Dimensions of SysBio: Read Article 07 S. Huang and
J. Wikswo. Dimensions of systems biology. In: Reviews of Physiology,
Biochemistry and Pharmacology, edited by S. G. Amara, E. Bamberg, T.
Gudermann, S. C. Hebert, R. Jahn, W. J. Lederer, R. Lill, A. Miyajima,
and S. Offermanns, 2006, p. 81-104. Post a PCRC on the Blog.
This is to provide another view of a more compact phase space for describing the complexity of systems biology.
The Systems Biology of COVID-19 and the SARS-CoV-2 virus. The class will build a foundation that includes the emergence of complexity, simple biological subsystems, their reductionist and equivalent toy and organ-chip models, and the measurements required to specify model architecture and parameters. Applications to biology, physiology, medicine, chemical and biological defense, pharmacology, drug discovery, and toxicology. UGrad: PHYS 240 01 and BME 290B; Grad: PHYS 326 and BME 395C.
0. Knew:
ReplyDeleteI knew about the use of non-mutually exclusive multidimensional phase space to explain the complexity of systems biology. I knew about the many spatial and temporal scales that need to be considered when looking at biological systems. I knew about the need for understanding and controlling feedback loops as discussed at the end of the temporal complexity section.
1. Learned:
I learned about five dimensions that can be used to explain the complexity of systems biology and that complex does not mean complicated. I learned about the spectrum of models used to describe biological systems, specifically the extreme of abstraction and the other extreme of specific, detailed models. I learned that there is a fundamental limit to modeling. I learned about thinking of biological systems as the union of analog and digital computing machines and how useful this is (distinct from the mathematical modeling described in the previous section), but that there is also a limit to this approach (computational irreducibility).
2. Pressing Questions:
Are there any other useful dimensions that could be added to these five dimensions (since this list was made about ten years ago)? Or are there areas of study in systems biology that have developed that do not fit into theses five dimensions?
Is there are trend relating the various spatial and temporal scales for processes? For example, do processes that occur on small spatial scales tend to only operate on small temporal scales as well (and vice versa), or is there no such trend?
3. Presentation Topic:
It was beyond the scope of the paper to discuss specific systems biology models, and the paper just discussed the extremes of the types of models that are used. I would like a presentation over some specific models that are being used today. These examples would also give the complexity discussed in the paper even more tangibility.
4. Thoughts:
This paper was a great review on the complexity and challenges facing systems biology. It was nice to be able to relate specific examples of what we have already covered in class to the topics discussed in this paper.
Ha.. I didn't see your #2 until after I posted by response. I was wondering if these 5 are all encompassing as well. I thought of a biochemical dimension since chemical balances can play a huge role in biology; however, I'm not sure this would necessarily be its own dimension or whether it would fall into a listed category.
Delete2) I have not yet been convinced of any more than five dimensions. Biochemical = molecular
Delete3) We will get to at least one classical ODE model.
0. KNEW:
ReplyDeleteI knew the difference between a complicated system and a complex system. My favorite analogy to draw a distinction between these ideas is found in a book by Michael Lewis, "A car key is simple. A car is complicated. A car in traffic is complex." I was familiar with the idea of systems biology, hence the name of the class, but much like the consensus I had no formal definition. Despite this lack of a definition, I knew that a goal of systems biology was to try and overcome the limitations that are present in molecular biology due to "reductionism" in literature.
1. LEARNED:
I learned that the framework for systems biology can be constructed by five dimensions: molecular complexity, structural complexity, temporal complexity, abstraction and emergence, and algorithmic complexity. Each of these structures has its individual complexities in order to distance themselves from previous reductionist approaches. I learned that a goal of the new era of systems biology was to enter a stage of "theories with data (and vice versa)" and to depart from previous stages of "theories but no data" and "data but no theories." In addition, I learned that this five-dimension approach to systems biology aims to embrace the complexities of this multidisciplinary effort rather than simply characterizing specific proteins and pathways.
2. PRESSING QUESTIONS:
How important is it to have a unified definition of systems biology before creating specific systems biology models? The paper states that systems biology is a rising sub-discipline of biology, meaning more and more people are trying to understand this issue. So would a formal definition create a unified approach to "solving" this issue, or would it create unwanted reductionist characteristics?
3. PRESENTATION TOPIC:
Objectives and clarifications of the five dimensions that are described in the paper, specifically that of abstraction and emergence.
