Jul 20, 2009
- Jul 24, 2009
e-Science Centre, 15 South College Street, Edinburgh
Organisers
| Name |
Institution |
| Davies, Jamie |
University of Edinburgh |
| Grinfeld, Michael |
University of Strathclyde |
| Webb, Steven |
University of Strathclyde |
We are witnessing an explosion in the amount of available experimental data obtained by high throughput techniques in molecular biology. Arguably, methods for transforming this data into knowledge, which would allow understanding, and could guide principled drug research and therapy protocol development, have not kept pace with technological developments. Furthermore, the sheer complexity of biological systems requires new reduction and knowledge representation tools.
It is our belief that mathematics has much to contribute to biology, but that in order to do that, modellers have to free themselves from the assumptions that underlie the very successful application of mathematical methods in physics. The development of new modelling methodologies has to be informed by input from biologists with a broad view of their discipline, and from philosophers of science with expertise in modelling. The principal aim of the workshop is to promote exchange of ideas among researchers interested in well-founded systemic modelling of biological systems.
Specific objectives include:
- discussion of recent modelling techniques, such as process algebra, reverse-engineering of genetic networks, Gröbner bases and control-theoretic methods
- clarification of fundamental philosophical difficulties of modelling biological systems and approaches to overcoming these difficulties
- identifying new areas in ageing, cancer, developmental biology, and evolution, that could benefit from sound modelling
Sessions in the workshop will be centred around some of the following questions:
- How does one ensure robustness of models against advances in biological knowledge?
- How important are stochastic effects in understanding biological systems?
- Are there principles of biological organisation (for example, in signal transduction systems) that can be discerned as the size of the subsystem being considered increases?
- Is there a principled way of lumping variables in biological models, a "statistical mechanics" of biological systems?
- Is there any way to model biological systems holistically without appealing to teleological notions or to notions of optimality? Is the property of robustness a good candidate for an organising principle in biological systems?
- In what sense is it true to say that biological systems have a (distributed) representation of their state, and what explanatory purchase is in such a notion?
- What is a fruitful control-theoretic way of looking at a distributed biological system? How flat, in terms of control, are biological systems?
- Can notions of emergence, downward causality, information processing, or computation be made to yield any predictive insights?
- Are the classical notions of causality sufficient to describe historically (both ontogenetically and phylogenetically) constituted systems?
- Is a clearer understanding of evolutionary development of a particular system (such as the immune system) helpful for modelling and predicting its contemporary function? For example, is considering senescence and neoplasia as necessary consequences of multicellularity and longevity useful for the understanding of these processes?
- Why is it that defining a living system is not an important goal of philosophy of biology, let alone biology itself?
Funded by
EPSRC
LMS
CSBE
Bridging the Gap, University of Strathclyde
Arrangements
The workshop will commence on the morning of Monday 20 July and close late afternoon on Friday 24 July.
Participation
The workshop is now full and applications have closed.
Please note that a 30.00 GBP registration fee will be payable for this workshop. Further details about payment will be sent to you by email.
Venue
The workshop will be held at 15 South College Street, Edinburgh. All lectures will be held in the Newhaven Lecture Theatre. To view this room and a list of the visual equipment available click here. In addition, two blackboards have recently been installed. Follow this link for a map showing the location of 15 South College Street, or this map may also prove useful.
Wireless Access The workshop venue, 15 South College Street, has wireless access throughout. On arrival at Registration you will be given instructions and a code for accessing the wireless network.
Travel
Information about travel to the UK and Edinburgh is available here. Participants (other than invited Speakers) are asked to fund their own travel to attend the workshop. You may find this map useful.
A taxi directly from the airport will cost approximately 15.00 to 20.00 GBP to the city centre for a one-way journey.
For Speakers residing at Pollock Halls of Residence, a taxi will cost 18.00-22.00 GBP direct from the Airport to Pollock Halls. Alternatively, the Airport Bus into the city centre costs 6.00 GBP return. If you alight the bus where it terminates on Waverley Bridge, a black taxi cab will take you to Pollock Halls for approximately £6.00. From the city centre at South Bridge, it is also possible to get a No. 30 or No. 48 Lothian bus. Ask the driver to let you know when you are at the Commonwealth Swimming Pool on Dalkeith Road - Pollock Halls is the complex of buildings immediately behind the Swimming Pool.
