Investigators whose scientific safari takes them into a dense jungle of proteins, genes and signaling pathways owe it to themselves and their funding agencies to check out Robert Hoffmann's iHOP. When I was in college this acronym stood for International House of Pancakes, but the scientific iHOP stands for Information Hyperlinked Over Proteins and is even more satisfying. This free tool is an ingenious combination of database and text mining that I use for every project I work on. It's especially helpful when your work takes a sudden turn into a portion of a signaling network you haven't worked with before.
The most useful feature, in my view, is that text-mining results yield complete sentences extracted directly from the scientific literature and referring to the gene or protein of interest. Each sentence is followed by a link to the abstract, and every list of sentences can be sorted, filtered, or searched. Hoffmann's concept is clean, simple and powerful. New and useful features appear regularly, and Hoffmann is a thoughtful and insightful correspondent in response to e-mailed questions and suggestions.
For targeted searching of the scientific literature, iHOP beats Google. Try iHOP. I bet you bookmark it.
Sunday, April 26, 2009
Sunday, February 15, 2009
The Audacity of Synthesis
As a former academic with a passion for quantitative biology, I retain considerable interest in the organizational approaches taken by the world's universities. What surprises me is that in an age almost universally described as an era of synthesis, we continue to build academic departments as if reductionism was still the order of the day.
I remember years ago smiling at the umbrage taken by biochemistry grad students at the annual meeting of the Federation of American Societies for Experimental Biology when they discovered that their badges were emblazoned with the number "2" while those of us from the no-longer-dominant American Physiological Society displayed the proud "1." They were disconcerted to discover that ASBC had started as a splinter group within APS. Indeed, only rarely have groups of academics who develop a novel shared perspective chosen to remain within their parent organization. Young turks would not be turks if they ceded leadership positions to their elders.
This rebelliousness has provided the founding impetus for countless disciplines. I want to argue that the time has come for retrograde rebellion. Today's young turks should be merging disciplines, not creating new ones.
Unfortunately, these young turks are still reading the "Young Turks' Handbook" they found in their chairman's desk drawer. They act as if the only way to be a young turk is to form a splinter group and make it powerful. Biophysics, Mathematical Biology, Biological Engineering, Biomedical Engineering, Bioengineering, Systems Physiology, Systems Biology, Integrative Bioinformatics, Computational Biology, Physical Biology, Theoretical Biological Physics. What really separates these "disciplines?" What are these Turks thinking?
Dick Ross, one of the great deans at The Johns Hopkins School of Medicine, once floated the idea of reducing all our departments to two: basic science and clinical. In retrospect, it's a brilliant idea. At the time, of course, the faculty and the department chairs were aghast. We suspected he was just tired of negotiating with 30 brilliant adversaries about how to best allocate scarce resources. In hindsight, who can blame him. But you won't be surprised that the established academic fiefdoms rose up en masse to resist the inevitable loss of power. Today, however, a more compelling case can be made for Dr. Ross' idea.
Indeed, the rebels of the next few decades might be well-advised to work toward unification, not further secession. Where are the leaders bold enough and confident enough to work together instead of working apart? Where is the biomathematics department that is willing to join a cell biology department, or the biochemistry department willing to be merged with a physiology department? In the past, such events have always been seen as deaths. We need enlightened leaders who see them as births and who run the merged department with the best interests of ALL its members at heart.
The same could be argued for scientific societies, scientific journals and scientific meetings.
If the 21st century is to become the age of biological synthesis, then our academic structures might usefully reflect this. Academic departments proliferated during the age of reductionism. Perhaps they should contract during an age of synthesis.
Leaders in biological synthesis will recognize they need the perspectives of ALL the former reductionists in order to succeed. What better way to achieve this than to bring all those reductionists together again, in their academic departments, their professional societies and their national meetings?
I remember years ago smiling at the umbrage taken by biochemistry grad students at the annual meeting of the Federation of American Societies for Experimental Biology when they discovered that their badges were emblazoned with the number "2" while those of us from the no-longer-dominant American Physiological Society displayed the proud "1." They were disconcerted to discover that ASBC had started as a splinter group within APS. Indeed, only rarely have groups of academics who develop a novel shared perspective chosen to remain within their parent organization. Young turks would not be turks if they ceded leadership positions to their elders.
This rebelliousness has provided the founding impetus for countless disciplines. I want to argue that the time has come for retrograde rebellion. Today's young turks should be merging disciplines, not creating new ones.