4. THOUGHTS:
I enjoyed learning more about how systems biology is trying to address the complexity that is inherent in understanding biology, and thus learning more about the goals of this class. This article helped me understand a new perspective of systems biology as an awareness of previous limitations.
0) Good analogy
Delete1) Yes.
2) The unified definition is not critical in building a model, but it could really help define which model is worth building, and why.
3) We will discuss this a bit more if there is time.
4) Good - one key goal in this course is perspective!
Kelly McGee
ReplyDeleteArticle 8a
Engineering Challenges for Instrumenting and
Controlling Integrated Organ-on-Chip Systems
0: Knew: I was aware that the physiological output of different organs was different, even when the relative size of those organs were accounted for. I was aware of the importance of volume-to-surface-area ratios in the size of vasculature.
1: Learned: I was made aware of how important the issue of scaling is in creating multi-organ homunculi. I was also informed of the benefits in focusing on physiological output rather than simple scaling by size.
2: Questions: What are some current examples of how engineers are approaching the challenges of creating microfluidic devices that model vasulature? Could we examine some work from VIIBRE in this area?
3: Presentation: The various organs and the amount/strength of effect of the relevant biochemical messaging pathways that connect them, and what size each is in phase space.
4: Comments: Interesting article. I liked it.
Your comment is for another paper! Check the blog headings for the two articles! Go to the box for the correct ones - https://vanderbilt.app.box.com/files/0/f/11327518883/C08_2016_09_20_Dimensions_BioNEMS
DeleteAck! My Bad.
Delete1: Learned: I learned that you should always check the box for the correct paper, rather than googling the title from the gmail bump.
Chinowsky_TheorSysBio_PCRC_09122016
ReplyDelete0. Knew
I’ve had conversations in the past about the shift from “an era of data but no theories” to “theories with data” in terms of biology, specific in terms of what would be considered dimension 2 in the context of this paper. Throughout the first month of this class, I have become increasingly aware of the complexity that systems biology must encompass if we wish to gain an understanding of the entirety. However, I had mostly been thinking about this in terms of the molecular and structural basis. I’ve also briefly looked into the chaotic nature of biology previously, and it’s a topic I would love to explore further.
1. Learned
This paper explained the scope of system biology in terms of five dimensions (which are “quasi-orthogonal”, which I found important), and gave a brief overview of what each of these dimensions encompasses. The reason I found the “quasi-orthogonal” bit important is because I learned that while each of these dimensions does have unique complexities and difficulties, they cannot be completely separated from each other, and cannot truly exist as their own, unique, phase space. I liked learning more about the temporal oscillations and complexities, which I had never really considered. I also learned that one of the main challenges of several of these dimensions is that they lack the high-throughput methods that allowed us to use a reductionist methodology on dimension 1 type problems.
2. Pressing
Do complexity and molecular crowding play a role in why “standard devices for actuation, control, and sensing that would allow the manipulation of more complex experimental systems” (pg 88) have not been developed yet?
3. Presentation
I think it’d be interesting to explore the fractal structures that exist in the life sciences
4. Thoughts
Multidisciplinary certainly seems to be the buzzword of system biology. I dig it! I think embracing a multidisciplinary approach will only result in further scientific advantages. I also liked that this paper didn't frame system biology in the context of drug discovery.
0-1) It looks like you are gaining a good perspective.
Delete2) Yes - controlling the inside is a big challenge!
3) They are fascinating, but beyond what we can cover in this class. Start digging on your own.
4) Drug discovery is benefiting from systems biology, but SB is vastly broader. Personally, I think that the systems biology of development has some major low-hanging fruit!
0. Knew:
ReplyDeleteI knew of systems bio and the individual components discussed (ie size and time scales) of some of the interactions that occur between systems. A background of physiology was helpful to so that I could put some of the discussion in perspective.
1. Learned:
I learned 5 different aspects (dimensions) from that must be considered when developing an organ on a chip and interconnecting those into a system. This was very interesting for me to read about and give me new perspective on how to approach a modeling project I'm working on for my research. I learned a lot from Dimension 4, abstraction and emergence that lead to a better understanding of some of our discussion in class. Also appreciated reading about open vs closed loop (not a lot, but the 1 sentence helped put that into perspective).
2. Questions:
Are there other "dimensions" that should be considered? I thought of biochemical complexity immediately, but maybe this falls into one of the other dimensions discussed or maybe this isn't necessary to consider for this discussion?