Lothian buses charge £1.20 for a single, £3.00 for a day ticket. Please note that the exact fare is required and no change is given.
If travelling by train, please note that Edinburgh has two railway stations - Waverley Railway Station being the main station and closest to the workshop venue at 15 South College Street (also, for Speakers, the closest to Pollock Halls of Residence). If you alight the train at Edinburgh Waverley, the workshop venue is an easy 10 minute walk over North and South Bridge map. The second railway station is called Haymarket and is at the West End of the city centre.
UK Visas
If you are travelling from overseas you may require an entry visa. A European visa does not guarantee entry to the UK. Please use this link to the UK Visas site to find out if you need a visa and if so how to apply for one.
Accommodation
Participants should make their own accommodation arrangements. A list of Edinburgh accommodation of various sorts and prices is available here . Sections 4 is particularly relevant.
Invited Speakers will have received an email giving full details of accommodation arrangements made on their behalf by ICMS. Please email Audrey Brown at ICMS if you were anticipating but have not received this information.
Registration
Registration will take place 09.00 - 09.50 on Monday 20 July at 15 South College Street, Edinburgh.
Talks and Posters
The Programme which is posted below. A list of visual equipment is available here. Two new blackboards have also recently been installed in the Newhaven Lecture Theatre.
We will not have an organised Poster Session, but boards will be available in the Chapterhouse Coffee/Lunch Room and posters can be displayed throughout the week and discussed during the coffee and lunch breaks. Posters should be A1 portrait size. If you wish to present an poster and it will be larger than A1, please let me know the dimensions in advance of the workshop.
Public Lecture - PLEASE NOTE THAT THIS PUBLIC LECTURE IS NOW FULL
A public lecture will be held on Tuesday 21 July at 18.00 in the Newhaven Lecture Theatre at 15 South College Street, Edinburgh. Marc Van Regenmortel from University of Strasbourg will speak on Two Darwinian enigmas: the nature of species and the nature of life. Please follow this link to view the abstract for this lecture as a pdf file.
Coach Trip to Rosslyn Chapel
A quick sandwich lunch will be provided in the Chapterhouse Coffee Room at 12.30 on Wednesday 22 July. At 13.15 we will assemble for a 3-minute walk to Crighton Street to board our coach, which will leave promptly at 13.30, for a 30 minute coach journey to Rosslyn Chapel. This extraordinary building dates back to 1446. The architecture, stone carvings and history draw visitors from all over the globe. A guided tour of the Chapel is arranged for 14.00 and will last approximately 30-40 minutes. There will then be time to wander round the Chapel grounds or visit the Exhibition Room and shop before boarding the coach for a return to Edinburgh at 15.30 or 15.45. Please note that, on our return, there will be no access to the building at 15 South College Street.
Knowledge Transfer Event
Following the coffee break on the afternoon of Thursday 23 July there will be a Knowledge Transfer (KT) event followed by a wine reception and dinner. The principal activities of the KT event are to discuss the extent to which mathematical models can provide insight into biological complexity and to explore current industrial problems and how mathematical expertise could assist in their resolution.
By bringing together industrial, biologists and mathematicians the KT event aims to:
• inform life sciences practitioners of the latest mathematical research in systems biology
• identify current problems and future ambitions from industry
• formulate viable modelling projects
• stimulate joint research activity and establish ongoing industry/academic collaboration.
Expected participants include:
Dauly, Claire (Lein Diagnostics)
Freeman, Tom (Fios Genomics and The Roslin Institute)
Ghazal, Peter (Division of Pathway Medicine)
March, John (BigDNA Ltd)
Ononokpono, Okon (KL Pharmaceuticals Ltd)
Plotkin, Gordon (CSBE)
Sinfield, James (genecom)
Catering
Morning and afternoon tea/coffee/biscuits will be provided on each day of the workshop.
On Monday 20 July and Wednesday 22 July, a light buffet lunch will be provided free of charge to participants in the Chapterhouse, 15 South College Street, Edinburgh. Participants are free to explore the many cafés, sandwich shops, restaurants and bars nearby for lunch on the other days of the workshop.
