models

• Neural models

• Pharmacokinetic/dynamics models

• Infectious diseases

New types supported by SBML Level 3 packages

• Flux balance constraints

• Qualitative models

• ... more in the works

Scope of SBML encompasses many types of models

Find examples inBioModels Databasehttp://biomodels.net/biomodels

Many software systems support SBML today

0

100

200

300

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

(number of tools in the guide, counted in middle of each year)

254+ today

7000 reactionsThiele et al., Nature Biotech., 31, 2013

Many significant and popular models are in SBML form

Where to find out more: SBML.org

Essential ingredients of the effortOur core values were formulated by Hamid Bolouri:

• Our goal was not to replace the systems others were developing—our goal was to add value to their work

• We made software tools available, for many platforms

• We made all our work licensed as open source and free of charge

We provided a focus for people to discuss standards and software

• We organized and hosted workshops. Lots of workshops. Lots.

• We listened to others and formulated solutions in response to their requests, and solicited constant feedback

Essential ingredients of the effortOur core values were formulated by Hamid Bolouri:

• Our goal was not to replace the systems others were developing—our goal was to add value to their work

• We made software tools available, for many environments

• We made all our work licensed as open source and free of charge

Essential ingredients of the effortOur core values were formulated by Hamid Bolouri:

Our goal was not to replace the systems others were developing—our goal was to add value to their work

We made software tools available, for many environments

We made all our work licensed as open source and free of charge

We provided a focus for people to discuss standards and software

• We organized and hosted workshops. Lots of workshops. Lots.

• We listened to others and formulated solutions in response to their requests, and solicited constant feedback

The most important outcome?

A community flourishedAttendees at SBML 10th Anniversary Symposium, Edinburgh, 2010

More agreement needs to be achieved, for additional facets of modeling

Numerous bottom-up efforts have self-organized

• Some overlapped, yet proceeded independently

Several groups realized the situation was not constructive

• Result: COMBINE – Computational Modeling in Biology Network

Main objectives:

• Coordinate meetings

• Harmonize standards development

• Develop standard operating procedures and common tools

• Provide a recognized voice

Later: the creation of COMBINE

Outli

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The early days of Systems Biology

The SBW and SBML projects

In hindsight ...

What did we get right and wrong?

Time it well

• Too early and too late are bad

Start with actual stakeholders

• Address real needs, not perceived ones

Start with small team of dedicated developers

• Can work faster, more focused; also avoids “designed-by-committee”

Engage people constantly, in many ways

• Electronic forums, email, electronic voting, surveys, hackathons

Make the results free and open-source

• Makes people comfortable knowing it will always be available

Be creative about seeking funding

Some things we (maybe?) got right

National Institute of General Medical Sciences (USA) European Molecular Biology Laboratory (EMBL)JST ERATO Kitano Symbiotic Systems Project (Japan) (to 2003)JST ERATO-SORST Program (Japan)ELIXIR (UK)Beckman Institute, Caltech (USA)Keio University (Japan)International Joint Research Program of NEDO (Japan)Japanese Ministry of AgricultureJapanese Ministry of Educ., Culture, Sports, Science and Tech.BBSRC (UK)National Science Foundation (USA)DARPA IPTO Bio-SPICE Bio-Computation Program (USA)Air Force Office of Scientific Research (USA)STRI, University of Hertfordshire (UK)Molecular Sciences Institute (USA)

SBML was made possible thanks to funding from:

Not waiting for implementations before freezing specifications

• Sometimes finalized specification before implementations tested it

- Especially bad when we failed to do a good job

‣ E.g., “forward thinking” features, or “elegant” designs

Not formalizing the development process sufficiently

• Especially early in the history, did not have a very open process

Not resolving intellectual property issues from the beginning

• Industrial users ask “who has the right to give any rights to this?”

Some things we certainly got wrong

Was it worth it?

There are tradeoffsThis was not the path I planned when I did my Ph.D.

• It’s been nice, but ...

Developing usable software ≠ developing research-grade software

• Takes huge amounts of time

- That’s time you are not writing papers

‣ Remember it’s still publish or perish ...

Ultimately must decide if you really want the life of a professor

Nicolas Le Novère, Henning Hermjakob, Camille Laibe, Chen Li, Lukas Endler, Nico Rodriguez, Marco Donizelli, Viji Chelliah, Mélanie Courtot, Harish Dharuri

This work was made possible thanks to a great communityAttendees at SBML 10th Anniversary Symposium, Edinburgh, 2010

John C. Doyle, Hiroaki Kitano

Mike Hucka, Sarah Keating, Frank Bergmann, Lucian Smith, Andrew Finney, Herbert Sauro, Hamid Bolouri, Ben Bornstein, Bruce Shapiro, Akira Funahashi, Akiya Juraku, Ben Kovitz

Original PI’s:

SBML Team:

SBML Editors:

BioModels DB:

Mike Hucka, Nicolas Le Novère, Sarah Keating, Frank Bergmann, Lucian Smith, Chris Myers, Stefan Hoops, Sven Sahle, James Schaff, Darren Wilkinson

And a huge thanks to many others in the COMBINE community

SBML http://sbml.org

BioModels Database http://biomodels.net/biomodels

MIRIAM http://biomodels.net/miriam

identifiers.org http://identifiers.org

SED-ML http://biomodels.net/sed-ml

SBO http://biomodels.net/sbo

SBGN http://sbgn.org

COMBINE http://co.mbine.org

URLs

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