3. Presentation:
Abstraction and emergence in sysbio
4. Comments:
Interesting approach to describe the complexity of systems bio that can be related to a lot of biological modeling problems. The table was a great summary for the paper!
0) A background in physiology is extremely important as people start trying to assemble systems.
Delete1) Abtraction and emergence are empowering concepts.
2) Don't know - let's see what turns up. I've not yet found any. Biochemical is molecular.
3) We will discuss this throughout the semester. Try to remind us when appropriate.
4) Good!
Ben Terrones
ReplyDeleteSysBio16 Asgn_8A_Class_08_Article_07_2016_09_20
0. KNEW:
I knew that there are multiple definitions of systems biology, and the general idea is to look at components in biology as functioning parts of the whole system. I knew most of the information in the first dimension, molecular complexity, and the main ideas of the second and third dimensions.
1. LEARNED:
I learned that it is possible, and even helpful, to split systems biology into five dimensions. There was a lot of the information in the first three dimensions but I knew the basics of them. I learned about the spectrum of models including the detailed modeling end and the end that represents higher abstraction. In reality, a practical model would be somewhere in the middle. However, in some scenarios the only way to accurately predict behavior is to create an exact model without simplification. The most interesting part to me was dimension 5. It may be that there is an upper limit to the complexity we are able to simplify with mathematical modeling and abstraction. If it is not possible to use mathematical shortcuts and computers aren’t powerful enough to model reality, then we are left with experimentation.
2. PRESSING ?:
This is kind of a general question, but since this was published about 10 years ago, what has changed in terms of computers’ ability to perform these kind of calculations? Is it still an unimaginable feat or could future technology make it possible to compute the necessary number of calculations to simulate biological phenomena?
3. PRESENTATION:
Pattern formation, graph theory in the analysis of network architectures.
4. THOUGHTS:
I found the part about the system being so sophisticated that the system used to describe it can’t “outrun” it, to be very interesting. This was a very interesting paper especially the algorithmic complexity section.
0) OK
Delete1) Good that you learned this!
2) Some problems are so hard that simple increases in computer power won't make all that much difference. Some of these problems scale exponentially with complexity.
4) Yes! I think algorithmic complexity is very interesting.
PCRC
ReplyDeleteSylvia Morrow
Asgn8A_S. Huang and J. Wikswo. Dimensions of systems biology.
0. KNEW: There were several concepts that I understood at a surface level from previous papers we've read and discussion in class. For example, the idea of systems biology addressing the fact that "the key operation is multiplication rather than addition—the whole is the product of high-order combinatorial multiplication, not a simple linear summation" (pg 84) and some of the control paper concepts such as differences between model approaches, feedback control, sensors, and actuators.
1. LEARNED: I found the 5-dimensional hyperspace to be a helpful illustration of how current biological studies are generally divided which, by association, led to a much better understanding of where growth is most valuable. I'm not convinced that I fully comprehend the implications of "the subconscious but widely held belief that knowing all the component parts of a system and their wiring diagram is equivalent to understanding that system" (pg 85) but found it to be thought provoking. I was aware that there was some level of chaotic behavior at smaller biological scales, but learning that "Chaotic behavior...has been associated with the healthy state, whereas loss of this type of temporal complexity is observed in disease states" (pg 89) was new and very interesting knowledge.
2. PRESSING ?:
--How close are we to producing working organs in the lab? Have efforts been made to connect these? What is the ethical discussion concerning producing human-like organism structures? Are OoC the simpler or more ethical solution?
--A semi-random question I had while reading the paper: How well studied is the variation among different people groups? Does modern medicine bias its research towards western bodies?
--What justification is there for talking about genes as the smallest components we're interested in instead of looking at the subcomponents of genes (in terms of the structural complexity dimension)?
--Have there been any significant changes with respect to "Few molecular biologists have fully embraced the idea of a state space to conceptualize dynamic behavior. Instead, they mostly operate in the domain of network architectures (topologies) as evidenced by the preoccupation with pathways and network charts as explanatory schemes." (pg 93) since 2006?
3. PRESENTATION:
--Maybe a short presentation with a qualitative description of how mathematical and computer science tools are able to contribute to systems bio?