On Thursday 23 July at around 17.30, an informal wine reception, to which all all participants are invited, will take place in the Chapterhouse Coffee Room. At 19.00 there will be a short walk to Blonde Restaurant, 75 St Leonards Street, Edinburgh, EH8 9QR, for the Workshop Dinner. The workshop grant will cover the cost of the workshop dinner and the pre-dinner drinks.
Financial Arrangements
The majority of participants will pay for their own accommodation and travel. Catering will be provided as listed above.
Invited Speakers should refer to the individual invitation and subsequent email correspondence with Audrey Brown at ICMS regarding the financial arrangements.
Unless otherwise stated in your invitation or further correspondence, there will be a registration fee of 30.00 GBP for the workshop. We ask that this is paid in advance by using this credit or debit card payment form. The form should be printed out, completed and faxed back (as email is not a secure way of sending credit card information). The fax number is on the form. If it is not possible for you pay in advance, you may print out the credit/debit card form above and bring the completed form along to Registration. We prefer not to handle cash at Registration. Alternatively, we can accept sterling cheques. The cheque should be made payable to Heriot-Watt University and can be handed over at Registration.
Programme
Monday 20 July
09.00 - 09.50 | Registration and coffee |
09.50 - 10.00 | Welcome and introduction |
10.00 - 11.00 | Arthur D Lander (University of California, Irvine)
Biology as strategy: exploring the control of growth and form PDF of Presentation
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11.00 - 11.30 | Tea/Coffee |
11.30 - 12.30 | William Bechtel (University of California at San Diego)
Thinking dynamically about biological mechanisms PDF of Presentation
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12.30 - 14.00 | Lunch provided in the Chapterhouse, 15 South College Street, Edinburgh
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14.00 - 15.00 | Tom Kirkwood (Newcastle University)
Mathematics and the science of ageing PDF of Presentation
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15.00 - 16.00 | Michael Grinfeld (University of Strathclyde)
The difficulties of mathematising biology PDF of Presentation
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16.00 - 16.30 | Tea/Coffee |
16.30 - 17.30 | Vincent Danos (University of Edinburgh)
Internal coarse-graining of rule based models of signalling networks PDF of Presentation
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Tuesday 21 July
10.00 - 11.00 | James R Faeder (University of Pittsburgh)
Rule-based modelling of biological signalling: a progress report PDF of Presentation
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11.00 - 11.30 | Tea/Coffee |
11.30 - 12.30 | Ana M Soto (Tufts University & University of Ulster at Coleraine)
Complex causality; the integration of physical parameters into biological causality in development and cancer PDF of Presentation
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12.30 - 14.00 | Lunch break |
14.00 - 15.00 | Reinhard Laubenbacher (Virginia Tech)
Parameter estimation for Boolean models PDF of Presentation
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15.00 - 16.00 | Karthik Raman (University of Zurich)
Systems-level modelling of pathogenic organisms for drug target identification PDF of Presentation
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16.00 - 16.30 | Tea/Coffee |
16.30 - 17.30 | Discussion |
18.00 | Public Lecture by Marc van Regenmortel in Newhaven Room, 15 South College Street
Two Darwinian enigmas: the nature of species and the nature of life PDF of Abstract
PDF of Presentation |
Wednesday 22 July
10.00 - 11.00 | Jamie Davies (University of Edinburgh)
Modelling morphogenesis in silico and in wetware PDF of Presentation |
11.00 - 11.30 | Tea/Coffee |
11.30 - 12.30 | Chris Myers (Cornell University)
The geometry of robustness in biological networks and cellular information processing PDF of Presentation
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12.30 - 13.20
| Lunch provided in the Chapterhouse, 15 South College Street, Edinburgh
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13.30 - 16.00
| Coach trip to Rosslyn Chapel, Midlothian. Bus will depart for Rosslyn at 13.30 prompt and return around 15.30 or 15.45. We aim to be back to the University about 16.00 to 16.15. |
Thursday 23 July
10.00 - 11.00 | Peter Schuster (University of Vienna)
Mathematical modelling of evolution – solved and open problems PDF of Presentation |
11.00 - 11.30 | Tea/Coffee |
11.30 - 12.30 | Baruch Rinkevich (Israel Oceanographic Research Institute)
Coral colony astogeny- modular organisms where biology and mathematics meet |