Unfortunately, these young turks are still reading the "Young Turks' Handbook" they found in their chairman's desk drawer. They act as if the only way to be a young turk is to form a splinter group and make it powerful. Biophysics, Mathematical Biology, Biological Engineering, Biomedical Engineering, Bioengineering, Systems Physiology, Systems Biology, Integrative Bioinformatics, Computational Biology, Physical Biology, Theoretical Biological Physics. What really separates these "disciplines?" What are these Turks thinking?
Dick Ross, one of the great deans at The Johns Hopkins School of Medicine, once floated the idea of reducing all our departments to two: basic science and clinical. In retrospect, it's a brilliant idea. At the time, of course, the faculty and the department chairs were aghast. We suspected he was just tired of negotiating with 30 brilliant adversaries about how to best allocate scarce resources. In hindsight, who can blame him. But you won't be surprised that the established academic fiefdoms rose up en masse to resist the inevitable loss of power. Today, however, a more compelling case can be made for Dr. Ross' idea.
Indeed, the rebels of the next few decades might be well-advised to work toward unification, not further secession. Where are the leaders bold enough and confident enough to work together instead of working apart? Where is the biomathematics department that is willing to join a cell biology department, or the biochemistry department willing to be merged with a physiology department? In the past, such events have always been seen as deaths. We need enlightened leaders who see them as births and who run the merged department with the best interests of ALL its members at heart.
The same could be argued for scientific societies, scientific journals and scientific meetings.
If the 21st century is to become the age of biological synthesis, then our academic structures might usefully reflect this. Academic departments proliferated during the age of reductionism. Perhaps they should contract during an age of synthesis.
Leaders in biological synthesis will recognize they need the perspectives of ALL the former reductionists in order to succeed. What better way to achieve this than to bring all those reductionists together again, in their academic departments, their professional societies and their national meetings?
Wednesday, February 11, 2009
Courage
Much of experimental biomedical research is published without any reference to modeling. And we all know modelers who publish without any reference to experimental data. More and more, however, investigators on both sides of the fence are calling for interaction.
The two cultures seem to resist this interaction, and we have all heard, or even spoken, the various rationales for remaining aloof, but I'm wondering if the real problem is a dearth of courage.
Modern science has placed a huge premium on "being right." Investigators often go to extreme lengths to defend their conclusions or their theories. No one enjoys being told that they are wrong, especially in public or in an NIH CSR summary statement.
For experimentalists, it's a great risk to convert our pet theory to an explicit model; we might find it cannot explain even our own data, much less the data in our competitors' papers. Modelers face the same risk when we take the chance of comparing our model's predictions directly to experimental data. We may find to our horror that years hard work were for naught - that our model is hopelessly inadequate.
But if we don't take these risks, we are casting a no-confidence vote for modern science. We may sustain our careers at the cost of hampering real progress.
Wouldn't our science be more important and more fun if we ceased trying to protect ourselves from "a beautiful theory destroyed by an ugly fact."? Courage!
The two cultures seem to resist this interaction, and we have all heard, or even spoken, the various rationales for remaining aloof, but I'm wondering if the real problem is a dearth of courage.
Modern science has placed a huge premium on "being right." Investigators often go to extreme lengths to defend their conclusions or their theories. No one enjoys being told that they are wrong, especially in public or in an NIH CSR summary statement.
For experimentalists, it's a great risk to convert our pet theory to an explicit model; we might find it cannot explain even our own data, much less the data in our competitors' papers. Modelers face the same risk when we take the chance of comparing our model's predictions directly to experimental data. We may find to our horror that years hard work were for naught - that our model is hopelessly inadequate.
But if we don't take these risks, we are casting a no-confidence vote for modern science. We may sustain our careers at the cost of hampering real progress.
Wouldn't our science be more important and more fun if we ceased trying to protect ourselves from "a beautiful theory destroyed by an ugly fact."? Courage!
Monday, December 22, 2008
Non-binding recruitment
I have a problem with recruitment.
The cell biological literature is replete with references to recruitment of proteins by other proteins. Typical is this one from the field of protein trafficking: "Arf recruits COP-I to the Golgi membrane."
At best this phrase introduces an unconscious synonym for binding; at worst it contains elements of vitalism or at least a Maxwellian demon. I've asked many biologists what they mean by recruitment, especially when they use binding and recruitment in the same paragraph. Responses vary but a summary might be: "Recruitment is definitely distinct from binding and I know recruitment when I see it."