4. THOUGHTS: A great paper - very well within the realm of my understanding. This course has definitely given me a greater appreciation for the complexity of biology, and I found that the "multiple layers of 'clever hacks'" idea to be a different perspective on this same complexity. In physics it's common to patch together a computer model so that in time it becomes unnecessarily complex, but at some point someone will just re-write the code with a structure that accommodates the progress made thus far. I would be interested in hearing if there is some biological equivalent to this re-write (I realize the total overhaul is nonphysical, but perhaps some smaller scale?)
0) Good!
Delete1) Both good things to learn.
2) Now, and yes.
Interindividual variations are being studied. The microbiome is really broadening our perspective.
Genes are the lowest level, unless you consider microRNA for regulation * * Role of Micro RNAs * * Volunteer needed * *
State space has a growing audience. Networks are still supreme.
3) We will see this later.
4) The current challenge seems to me to be to devise a layered abstraction!
Kelly McGee
ReplyDeleteArticle 8a
0: Knew: In regards to genomic and other "omics" I was aware of the technological advances and large amounts of funding that have transformed the field over the past couple of decades. I was aware of the incredible complexity at the molecular/genetic and structural levels. Furthermore, I was aware of the fact that we currently cannot fully analyze every aspect of biology.
1: Learned: The paper's language helped me to come to terms with the idea of "emergent phenomena." Quite frankly, I was still of the opinion that if we could fully understand quantum mechanics, we could understand applied chemistry-->applied biology-->etc. I now understand that there are aspects of interactions and phenomena at the chemical and biological levels that are not defined by their basic physical properties.
2: Questions: What current work is being done by mathematicians to help us find new functions/relationships to apply to these nonlinear, complex relationships?
3: Presentation: See #2. If there are any, I would love to learn more about them.
4: Comments: Very helpful paper for my current state of understanding.
1) Very important to learn this!
Delete2) This is a very broad question - one would have to dig into the literature.
3) ** new functions/relationships to apply to these nonlinear, complex relationships ** Nemenman paper??
3) I looked through the blog posts just far too confirm, but I wasn't aware of an article on which Nemenman was the first author. Were you referring to a paper we have already discussed, or just some paper by the Soviet computer scientist?
DeleteI think new mathematical tools/perspectives might be key. There is no way all of the variables you could put into a model like this are independent--everything should affect everything else!
DeleteNatalie Hawken
ReplyDeleteAsgn 8A, Article 07: Dimensions of Systems Biology
0. KNEW
From our class discussions, I knew the basics of the computational limitations for systems biology (a Leibniz problem). Also, I was aware of the research field dedicated to Dimension 1, who focus on finding discrete molecular pathways. I knew of the temporal and spatial scale of human body modeling, specifically the large range of orders of magnitude encapsulating in body function.
1. LEARNED
I learned the classifications for systems biology into the five dimensions discussed in the paper (molecules, space, time, abstraction and emergence, and algorithms). This helped me to understand how we can break up the immense problem of systems biology into more manageable pieces. I got a much better understanding of abstraction and emergence, which makes this problem so much more interesting (and much more complex). I also found it very interesting how this quasi anti-reductionist approach is gaining popularity with biologists and how we are beginning to break away from traditional approaches.
2. MOST PRESSING QUESTIONS
Though Dimension 2 (structural complexity) (pg 86) is definitely a valid dimension to analyze, are there any effective protocols for studying this dimension using machinery available now or in the near future? Is the structural complexity a dimension that will have to fit into the picture after the rest of the dimensions have been analyzed? On pg 94, the author mentions how soluble molecular complexity then in turn affects macroscopic systems. ("How does solution chemistry create form and physicality of macroorganisms?") Will we ever reach answers to these questions, whether it be from experimental analysis or from computing? Are these questions going to be stuck as thought experiments?
3. PRESENTATION TOPIC
What is "computational irreducibility" (pg 97) and what are other examples of systems that are computationally irreducible?
What are cellular automata, graph theory, and game theory (pg 95) and how do they apply to systems biology abstraction?
4. THOUGHTS
I thought this paper was a good way to pack together all of the short conversations we've had in class about systems biology. It was helpful to see all of the dimensions specifically addressed instead of referring to them with generalizations. The extreme limitations with computing power, lack of reductionist knowledge, and algorithmic complexity do make me hesitant to think that systems biology will make major strides in understanding. I think we need much more sophisticated tools for measuring, actuating, and calculating before we can start to fully analyze these issues.
0-1) Good.
Delete2) The field of structural biology is a good start at the molecular scale. **Agent-based models (cellular automata**) are probably good for multi-scale questions. There may be better ones that I don't know of.