12.30 - 13.30 | Lunch break |
13.30 - 14.30 | Olaf Wolkenhauer (University of Rostock)
The (re)construction of realities in systems biology PDF of Presentation
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14.30 - 15.30 | Jeremy Gunawardena (Harvard Medical School)
Seeing the wood for the trees: mathematical approaches to biological complexity PDF of Presentation |
15.30 - 16.00 | Tea/Coffee |
16.00 - 16.45 | Tom Freeman (Fios Genomics and The Roslin Institute)
Network visualisation and analysis of complex biological data PDF of abstract
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16.45 - 17.30 | Claire Dauly (Lein Diagnostics)
Processing techniques for confocal eye measurements PDF of abstract |
17.30 - 18.00 | Gordon Plotkin (CSBE)
A calculus and a language for (some of) systems biology |
18.00 - 19.00 | Wine reception in the Chapterhouse, 15 South College Street, Edinburgh
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19.15 | Workshop Dinner at Blonde Restaurant, 75 St Leonard's Street, Edinburgh |
Friday 24 July
10.00 - 11.00 | Hugh MacMillan (Clemson University)
Modelling of genetic and environmental factors in the generation of neuronal variability during cerebral cortical development PDF of Presentation
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11.00 - 11.30 | Tea/Coffee |
11.30 - 12.30 | Marc Van Regenmortel (University of Strasbourg)
Context dependence and relational nature of immunological data collected for mathematical modelling PDF of Presentation
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12.30 - 14.00 | Lunch break
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14.00 - 15.00 | William C Wimsatt (University of Chicago)
Nature at the edge: simplifying a complex system by natural means, or one way to reason with messy systems and get away with it? PDF of Presentation
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15.00 - 16.00 | Discussion |
16.00 - 16.30 | Tea/Coffee |
16.30 - 17.30 | Close of workshop |
Presentations:
| Presentation Details |
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| Bechtel, William |
| Thinking dynamically about biological mechanisms |
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The new mechanistic philosophy of science has emphasized the same explanatory endeavor that most biologists themselves emphasize: the decomposition of mechanisms into component parts and operations. In both fields there has been much less attention to the converse endeavor: the recomposition of those components into a mechanism organized so as to produce the phenomenon targeted for explanation. Philosophical accounts of mechanistic explanation typically acknowledge that mechanisms are organized, but give little attention to the impressive range of tools for understanding their spatial and temporal organization or to the proposals that emerge from their use. Our focus here is on temporal organization. The tendency, among both philosophers and biologists, is to think of operations as occurring sequentially so that scientists can trace operations “from start up to termination conditions” (Machamer, Darden, & Craver). But real biological mechanisms exhibit complex orchestration of operations in real time, often involving one or more feedback processes and non-linear interactions among operations. Computational biologists have made it their business to provide accounts of the complex dynamics of living systems. A typical model in computational biology is a system of differential equations whose variables correspond to selected properties of the parts and operations of the target mechanism. The tools of dynamical systems theory can elucidate such models, and often are explicitly called upon.
We refer to the explanations resulting from integrating mechanistic research and dynamical modeling as dynamic mechanistic explanations.
We illustrate such explanations using recent endeavors to employ computational models to understand the mechanisms responsible for circadian rhythms. Moreover, we argue that a crucial next step in developing the new mechanistic philosophy of science is a widening of its scope to encompass this important type of explanatory project. (Joint work with Adele Abrahamsen)
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| Danos, Vincent |
| Internal coarse-graining of rule based models of signalling networks |
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Rule-based modelling is an approach to modelling biological networks of high combinatorial complexity. It allows to capture uniformities in the description of such networks by using rules that only specify partial contexts under which a given binding or modification event can happen. An interesting by-product is an exact model reduction technique which I will present. It gives good results on models we have been working with.
