Main takeaway . . . The synthetic biologists began as electrical/computer engineers. They literally built the militarized ARPANET. Are they building a symbiotic biocomputational network (merging the ARPANET/Internet with all living biology)?
Co-Founder & CTO
- PhD student in Computational Biology, long experience in academia with a master at the Ecole Normale Superieure (ENS) ULM. Currently finishing his PhD at Harvard University on diverse topics around Evo-Devo of Arthropods.
- Expertise in Data Science and Machine Learning with a focus on sequence data and image analysis.
- Participates to interdisciplinary projects, recently with a collaboration with Marc Santolini on the extraction and analysis the iGEM competition.
- Helped the european union parliament on science policy surrounding green energy transition.
- Open Science and Open Source advocate through participation in multiple projects since childhood. Child of the cypherpunk hacker subculture.
Co-Founder & CEO
- One of the pioneers behind the DIYbio (Do-it-yourself Biology) and Open Science movements and communities. He founded in 2011 La Paillasse in Paris, the first French open lab who became one of the largest ones in the world. Helped hundreds of projects, impacted thousands of people worldwide.
- Bootstrapped synthetic biology in France as an academic researcher. He co-founded the first French iGEM team in 2007. He co-built the first dedicated research lab to synthetic biology in 2008 where he started his PhD. He published 9 peer-reviewed papers during that time.
- Decided to leave academia to focus entirely on building and experimenting with alternatives to a rusty academic system for the production of open knowledge and innovations, through La Paillasse first and now with JOGL.
- Co-Founded in 2015 the synthetic biology company PILI who is bringing the first sustainable alternative to petrochemistry for making dyes at industrial scale. PILI is now a 15 employees-strong R&D company and has raised more than $8M.
- He is a member of the French National Digital Council where he pilots the working group on ecological transition.
- Speaks worldwide on open research, DIYbio, collective intelligence and future of communities, science and innovation at gathering such as TEDx, Lift, OuiShareFest, MIT CommunityBio, CollaborAmerica Brazil, FoundersForum UK, MakerFaire Shenzhen and AfricaOSH.
Co-Founder & CSO
- Team leader in network science at CRI Paris, visiting researcher at the Network Science Institute of Northeastern University and at Harvard Medical School (Boston)
- 10+ years of experience in theoretical physics and network science in prestigious universities (ENS Paris, Princeton, Northeastern, Harvard), with applications to biological, medical, and social contexts
- Study the complex network effects underlying collaboration performance, in particular in Open Science contexts.
- Received in 2018 the Sage Bionetworks “Young Investigators Award” for his work on “Algorithms and the role of the individual”
JOGL’s most popular program has been the OpenCovid19 Initiative, powered by a global community of 4000+ volunteers and experts who create solutions to better prevent, detect, and treat COVID-19, and to help forecast the pandemic’s evolution, with support from the AXA Research Fund.
JUST ONE GIANT LAB
Provisional White Paper
Human civilization is continuously trying to pursue the improvement of its conditions through science and technology. In a pre-digital society, centralized institutions played a key role in associating talents and resources. However, it could only accommodate for a thin layer of actors who could access high-end education, the relevant social network and opportunities. In a digital society where information knows no bound, where prototyping is faster and cheaper, and where social interactions have no geographical limitations, education, talents, resources, and opportunities don’t need to be centralized and exclusive anymore. Moreover, when information becomes open, it favors the development of higher orders of collective and distributed intelligence which can overcome the solving power and efficiency of centralized institutions.
In a world where inequalities are still massive and where unprecedented crises are emerging on a global level, we can’t rely anymore solely on Academia and the Big Tech world. On the one hand, in academia, professional scientists (0,1% of Humanity) work in and career-centric organizations to produce knowledge, tools, and methodologies. In addition, public institutions lack the agility to explore emerging subjects and fast-prototype projects. On the other hand, start-up companies are agile but need to focus on high-margin markets. There is a big gap between what public institutions and companies can do. It is mostly filled by emerging fields of research, problems affecting people who can’t really pay for a solution, challenges that require interdisciplinary approaches and, often, a critical mass of participants.
In order to move beyond centralized organizations, new types of infrastructures are necessary to accommodate both the distributed nature of the multitude and the cost of breaking existing social boundaries based on jobs (like between installed researchers and professional amateurs). If successful, such infrastructures should multiply by at least 10 the number of contributors working on solving the most urgent and important problems on our planet.