3)**Game Theory**, **Graph Theory**
4) Have to start somewhere, and biology has already accomplished a great deal!
0.Knew: I knew about biological networks before reading this paper, and I had an idea of how they were created. I also knew, regarding structural complexity, that there are many features, reactions and interactions at several different size scales in living systems. I knew that the cell division cycle is a common example of a biological temporal pattern, and I knew that mathematical modeling of a system allows one to predict behaviors and outcomes. I knew that the body has several feedback systems in molecular regulatory networks, electrochemical circuits, etc.
ReplyDelete1.Learned: One of the things I learned was why it is hard to precisely define systems biology: many justifications for systems biology reflect what different life scientists consider reductionism, and there are so many opinions about what to do moving forward with these different ideas of reductionism. I learned that what the paper calls device physics has been traditionally associated with biophysics, but that it is also associated with bioinformatics. It is also interesting that gene expression noise has been gaining popularity in systems biology. I learned that oscillations and behaviors that alternate are considered emergent because, according to the article’s definition of emergent, they are abstract properties that are not represented as a property of an individual material system subcomponent.
2.Pressing Questions: When did the omics revolution begin? Is it the date of the reference sited (2002)? P.82
With how many other organisms that we consider primitive do we share a number of genes in the same order of magnitude? Has testing been done on these organisms that can reveal something about the way the human body functions? P.84
How does one perform expression profiling on cells? P.91
3.Presentation: It would be interesting to learn more about the temporal pattern in the cellular response to DNA damage. I am on a DNA repair team in class, so I know I will be doing more research about how the cell works to repair its DNA, and I can include information about its temporal pattern. Maybe a good visual representation.
4.Thoughts: I really appreciate the table included in the paper, namely the column “Approach, tools or traditional disciplines” because sometimes I want to know how something is studied, and this provides a means to find out more about the specific methods involved. I appreciated the analogy comparing molecular complexity to software and structural complexity to hardware. I also liked that I could find examples of things we had discussed in class, like the reference of circadian rhythm in the abstraction and emergence dimension.
0. KNEW
ReplyDeleteI had a very basic idea of what systems biology is from previous papers we have read and the importance of bioinformatics in it, but not much else.
1. LEARNED
Systems biology can be characterized by the five dimensions: molecular complexity, structural complexity, temporal complexity, abstraction and emergence, and algorithmic complexity.
Molecular complexity would be defining the network map of all specific, regulatory interactions between molecules. Puzzling this out requires interdisciplinary work in bioinformatics.
Structural complexity exhibits system properties not obvious from the properties of its component parts and contains its own set of rules that determine new interaction modalities unseen at the smaller scale.
Temporal complexity can appear patterned such as in oscillations( via stochastic fluctuations) or irregular (via deterministic chaos), or in the stimulus-response characteristic such as hysteresis.
I have a better understanding of why organ-on-a-chip systems and simulations could never replace true in vivo models - the complexity of these 'digital/analog' computers could never be truly mimicked.
2. QUESTIONS
Do researchers who study structural complexity actually measure whole changes in protein phosphorylation state, pH distributions, temperature, currents, material properties, etc? It appears to be quite difficult to quantify and execute.
3. PRESENTATION TOPIC
Computational irreducibility (p. 98), stochastic versus deterministic equations (p. 95).
4. THOUGHTS
It was a revelation to me to learn that there is still significant heterogeneity in a population of genetically identical cells, due to random fluctuations of gene expression or persistent epigenetic individuality. This was an enlightening paper that helped explain many of the topics covered in class, and I'm more aware of the limitations currently faced in systems biology.
0 Knew
ReplyDeleteFrom our class discussion and my background I was generally familiar with the "dimensions" of molecular and structural complexity. The other dimensions of complexity were mostly new to me.
1 Learned
I really liked this definition of systems biology: "the analysis of entirety rather than the entireness of analysis" I also learned for the first time about the temporal, emergence, and algorithmic dimensions of systems biology.
2 Questions
Two what degree are the different dimensions of systems biology interact. Can we reach new understanding by considering only one or two dimensions or is a consideration of all dimensions necessary.
Where do hypothesis fit into systems-biology?
How do systems biologist define themselves when applying for funding?
3 Presentation topic
I'd like to know more about gene expression noise
4 Thoughts
Excellent read, excited for our class discussion. If we (I) am to approach systems biology, which dimensions should we (I) consider?