So here is the challenge to readers of this post: Give us an example of recruitment that highlights the difference between recruitment and binding. Personally, I think this impossible.
The cell biological literature is replete with references to recruitment of proteins by other proteins. Typical is this one from the field of protein trafficking: "Arf recruits COP-I to the Golgi membrane."
At best this phrase introduces an unconscious synonym for binding; at worst it contains elements of vitalism or at least a Maxwellian demon. I've asked many biologists what they mean by recruitment, especially when they use binding and recruitment in the same paragraph. Responses vary but a summary might be: "Recruitment is definitely distinct from binding and I know recruitment when I see it."
So here is the challenge to readers of this post: Give us an example of recruitment that highlights the difference between recruitment and binding. Personally, I think this impossible.
Thursday, December 18, 2008
How to measure the success of biomedical modeling
Today's edition of the Predictive Biomedicine Newsletter includes an interesting piece from John Russel based on his attendance at IBM's invitation-only Modeling and Simulation Summit. Full disclosure: I was not invited.
Russell makes the provocative suggestion that perhaps what is needed to convince decision makers to embrace modeling is a central repository of modeling and simulation success stories. Many of us working in systems biology would agree with this recommendation. Many of us have told our students that this is the way to sell modeling to skeptical colleagues. But there is a problem and Russell adumbrates it in his next paragraph.
"Measuring modeling and simulation’s contribution to a project’s success is also a challenge. It’s often not easy to demonstrate that modeling was decisive versus incremental to a project’s overall success. Even when modeling is successful, other researchers on the project may feel they would have come to the correct answer soon enough without modeling."
The problem in a nutshell is how to define "decisive success" so that all the stakeholders agree on the successful/unsuccessful outcome at the end of the test.
To me what is missing is the control group.
We need expert teams aiming to solve the same problem with and without modeling. This is not easy to arrange. Moreover, we will still have the problem of how to agree that the problem is solved. And we won't know if one team is simply smarter or luckier than the other.
From another perspective, however, the experiment is already underway. Companies of all sizes whose business models are to sell the fruits of modeling and simulation are hard at work in biological, biomedical and pharmaceutical discovery. These are the people who provide the necessary tests. I'm emphatically NOT speaking about large research and development enterprises for whom an investment in modeling and simulation is mere bet hedging. I'm talking about those companies whose foundation is modeling. Companies like Entelos, and Merrimack and GNS who have bet the ranch on the conviction that human biology is far too complex to be manipulated successfully by the unaided human brain. Companies who understand that biology and medicine are HARDER problems than engineering a new airplane or a new chip or a new international communications network, or a new approach to managing financial markets or natural resources. Companies who recognize that no one can solve such problems without the aid of basic physical and chemical principles encoded in some sort of modeling framework. These companies have modeling built into their genomes. They absolutely KNOW that modeling is an essential element of biomedical research. These are the companies that will increase in value if we are right, and decrease in value if we are wrong. For the record, I myself have invested more than 40 years in applying modeling to biomedical research and 12 years in building small companies with modeling foundations. I believe.
What we cannot predict is which such companies will find a truly effective approach to leveraging modeling in biomedical and pharmaceutical research and development. Indeed, it may well be that a modeling division in an established pharmaceutical company will find one first.
But if you are looking for a list of modeling success stories, you could do a lot worse than tracking the fortunes of companies large and small, publicly held, venture-backed, or privately held, who are 100% committed to finding the most effective way to manage the modeling process in biomedical research. One of these, or perhaps one that is still just a great idea in someone's head is, without a doubt, going to make multiple billionaires AND a far healthier planet.
Russell makes the provocative suggestion that perhaps what is needed to convince decision makers to embrace modeling is a central repository of modeling and simulation success stories. Many of us working in systems biology would agree with this recommendation. Many of us have told our students that this is the way to sell modeling to skeptical colleagues. But there is a problem and Russell adumbrates it in his next paragraph.
"Measuring modeling and simulation’s contribution to a project’s success is also a challenge. It’s often not easy to demonstrate that modeling was decisive versus incremental to a project’s overall success. Even when modeling is successful, other researchers on the project may feel they would have come to the correct answer soon enough without modeling."
The problem in a nutshell is how to define "decisive success" so that all the stakeholders agree on the successful/unsuccessful outcome at the end of the test.