Just One Giant Lab (JOGL), is the first research and innovation laboratory accessible to anybody, operating as a distributed, open and massive mobilization platform for volunteer-based, IP-free task solving. JOGL helps sync humanity onto solving our most urgent and important problems using Open Science, Responsible Innovation and Continuous Learning. JOGL partners with academic labs, companies, schools, startups, foundations, NGOs and public services to create massive mobilization on distributed and participatory research programs for understanding and solving Health, Environmental, Social and Humanitarian issues.
- Help the creation of open knowledge, tools, and methodologies to understand and solve our most important and urgent problems
- Give everyone a chance to challenge themselves and learn in the process
- Sync humanity for long-term impact through collaboration
- Go beyond the traditional academic and corporate frameworks
- Provide legitimacy and opportunities to leaders and contributors around the world
- Give contributors direct access to the doers of the world
- Make research and innovation more inclusive, accessible and responsible
- Offer a public window to the world of inventors and doers, their passion, their struggling, and their achievement
- Focus on the 17 sustainable development goals that are defined by the United Nations
By making the process of contributing to solving important challenges accessible and valorizing for anyone, we want to multiply the number of contributors by 10 (from 5 million to 50 million) in 10 years, while making all produced knowledge, tools and methodologies universally open for use and adaptation.
Basic platform architecture
JOGL needs to solve a complex challenge: it needs to provide an attractive tool that easily onboards inexperienced or novice technology users while providing powerful functionalities to handle dynamic project management. Moreover, it aims at becoming a reference for publishing open science and innovation. Bringing those two sides together in the same piece of technology requires carefully crafted UX. Finally, because a lot of knowledge will be stored on JOGL, it will need to be efficiently searched and displayed. To this end, we will bring the power of Artificial Intelligence and data visualization to link and understand published data.
In order to be versatile, and iterate quickly, we decided to have an object-oriented approach. Each object can be connected to another object according to different types of connections (author of, parent of, following, etc). Thus, this allows for changes to be made on parts only, quickly and easily (reduced collateral effects). Moreover, this approach allows us to map and study the landscape of knowledge, collaborations, and organization in a meaningful way using network theory.
We decided to focus on the following core objects:
- ● Feed: Follow and share updates, news, results and opportunities about your project to various communities
- ● Challenge: Aggregates project and allow for communities to solve ambitious goals.
- ● Project: A defined initiative with specific goals, needs, opportunities and leadership.
- ● User: Defined by its interests, skills, activities.
- ● Organization: Moral entity (company, non-profit,…) existing in the physical world.
- ● Community: A network of users, projects, organizations, resources, needs and opportunities.
- ● Needs and resources: Special objects used by JOGL to match people, projects and organizations together
- ● Notifications: Be aware of what happens around you
- ● Direct messaging: You can contact anyone on JOGL directly.Modus OperandiOur experience working with communities and distributed projects has given us an understanding of the key role of “T rust”tomakecollaborationsfruitful.Inordertoestablishatrustfulrelationshipbetweencontributorswhodon’t necessarily know each other, we will introduce roles within JOGL that will state a set of responsibilities for each of them. Succeeding in facing those responsibilities will make you more trustworthy within your community and the global JOGL community. Failing them will however have the opposite effect.Here are the three roles that we will implement at first within JOGL:
- Leader: A legitimate (group of) person who has for responsibility to bring a project to completion or to agiven milestone. A leader is expected to have experience, time, resources to make the project successful. A leader can be for example a researcher, an entrepreneur, a project director within an NGO or public institution.
- Contributor (Jogler): Any person who wants to commit to helping a project on a given need. A contributor’s responsibility is to do what he/she committed on. A contributor is expected to have relevant experience, time and autonomy. A contributor can be for example a professional, a student, an amateur or any person capable of matching the level of difficulty of the need.
- Enabler: An organization who seeks to provide resources and opportunities to impactful projects or who wishes to organize a challenge to birth new initiatives. An enabler’s responsibility is to deliver the resources it promised. An enabler can be a foundation, a research/innovation fund, a big or small company, a non-profit. It can provide tools (software/hardware), skills (pro bono), workspace, free services, visibility and/or funding.