0. KNEW
ReplyDeleteAs far as molecular complexity, I was familiar with the fundamentals of genomics, proteomics, metabolomics, etc from previous classes. I also had knowledge of high throughput sequencing technologies and the formation of molecular databases such as KEGG with the purpose of identifying, categorizing, and characterizing all possible species in the functioning cell. This class has also made evident the path of reverse engineering a network map of intracellular interactions.
1. LEARNED
The partitioned approach of this paper made clear the five dimensional phase space involved in dissecting the multifaceted complexity of living organisms—molecular complexity, structural complexity, temporal complexity, abstraction and emergence, and algorithmic complexity. Genotypic complexity cannot necessarily be linked to phenotypic complexity, related to the increase of cell-cell and protein-protein interactions brought about by evolution. Important is the idea that the software of the cell (and organism) has been brought about by “ad hoc” inventions for a specific purpose, rather than a deliberate engineering for optimal efficiency.
Organism complexity is also dependent on its hardware. Fundamental to this dimension is a multi scale approach, and understanding that the interactions on one level of the vertical hierarchy does not necessarily translate to interactions on the next. Regarding temporal complexity, it is not complete to measure dynamic output following some stimulus, but it is also necessary to assert control of the system for the purpose of understanding the feedback mechanisms. I also learned the definition of emergence in relation to systems biology—“any abstract property of a system that is not obviously manifested as a property of an individual material subcomponent of the system.”
2. ?
Have other dimensions of phase space in systems biology arisen in the 10 years passed since this article was published?
3. PRESENTATION
In depth explanation of emergence and abstraction
4. THOUGHTS
I liked this article. It was broken down nicely, and many analogies cleared things up (ie the car running because of thermodynamic laws, etc)
0 Knew:I was aware of the incredible amounts of data created by biological assays and the need to decipher the data.
ReplyDelete1 Learned: A solid definition of systems biology as well as a dimensional approach to view systems biology which is a new perspective for me. This also allowed me to better understand the scope of systems biology.
2 Questions: Should there be a dimension for genetic space?
3 Presentation: How does graph and game theory apply to this paper?
4 Thoughts: What if any plans are there to reduce the completed protein network into a simple list of effects instead of a set of equations/concentrations.
Stephen Lee
ReplyDeleteClass 08, Assignment 8a
Dimensions of systems biology
(0) Knew:
I understood the basics of systems biology as a developing field. I’m also familiar with much of the content the paper uses to derive its main points.
(1) Learned:
My conception of systems biology was influenced heavily by this paper, albeit not defined clearly. Beyond my misconception that systems biology was simply modeling in vivo behavior in vitro using engineering/computational principles, I learned that the field encompasses five dimensions of complexity, each with specific challenges impeding the advancement of the field as a whole. With regard to the “abstraction and emergence” dimension described, I was introduced to the idea of reconciling different epistemological approaches taken by biologists and physicists (not something I had previously thought about). I was also intrigued by the notion that cellular “computing” incorporates both digital and analog information.
(2) Pressing Questions:
How is one to create a “computationally irreducible” system to model a both digital and analog information acting in simultaneity? Somewhat of an aside – how could one define molecular biology as “data but no theories”? Should we not be reforming the behavior of scientists in each isolated field before attempting to construct a unifying one?
(3) Presentation Topic: A presentation simplifying each of the dimensions described (as in the table on pg. 83) to better facilitate discussion.
(4) Thoughts:
This was a thought-provoking review. I always appreciate more abstract perspectives on science.
Stephen Lee
ReplyDeleteClass 08, Assignment 8a
Dimensions of systems biology
(0) Knew:
I understood the basics of systems biology as a developing field. I’m also familiar with much of the content the paper uses to derive its main points.
(1) Learned:
My conception of systems biology was influenced heavily by this paper, albeit not defined clearly. Beyond my misconception that systems biology was simply modeling in vivo behavior in vitro using engineering/computational principles, I learned that the field encompasses five dimensions of complexity, each with specific challenges impeding the advancement of the field as a whole. With regard to the “abstraction and emergence” dimension described, I was introduced to the idea of reconciling different epistemological approaches taken by biologists and physicists (not something I had previously thought about). I was also intrigued by the notion that cellular “computing” incorporates both digital and analog information.