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| Davies, Jamie |
| Modelling morphogenesis in silico and in wetware |
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Morphogenesis - the process by which organisms gain their external forms and internal anatomies - is attracting increasing research interest, both for reasons of basic understanding and for technical applications such as tissue engineering. Morphogenetic events generally seem to be complicated, involving mechanisms at a variety of organizational levels (molecules, supra-molecular assemblies, cells, tissues) and time-scales, and they are further obscured by being accompanied, usually, by cells differentiating to perform specialized physiological functions. Modelling, and the simplification that it entails, is therefore a valuable tool in the search for morphogenetic understanding. This presentation will address three type of modelling: "conventional" mathematical modelling of (key features of) the in vivo system; creation of simple culture models using cells from the in vivo system, and a dialogue between wet-ware and mathematical experiments on them, and creation, using insights from modelling, of a synthetic biological system in that will undergo designed, rather than evolved, processes of morphogenesis. Examples of each system, and their possible applications, will be discussed.
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| Faeder, James R |
| Rule-based modelling of biological signalling: a progress report |
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Signalling in cells generally involves protein-protein interactions, which can produce myriad protein complexes. Such protein-protein interactions can be represented compactly and precisely using graphical reaction rules, which can be processed automatically to obtain a chemical reaction network. However, reaction networks implied by typical sets of rules are often too large for conventional simulation procedures to handle. To address this challenge, we have developed a kinetic Monte Carlo method that can take advantage of a rule-based model specification. Rules are used directly to advance a simulation, thus avoiding the computationally expensive step of generating the underlying chemical reaction network implied by the rules. Unlike previously proposed methods that adaptively generate species and reactions in response to network activity, the method is not overwhelmed when the likelihood of encountering new species each time a reaction fires becomes high. The method is illustrated by using it to characterize the interaction of a trivalent ligand with a bivalent cell-surface receptor. The results of the simulations suggest formation of extremely large receptor aggregates under typical experimental conditions.
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| Grinfeld, Michael |
| The difficulties of mathematising biology |
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By comparing the situation in biology with that in physics, I will show that it is virtually impossible to have "good" mathematical models in biology. I will also show in two simple examples that in its capacity as "logic inspector", mathematics can play an invaluable role in biological research.
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| Gunawardena, Jeremy |
| Seeing the wood for the trees: mathematical approaches to biological complexity |
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The molecular networks that implement cellular processes contain many components, which provides one axis of biological complexity. However, the protein components are themselves usually posttranslationally modified (phosphorylated, methylated, ubiquitinated, etc) on multiple amino acid residues (sites), implying a further exponential increase in the accessible state space. Such complexity presents a formidable challenge to both experiment and theory. We outline recent mathematical work which hints at a way around this. We use ideas from algebraic geometry to show that certain posttranslational modification networks, when considered at steady state, have an effective complexity that scales linearly with the number of components and not exponentially with the number of sites. This exponential reduction in complexity permits analysis of previously intractable problems and leads to novel biological insights. We suggest that mathematical approaches of this kind will be essential if we hope to distill biological principles from molecular complexity.
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| Kirkwood, Tom |
| Mathematics and the science of ageing |
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Understanding ageing is a problem that engages with mathematics at many levels. Application of mathematics to evolutionary biology helps make clear why the ageing process has evolved, basically because older organisms would count less in terms of Darwinian ‘fitness’ even if they did not deteriorate. This had led to limited investment in the long-term durability of the body – a concept known as the “disposable soma” theory. Mathematical modelling of life histories and of how resources are used to optimal effect to enhance fitness is helping to study the role of calorie restriction in extending the life spans of mice and other short-lived animals. Similar modelling is helping to understand why menopause might have evolved. Mathematics is also contributing to investigating the complex biology of how ageing occurs. Inside the cells and organs of our bodies, a diverse array of interacting processes is at work giving rise on the one hand to the gradual, lifelong accumulation of molecular and cellular damage that causes senescence, while on the other hand sophisticated networks of repair and maintenance systems work to slow or reverse some of this build-up of faults. Systems biology, with significant inputs from mathematicians, is beginning to unravel some of this complexity.