Matching needs, resources and opportunities
While JOGL can be used autonomously by any person, projects or organization, the complex networks of stakeholders that surround specific social/environmental problematics or research subjects are hard to navigate, even for the most professional and experienced organizations. At JOGL, we wish to offer a focusing lens to enable the synchronization of a large number of talents, ideas, resources and stakeholders through the organization of challenges that remains open until a solution is proposed and tested and validated.
It consists in providing incentives through opportunities to gain legitimacy and resources to an existing or new project, thanks to a college of enablers (organizations providing resources), with the help of the larger community of JOGLers. Because the value to gain is higher than with any single project alone, challenges are more ideal to recruit partners and collaborators around a specific problem to solve.
In order to synchronize large communities, JOGL needs the support of AI to smartly sift through the extensive user data generated and recommend useful content and actions for the benefits of the projects. The objective is to help people and communities to efficiently find what they need to achieve their research goals, provide all possible incentives to support projects and challenges. The smart compiler (“Brain”) provides such a solution to recommend content and actions for continuous learning, efficient project management, redundancy reduction and maximizing impact at the individual or group scale.
The Brain will take as input the current network of users, tasks, projects, results existing on the platform as input, and use predictive modeling to recommend new links in this network, providing the user with a prioritized list of relevant objects:
- ● Users to follow
- ● Projects to follow/participate in
- ● Tasks / Opportunities to solve
- ● Resources / Data to use for their own project
- ● Content to exploreFinally, the Brain will learn from user behavior through machine learning, using both usage data (user accepted this recommended task) as well as micro-surveying (button “not for me / not interested” to express lack of interest for a task/person to follow etc)
Example of features (will not necessarily look like that in the end)
- ● Explore global challenges
- ● Find interesting projects working to solve them
- ● Find people passionate about making a change.
● Get recognized! Build the strength of your profile by contributing to solutions
● Gain reputation, badges, and 21st century skills
● Showcase a portfolio of projects
● Explore opportunities and tasks
● Earn reputation and shares by completing them
● Work is validated through peer-review
● Explore the community
● If you find a project or an innovation that is useful, copy it
● As you do, shares are awarded to those that created it,
● Everyone is rewarded, as the sum becomes greater than the parts.
iGEM started out as an experiment to create the synthetic biology industry. Our founders asked the question: Can students work together to build working biological devices within a summer? Now, over a decade later we know the answer is, “Yes!”. The iGEM Competition is intentionally set up as a sandbox for innovation. Teams are not prescribed themes or given specific problems to solve. Instead they are given the space to use synthetic biology to tackle problems however they see fit. As a result, iGEM has become a natural test-bed for new ideas and approaches in synthetic biology.
Throughout our history, we have been taking steps towards an imagined future: synthetic biology without DNA manipulation. In this future, engineers wouldn’t be concerned with samples and assembly because they could synthesize their designs entirely. Instead, the focus would be on the information associated with the system. Eventually, exchanging information about parts would replace the need to exchange physical samples between labs. It would be the data and documentation of your parts that would be important. This year we are taking another step towards making this future a reality.
In the first phase, our partners brought free DNA synthesis directly to teams. Free synthesis allowed teams to focus more on design and characterization, rather than assembly of DNA fragments. Now we are entering the second phase. Through increased synthesis and more access to helpful tools and technologies, iGEMers will have more time to focus on the information that they generate in their projects.
For the past five years, IDT has provided free DNA synthesis to all iGEM teams. With this deal, teams lived in the future. Instead of obtaining DNA fragments from pre-existing samples by PCR or restriction digests, teams could synthesize samples of basic parts. Instead of worrying about assembly, they could synthesize entire devices. Their time was freed up so that they could spend more time on designing, measuring, and documenting their parts. This was a big step towards bringing our vision to life.
This year, synthesis is taking two big roles in the competition:
- More DNA synthesis for teams: In addition to IDT’s generous offer, Twist Bioscience is also offering synthesis to iGEM teams for use in their projects. Combined, that’s up to 30KB of custom DNA per iGEM team.
- DNA synthesis for The Registry: At the end of the season, Twist will synthesize samples of part submissions so that they are available to our community through future DNA Distributions. Teams no longer have to submit physical samples in order to share their parts and grow the Registry collection!