To me what is missing is the control group.
We need expert teams aiming to solve the same problem with and without modeling. This is not easy to arrange. Moreover, we will still have the problem of how to agree that the problem is solved. And we won't know if one team is simply smarter or luckier than the other.
From another perspective, however, the experiment is already underway. Companies of all sizes whose business models are to sell the fruits of modeling and simulation are hard at work in biological, biomedical and pharmaceutical discovery. These are the people who provide the necessary tests. I'm emphatically NOT speaking about large research and development enterprises for whom an investment in modeling and simulation is mere bet hedging. I'm talking about those companies whose foundation is modeling. Companies like Entelos, and Merrimack and GNS who have bet the ranch on the conviction that human biology is far too complex to be manipulated successfully by the unaided human brain. Companies who understand that biology and medicine are HARDER problems than engineering a new airplane or a new chip or a new international communications network, or a new approach to managing financial markets or natural resources. Companies who recognize that no one can solve such problems without the aid of basic physical and chemical principles encoded in some sort of modeling framework. These companies have modeling built into their genomes. They absolutely KNOW that modeling is an essential element of biomedical research. These are the companies that will increase in value if we are right, and decrease in value if we are wrong. For the record, I myself have invested more than 40 years in applying modeling to biomedical research and 12 years in building small companies with modeling foundations. I believe.
What we cannot predict is which such companies will find a truly effective approach to leveraging modeling in biomedical and pharmaceutical research and development. Indeed, it may well be that a modeling division in an established pharmaceutical company will find one first.
But if you are looking for a list of modeling success stories, you could do a lot worse than tracking the fortunes of companies large and small, publicly held, venture-backed, or privately held, who are 100% committed to finding the most effective way to manage the modeling process in biomedical research. One of these, or perhaps one that is still just a great idea in someone's head is, without a doubt, going to make multiple billionaires AND a far healthier planet.
Monday, December 8, 2008
Slash and burn
Slashes (/) appear frequently in the biological literature. But I'm worried we haven't come to a universal agreement on what they mean. Perhaps there already is a standard recommendation and some reader will take pity on me and post a URL, but I've asked a few biologists and heard multiple answers. I've also run a few Google searches and here is what I know at this point.
The most common use of / is to separate the individual items in a list of synonyms. For example: Rbx1/Roc1/Hrt1. Three names for the same protein.
Another usage is to specify a particular protein component in a multimeric complex. For example: SCF/Slimb represents a generic complex named SCF, which is made up of Skp1, Cul1, Rbx1, and one or another F-box protein. The notation SCF/Slimb indicates an SCF complex in which Slimb is the F-box protein.
And occasionally you see complexes specified using a slash as a ditto. Common examples are Arp2/3 and chlorophyll a/b.
I'm not a text miner but as a consultant I am often reading in a field of cell biology that is entirely new to me. Once I was led wildly astray by an author who named a complex by separating its components with slashes. In that case A/B/C meant a heterotrimer made up of three proteins: A, B, and C. This would be a text mining nightmare. Indeed, I found several bioinformatics papers whose sole purpose was to identify synonyms in biological text. Not an easy job.
I'm not sure what the forum should be, but it seems obvious to me that general agreement about the meaning of / would be worth promoting.
Personally, I'd like to see the colon (:) used as the separator of a complex's components. For example, The SCF complex mentioned above would be symbolized as:
Slimb:Skp1:Cul1:Rbx1. But that's another post.
What do YOU mean when you separate symbols with slashes?
The most common use of / is to separate the individual items in a list of synonyms. For example: Rbx1/Roc1/Hrt1. Three names for the same protein.
Another usage is to specify a particular protein component in a multimeric complex. For example: SCF/Slimb represents a generic complex named SCF, which is made up of Skp1, Cul1, Rbx1, and one or another F-box protein. The notation SCF/Slimb indicates an SCF complex in which Slimb is the F-box protein.
And occasionally you see complexes specified using a slash as a ditto. Common examples are Arp2/3 and chlorophyll a/b.
I'm not a text miner but as a consultant I am often reading in a field of cell biology that is entirely new to me. Once I was led wildly astray by an author who named a complex by separating its components with slashes. In that case A/B/C meant a heterotrimer made up of three proteins: A, B, and C. This would be a text mining nightmare. Indeed, I found several bioinformatics papers whose sole purpose was to identify synonyms in biological text. Not an easy job.