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| Lander, Arthur D |
| Biology as strategy: exploring the control of growth and form |
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Biology differs from all other natural sciences in having a strategic dimension: systems and states of affairs exist in the living world because they embody strategies for carrying out tasks—tasks that contribute, ultimately, to fitness. To truly understand biology we are obliged to find not only the mechanistic explanations for how things happen, but also the strategic explanations for why things are there (i.e. what it is about particular states of affairs that enables them to have been selected for). Historically neglected by Molecular Biologists, the exploration of biology’s strategic side is seeing a revival among Systems Biologists, who seek meaningful explanations for biological complexity, and fail to find them in the enumeration and categorization of mechanisms. Focusing in the area of biological development, I will discuss cellular and molecular mechanisms underlying morphogenesis and tissue growth control, and argue that meaningful understanding of these mechanisms is best obtained through model-driven exploration of their contributions to tasks such as increasing speed, improving robustness, and suppressing noise. I will also discuss how the inherent tendency of strategies to interfere with each other forces biology toward complex solutions to real world problems (a phenomenon that has also been recognized in engineering), and will propose that elucidating rules of strategic interference constitutes an important first step toward a global understanding of biological organization.
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| Laubenbacher, Reinhard |
| Parameter estimation for Boolean models |
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During the last decade finite dynamical systems, that is, discrete dynamical systems with a finite phase space, have been used increasingly in systems biology to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. In part, this is motivated by the fact that in many cases the available data quantity and quality is not sufficient to build detailed quantitative models such as systems of ordinary differential equations, which require many parameters that are frequently unknown. In addition, discrete models tend to be more intuitive and more easily accessible to life scientists. Boolean networks are the main type of finite dynamical systems that have been used successfully in modeling biological networks. This talk will describe algorithms and software to provide to the modeler capabilities in the Boolean network paradigm similar to those used in the construction of continuous models, primarily parameter estimation and simulation tools.
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| MacMillan, Hugh |
| Modelling of genetic and environmental factors in the generation of neuronal variability during cerebral cortical development |
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We present a computational framework for testing hypotheses about developmental links between the cellular response to endogenous DNA damage, the cell fate decision-making that defines the population kinetics of neuron production, and the genome rearrangements within neural progenitor cells, such as that due to widespread transposable element activity. Such events contribute to neuronal genetic heterogeneity, or mosaicism, in the mammalian brain. Modeling begins with simplified single-cell models of DNA damage response over the course of a progenitor cell division cycle. Then, introducing physiological noise, we simulate an ensemble of these simplified models and assign daughter cell fates according to a chosen protocol. Each set of model assumptions defines a stochastic branching process, and we have begun to evaluate the plausibility of different sets of model assumptions within a common computational framework.
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| Myers, Chris |
| The geometry of robustness in biological networks and cellular information processing |
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Robustness of biological systems arises in part through active control, but also through more passive means, such as the existence of extended neutral spaces that support degenerate genotype-to-phenotype maps across levels of biological organization. These neutral spaces can have nontrivial geometries that impact the nature of system robustness. At the scale of chemical kinetic networks, "sloppiness" in parameter sensitivities supports robustness of dynamical response to correlated parameter variation, and may provide support for the exploration of novel phenotypes. At the scale of molecular interactions that give rise to such networks, elaborate niches in high-dimensional sequence spaces arise in problems of molecular discrimination, revealing connections to fundamental problems in coding and computational complexity theory.
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| Raman, Karthik |
| Systems-level modelling of pathogenic organisms for drug target identification |
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Systems–level modelling of pathogenic organisms has the potential to significantly enhance drug discovery programmes. Using Mycobacterium tuberculosis as an illustrative example of an important pathogen, we discuss how systems–level models can positively impact drug target identification. Constraint-based stoichiometric models of metabolism, studied using flux balance analysis are useful to identify critical points in bacterial metabolism, for targeting drugs. We then discuss the analysis of protein–protein functional linkage networks to identify influential hubs, which can be targeted to disrupt bacterial metabolism. Another important aspect, especially in tuberculosis, is the emergence of resistance. A network analysis of potential information pathways in the cell helps to identify important proteins as co-targets, targeting which could counter the emergence of resistance. We integrate the analysis of pathogenic metabolism, protein–protein interactions and protein structures, to develop a generic drug target identification pipeline, for identifying most suitable targets. We finally discuss the modelling of the interplay between the pathogen and the human immune system, using Boolean networks, to elucidate critical factors influencing the outcome of infection. Challenges that exist in the modelling of pathogenic organisms will also be discussed. The strategies discussed can be applied to understand various pathogens and can impact many drug discovery programmes.