(2) Pressing Questions:
How is one to create a “computationally irreducible” system to model a both digital and analog information acting in simultaneity? Somewhat of an aside – how could one define molecular biology as “data but no theories”? Should we not be reforming the behavior of scientists in each isolated field before attempting to construct a unifying one?
(3) Presentation Topic: A presentation simplifying each of the dimensions described (as in the table on pg. 83) to better facilitate discussion.
(4) Thoughts:
This was a thought-provoking review. I always appreciate more abstract perspectives on science.
0 Knew
ReplyDeleteChallenges in systems biology: uniting disparate disciplines, computational complexity, reductionism/abstraction, multistability
1 Learned
Five dimensions (i. molecular complexity; ii. structural complexity; iii. temporal complexity; iv. abstraction and emergence; v. algorithmic complexity); I've heard of "dissipative structures" before but the discussion on pattern formation helped put some of my thoughts into context
2 Questions
How far computational irreducibility will allow us to go? If biological computation exploits the physics of living system[s], (pg 96) may biological computation be exploited to overcome computational irreducibility?
3 Presentation
Systems biology from the perspective of a theoretical physicist/mathematician (in comparison to the RTA and cisplatin studies)
4 Thoughts
I enjoyed the more philosophical tone on pg 82/throughout. The paper read similarly to an STS paper (science, technology and society). It'd be interesting to review some aspect of the history/current state of systems biology using sociotechnical analysis/Actor-network theory.
0. KNEW:
ReplyDeleteBefore reading this paper, I had a basic idea of what systems biology was, but confined it in the way the beginning of the paper describes.I was aware of what reductionism is. I understood by the names, what the five dimensions were, but have never seen them before. I knew about “omics” sciences. I knew the gene similarities of other organisms and humans. I knew about how the body is structured and its different systems. I also knew how bioinformatics typically structures networks.
1. LEARNED:
I learned about the useful framework of giving five-dimensions to define systems biology. I learned the challenges of the five dimensions and where these have traditionally been in other scientific fields. I learned about functional genomics has been reductionism in nature. I learned about the importance of organization when describing biology. I learned that the second dimension, structural complexity is the first further from the traditional network-based approaches used in biology. I learned about the interconnectedness of systems of different scale. I learned how fractals and chaotic behavior can be conceptually linked. I learned how the dimensions interact and where systems biology was in 2006. I gained a great working definition of emergent properties.
2. PRESSING ?:
How has the field adapted to these needs during the last 10 years?
3. PRESENTATION:
I would compile all the example of the dimensions and map them onto a graphic to get a full picture of what it means to take into account all the dimensions.
4. THOUGHTS:
I very much enjoyed this paper as it gave a great outlook on the field of systems biology and really spoke to all the factors involved. It expanded my perception of the area and put the RTA efforts into a larger picture. However, I wish this was updated since I feel like it might be somewhat older knowledge, at least in regards to needs and examples.
Class 8. Assignment 8A: Dimensions of systems biology.
ReplyDelete0. KNEW:
I think I understood or was somewhat familiar with most of the individual ideas in the article--but I had never thought to organize my mental picture of systems biology in this way. Given that our naive conception of it seems to be throwing everything but the sink at solving biological problems, this is a helpful framework to think about.
1. LEARNED:
I learned about a reasonable five-dimensional classification of systems biology, which distinguishes several different ways that complexity can be introduced into biological problems. There is biochemical complexity from having a lot of interacting molecules (molecular complexity), complexity from having different complicated structures at each length scale (structural complexity), complexity from needing to deal with processes that span some 10^18 (or more) temporal orders of magnitude (temporal complexity), complexity from emergent behavior in the sense of statistical mechanics (abstraction and emergence), and complexity from the challenges of understanding how biological computers store and process information.
2. PRESSING ?:
Now that we've somewhat cleanly (though there is some overlap, as discussed in the paper) delineated different sources of complexity, we must ask: must all of these be incorporated? For example, is it possible or even a good idea to incorporate many of these dimensions into some set of nonlinear, coupled differential equations? Is there a better way to analyze biological systems?
3. PRESENTATION:
A good presentation here would briefly describe each dimension of complexity discussed here, and give a bunch of examples. It would also be nice (if this could be found) to show an example of a model incorporating more than one dimension effectively.
4. THOUGHTS:
We are reading a lot of reviews. But I guess they're good to help get our footing. Also, I need to write these posts a lot earlier. I have a lot to say but not enough time to say it.