With technology like DNA synthesis at their fingertips, teams have been employing newer assembly methods like Gibson and Type IIS. Beginning in 2019, iGEM is officially fully supporting parts that are Type IIS compatible. With Type IIS assembly methods, teams can build entire devices in a single reaction, which will allow them to design, build, and test more devices in a shorter amount of time.
In our vision of the future, eventually engineers won’t worry about DNA assembly at all if they can obtain fully constructed DNA devices from a manufacturer. However, we need to adopt a technology that will help bridge the gap between today’s reality where synthesized parts are still assembled together, and the future we envision where complete genetic devices arrive in the mail.
The key to success in this vision is information sharing. In a synthesis-rich future, information about a part is far more valuable than the sample that you work with. With information alone, a synthetic biologist should be able to design a biological device, order it, and proceed with testing it as soon as it arrives in the mail.
We are working hard on additional key components in this future focused on information. For example, what about parts and part abstraction in world without assembly? What about measurement? And automation? As we proceed along this path, we will continue to add to this vision.
If you believe bringing synthesis and a focus on information through iGEM is important for the future of the synthetic biology industry, we invite you to join us in bringing our vision to life. Contact us with all ideas, suggestions, comments, or proposals for partnership!
randy AT igem DOT org
For three decades, growth and progress have been the dominant themes of the computer industry. New technologies have emerged, grown, and been replaced, generating an unending stream of excitement. Careers and fortunes have been made. Three decades seems like forever - but it isn’t. Other fields have shown us that exponential growth is followed by consolidation and steady but slower progress.
That is life at the top of the S-curve. If the computer industry has reached the top, a different set of expectations and a different set of technology targets are appropriate for the next decades. We will discuss targets and strategies for innovation in this new environment.
About the speaker:
During his 30-year career in the computer industry, Randy was one of the developers of the ARPANET and early Internet, architect of the Butterfly Parallel Processor, and finally, CTO of Sun Microsystem’s storage division. He now works on Synthetic Biology at the AI Lab.
It may be useful to turn to iGEM’s history to see why this is so…
The story of iGEM begins with two pioneers in synthetic biology: Tom Knight and Randy Rettberg. Tom and Randy were both electrical engineers by training and early developers of the ARPAnet, the famed computer network developed by the U.S. Advanced Research Projects Agency (1969-1989) that was the precursor to today’s internet.
It was the early 2000’s. Tom had become fascinated with the idea of applying engineering principals to biology and had set up a biology lab in MIT’s Artificial Intelligence Lab. Randy, a close friend of Tom’s, was also fascinated with the idea of engineering biological systems and left his job as CTO of Sun Microsystems in 2001 to join Tom in exploring this idea.
The timing proved fortuitous – sequencing of the human genome was nearing completion (the project ran from 1990 to 2003) and DNA synthesis was finally becoming commercially available (Blue Heron, the first DNA synthesis company operated solely in the US, was founded in 1999). DNA sequencing gave researchers the ability to “read” DNA, while DNA synthesis enabled researchers to “write” DNA. With both DNA sequencing and DNA synthesis at hand, researchers could transform physical DNA molecules into pure information, and then back again from pure information into physical DNA molecules. These two technologies together opened the door to whole new approaches for working with biology, including engineering approaches.
Tom and Randy, both well versed in systems engineering, wanted to design and build biological systems using the same techniques they had used in designing and building computer systems. Back in the 1970’s they had used standardized digital parts found in the Texas Instruments TTL Data Book to build digital systems in silicon. Tom came up with the idea of creating a catalogue of standardized biological parts – Biobricks – that could be used to build biological systems in living cells. Tom’s publication “Idempotent Vector Design for Standard Assembly of Biobricks” has become the stuff of lore for synthetic biologists, and was the basis for the Registry of Standardized Biological Parts and the first Biobricks assembly standard.
Left: iGEM Founders; Randy Rettberg, Tom Knight and Drew Endy.
Right: iGEM team MIT 2004
In January 2003, Tom and Randy were joined by Drew Endy, another early pioneer of synthetic biology, and Gerald Sussman, a Professor of Electrical Engineering at MIT, in teaching an Independent Activities Period course. There, in real time, with 16 mostly undergraduate students, Tom, Randy, Drew and Gerald tested whether the approaches that had worked so well for engineering computer systems could work for engineering biological systems. In that course, students focused on designing DNA sequences rather than on the details of constructing DNA in the laboratory. The students’ designs were sent as information via the internet to the DNA synthesis company Blue Heron, and the synthesized DNA was returned to them at MIT.