I'm not sure what the forum should be, but it seems obvious to me that general agreement about the meaning of / would be worth promoting.
Personally, I'd like to see the colon (:) used as the separator of a complex's components. For example, The SCF complex mentioned above would be symbolized as:
Slimb:Skp1:Cul1:Rbx1. But that's another post.
What do YOU mean when you separate symbols with slashes?
Friday, November 28, 2008
The Desire for Simple Models
If you spend much time among modelers you will occasionally find one for whom some models are "too complex." One simple measure of complexity would be the number of state variables involved in the model being discussed. Many years ago, my good friend Art Shoukas was giving a lecture on cardiovascular physiology and showed a slide of Arthur Guyton's famous model of the cardiovascular system. This model contains on the order of 100 state variables. Being a biomedical engineer, it was natural for Art to present this diagram, but what surprised me was that he pointed to Arthur's creation and said, "...this model is useless." Perhaps from the perspective of a first-year medical student, Art was right. The Guytonian diagram was indeed beyond comprehension as an integrated whole. But I remembered, magnifying glass in hand, working my way through that entire diagram when I was in grad school. To me it had (and still has) enormous power both didactic and scientific. So why is it that some people insist on simple models?
It may be simply that their pencil and paper analysis skills are better than mine. But I think there is something more fundamental at work here. I sense that they don't find a model satisfying unless they can "understand" it - by which I think they mean they can calculate its predictions without the need for simulation.
I was reminded of this today while reading Alfred Tauber's article, The Immune System and Its Ecology, in the April 2008 number of Philosophy of Science. Tauber has a keen eye for the evolution of modeling approaches in immunology while maintaining an agnostic position on which is best. My reverie began when he cited Levins and Lewontin (1985) who "... observed that the computer models of 25 years ago were not holistic, but rather only expressions of large scale reductionism." Surely they had the Guytonian model in mind.
Most human beings prefer models that explain everything and yet still fit inside a human brain where they can be manipulated successfully.
I've never felt this way and this is the point I wanted to make in this post. I think there are three reasons why "large scale reductionism" is the most useful approach for computational cell biology.
1) It proceeds by assembling the reductionist pieces adduced by individual scientists, without asserting that the model works in some holistic, known way. In effect, the assembled model serves as a working hypothesis that needs to be tested against many, many experimental data sets collected at all levels of physiological organization.
2) In general, we are not (at least I am not) smart enough to know how to simplify a complex model so that the simplification can serve as a trustworthy surrogate for the "full" system. Indeed, one of my teachers, Mones Berman, concluded that complex models should be built first and then simplified as it becomes clear what is essential and what is not.
3) It's vital for multi-scale models to retain the molecular details because it is at the molecular level that we most often intervene in our efforts to improve quality of life for the people who pay us to do biomedical research.
It may be simply that their pencil and paper analysis skills are better than mine. But I think there is something more fundamental at work here. I sense that they don't find a model satisfying unless they can "understand" it - by which I think they mean they can calculate its predictions without the need for simulation.
I was reminded of this today while reading Alfred Tauber's article, The Immune System and Its Ecology, in the April 2008 number of Philosophy of Science. Tauber has a keen eye for the evolution of modeling approaches in immunology while maintaining an agnostic position on which is best. My reverie began when he cited Levins and Lewontin (1985) who "... observed that the computer models of 25 years ago were not holistic, but rather only expressions of large scale reductionism." Surely they had the Guytonian model in mind.
Most human beings prefer models that explain everything and yet still fit inside a human brain where they can be manipulated successfully.
I've never felt this way and this is the point I wanted to make in this post. I think there are three reasons why "large scale reductionism" is the most useful approach for computational cell biology.
1) It proceeds by assembling the reductionist pieces adduced by individual scientists, without asserting that the model works in some holistic, known way. In effect, the assembled model serves as a working hypothesis that needs to be tested against many, many experimental data sets collected at all levels of physiological organization.
2) In general, we are not (at least I am not) smart enough to know how to simplify a complex model so that the simplification can serve as a trustworthy surrogate for the "full" system. Indeed, one of my teachers, Mones Berman, concluded that complex models should be built first and then simplified as it becomes clear what is essential and what is not.
3) It's vital for multi-scale models to retain the molecular details because it is at the molecular level that we most often intervene in our efforts to improve quality of life for the people who pay us to do biomedical research.
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