That fateful course would eventually become the International Genetically Engineered Machine (iGEM) competition. Though 2003 proved challenging – not all of the DNA designs could be synthesized, and not all of the synthesized DNA worked in cells – it marked the beginning of a revolution in synthetic biology.
· Students designed biological systems to make cells blink, based on the based on the Repressilator by Michael Elowitz and Stanislas Leibler;
· Tom Knight implemented his idea for the Biobricks assembly standard;
· the first iteration of the iGEM Registry of Standardized Biological Parts was created;
· iGEM’s Get & Give (& share) policy gave rise to an open, collaborative community of synthetic biologists;
· and the next generation of pioneers in synthetic biology were inspired – three of the co-founders of Gingko Bioworks, one of the most successful synthetic biology companies, participated in the 2003 course.
· iGEM was set up as a summer competition involving five teams: Boston University, CalTech, MIT, Princeton, and University of Texas Austin;
· the iGEM Registry grew to 50 biological parts;
· the values of Human Practices were baked into iGEM (i.e., make something useful, share with others);
· and more pioneers in synthetic biology were inspired – two additional co-founders of Gingko Bioworks were part of the 2004 MIT iGEM team.
· iGEM became a true international competition, with 13 teams from four countries, including Canada, Germany, the United Kingdom, and the United States participating;
· the iGEM Registry grew to 125 biological parts;
· and iGEM was featured in Nature.
And the iGEM firsts have continued:
Today, iGEM continues to inspire pioneers in synthetic biology, having touched the lives of over 40,000 students, their teachers and mentors and communities, and having launched over 150 start-up companies with roots in iGEM. And through the After iGEM program, these pioneers are effecting positive change in the world by engaging local communities as iGEM Ambassadors, representing the synthetic biology research community as iGEM Delegates at international meetings, supporting current teams through the iGEM Mentorship program, and more.
In future posts, you’ll learn even more about iGEM as the pioneering organization of synthetic biology. In the meantime, please keep sending your questions, thoughts, and perspectives on what iGEM means to you to blog [AT] igem [DOT] org!
Tom Knight is an American synthetic biologist and computer engineer, who was formerly a senior research scientist at the MIT Computer Science and Artificial Intelligence Laboratory, a part of the MIT School of Engineering. He now works at the synthetic biology company Ginkgo Bioworks, which he cofounded in 2008.
Work in electrical engineering and computer science
Tom Knight arrived at MIT when he was fourteen. Even though he only started his undergraduate studies at the regular age of 18, he took classes in computer programming and organic chemistry during high school because he lived close to the university. He built early hardware such as ARPANET interfaces for host #6 on the network, some of the first bitmapped displays, the ITS time sharing system, Lisp machines (he was also instrumental in releasing a version of the operating system for the Lisp machine under a BSD license), the Connection Machine, and parallel symbolic processing computer systems.
In 1967 Knight wrote the original kernel for the ITS operating system, as well as the combination of command processor and debugger that was used as its top-level user interface. ITS was the dominant operating system for first Project MAC and later the MIT Artificial Intelligence Laboratory and MIT Laboratory for Computer Science. ITS ran on PDP-6 and, later, PDP-10 computers.
Knight developed a system to use standard television sets as a terminal interface to the PDP-10.
In 1972, Knight designed one of the first semiconductor memory-based bitmap displays. This was later commercialized and led directly to the development of the Bedford Computer Systems newspaper layout system and influenced many of the bitmapped display devices available today. That same year, along with Jeff Rubin, Knight designed and implemented a network file system that provided the first transparent remote file access over the ARPANET.
In 1974, Knight designed and implemented the prototype version of the MIT Lisp Machine processor, with the production version following in 1976. The Lisp Machine was a microprogrammed machine, tuned for high-performance emulation of other instruction sets. The design of the Lisp Machine was directly implemented by both Symbolics and LMI and was the basis of all of their computers. Texas Instruments implemented surface mount and single-chip versions of the architecture in 1983 and 1987, respectively.
Knight collaborated with Jack Holloway in designing and implementing the Chaosnet, a re-engineered version of the Xerox 3 Mbit/s Ethernet. In 1975 this network became the first local area network on MIT’s campus. Chaosnet’s innovation of a preamble bit string for packets was eventually incorporated into the 10 Mbit/s Ethernet standard.
In 1980, Knight participated in the development of the Connection Machine architecture and its original implementation. Other notable and diverse accomplishments during the 1980s included the creation of the first silicon retina in 1981, the creation of a single-chip optical mouse, the design of the Cross-Omega interconnection network architecture, and the design of the Transit multiprocessor interconnection architecture.
During the early 1990s, Knight was involved in the formation of Permabit and of Exa Corporation and the architecture of the latter’s initial version of its FX/1 lattice gas parallel fluid flow computer. Advances included using over-relaxation techniques to make 10x algorithmic improvements in lattice gas computations, landmark CFD accuracies, and correction of misconceptions about the origin of fluid turbulence in simple two-dimensional flow situations. Within the Artificial Intelligence Laboratory, he led the Abacus SIMD project, worked on VLSImicro displays, and made advances in the field of adiabatic (reversible) computing.
Work in synthetic biology
It was also during this period that Knight’s interests in biological systems began. Inspired in part by the work of Harold J. Morowitz, a Yale physicist and biologist, Knight studied biochemistry, genetics, and cellular biology, and set up a biology lab within the MIT AI Laboratory. In this lab he created the concept of the BioBrick plasmid DNA part and began creating a library of BioBricks that could be used to simplify the genetic engineering of Escherichia coli cells. Today, BioBricks form the basis of the enormous annual iGEM (International Genetically Engineered Machine) competition and Knight is sometimes referred to as the godfather of synthetic biology. Knight co-founded Ginkgo Bioworks, a synthetic biology company.
From Wikipedia, the free encyclopedia
Harold Joseph Morowitz (December 4, 1927 – March 22, 2016) was an American biophysicist who studied the application of thermodynamicsto living systems. Author of numerous books and articles, his work includes technical monographs as well as essays. The origin of life was his primary research interest for more than fifty years. He was the Robinson Professor of Biology and Natural Philosophy at George Mason University after a long career at Yale.
Life and career
Morowitz was born in Poughkeepsie, New York. He received a B.S. in physics and philosophy in 1947, an M.S. in physics in 1950, and a Ph.D. in biophysics in 1951, all from Yale University. Morowitz was a professor in the department of molecular biophysics and biochemistry at Yale from 1955 to 1987, also serving as the Master of Pierson College from 1981 to 1986. He spent the rest of his career on the faculty at George Mason University, which he joined in 1988 as Clarence Robinson Professor of biology and natural philosophy. He served as the founding director of the Krasnow Institute for Advanced Study at George Mason from 1993 to 1998. Morowitz was closely associated with the Santa Fe Institute since 1987, where he was Chairman Emeritus of the Science Board. He also served as the founding editor of the journal Complexity.[](Harold J. Morowitz - Wikipedia) In the 1990s he contributed a monthly column on science and society to Hospital Practice.
Morowitz was a longtime consultant for NASA, and served on the committees that planned the quarantine procedures for Apollo 11 and the biology experiments the Viking probe carried to the surface of Mars. He was a member of the science advisory committee for Biosphere 2 in Oracle, Arizona, which, at 3.14 acres, is the largest enclosed ecosystem ever built.
Some leading biophysicists have suggested that Morowitz may have discovered a “fourth law of thermodynamics” when, in 1968, he found that, “in steady state systems, the flow of energythrough the system from a source to a sink will lead to at least one cycle in the system.” Eric D. Schneider, for example, says, “Morowitz’s cycling theorem is the best candidate for a fourth law of thermodynamics.”
The origin of life
Morowitz’s book Energy Flow in Biology laid out his central thesis that “the energy that flows through a system acts to organize that system,” an insight later quoted on the inside front cover of The Last Whole Earth Catalog. He was a vigorous proponent of the view that life on earth emerged deterministically from the laws of chemistry and physics, and so believed it highly probable that life exists widely in the universe.
Harold J. Morowitz
Born December 4, 1927
Poughkeepsie, New York, U.S.
Died March 22, 2016 (aged 88)
Alma mater Yale University
Institutions George Mason University
“Harold was a key figure and friend in the development and maturation of the Santa Fe Institute – one of those figures who personifies the Institute’s ambition and mission,” says SFI President David Krakauer. “He is greatly missed.”