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| Rinkevich, Baruch |
| Coral colony astogeny- modular organisms where biology and mathematics meet |
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In multicellular organisms, the level of integration among bodily components dictates the final functional performance of the entire organism. This is highlighted in a number of sessile modular organisms like trees and a range of marine invertebrate taxa sharing similar morphometric traits, which produce morphological complexities. The whole organism architecture is achieved by amalgamating properties at more than a single level of construction, depicting fixed and flexible morphometric rules, phenotypic plasticity and growth patterns that directly affect life-history traits and fitness. A further challenging topic is the study of organisms’ architectures, made of multiple genetically identical modules, at several levels of organization, which are physiologically and structurally integrated. Of primary importance are branching structures (in plants and animals alike) that elucidate rules and inherent genetic control for bodily architectures. However, whereas the scientific literature often deals with differences in morphologies in branching types, very little attention is given to astogeny rules, including the impacts of positional value. This is the place where biology and mathematics meet. This review will depict several cases and examples elucidating the rules that govern colony astogeny and development in branching coral forms. Results also show how phenotypic plasticity enables branching corals to adjust morphologies and various life history traits to variable environmental challenges.
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| Schuster, Peter |
| Mathematical modelling of evolution - solved and open problems |
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ODE models of evolutionary processes at the molecular level are testable by suitable experiments but suffer from high dimensionality. Reduction to tractable numbers of variables is possible for certain conditions. Stochastic phenomena and spatial pattern formation are highly important but hard to handle in general systems.
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| Soto, Ana M |
| Complex causality; the integration of physical parameters into biological causality in development and cancer. |
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Multicellular organisms and their cells are ontogenetically linked. A zygote divides, producing more cells, which are organized in a tri-dimensional pattern. Both association patterns and cell types change as tissues and organs are formed. The context of multicellular organisms is a product of history (evolution and ontogeny). Hence, we propose to consider multicellular organisms as complex systems in which the relations among their parts are contextual and interdependent. We argue that this context-dependence is an effect of diachronic emergence. This reciprocity makes it difficult to establish detailed cause and effect relationships. Thus, hierarchical levels are entangled, precluding at times experimentally isolated cells from revealing their full role in situ in the originating organism. One of the reasons for this outcome is the generation of mechanical forces in the tissue. These mechanical forces are due to a) the adhesion between cells, b) the adhesion between cells and the extracellular matrix that surrounds them, and c) the global properties of the tissue itself (ie, rigidity, elasticity, viscosity). These mechanical forces shape the tissue and even determine cellular fate. We will discuss the how the integration of these physical parameters are contributing to the understanding of organogenesis and carcinogenesis and the use of mathematical modeling and computer simulation to the analysis of normal and neoplastic development.
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| Van Regenmortel, Marc |
| Context dependence and relational nature of immunological data collected for mathematical modelling |
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The capacity of the immune system to discriminate between millions of discrete molecular structures present in antigens is one of the most effective differentiation mechanisms that exists in biology. We will discuss here only the recognition potential of antibody molecules. The regions of antigen molecules recognized by antibodies are called epitopes while the regions of antibodies that recognize epitopes are called paratopes. Both regions are identified by analyzing the 3D structure of antibody-antigen complexes and they correspond to the amino acid residues that make direct contact with each other in the complex.
In proteins, most epitopes consist of several short stretches of residues distant in the protein sequence that are brought together by the folding of the polypeptide chain. The atomic groups forming such a discontinuous epitope are not held together by internal chemical bonds and the epitope arises because the chain acts as a scaffold. If the scaffold is perturbed, for instance, by a change in chain conformation, the epitope ceases to exist (Van Regenmortel 2009). Discontinuous epitopes cannot be isolated as such from the protein antigen and they do not possess binding activity outside the context of the 3D protein structure in which they are embedded. They also cannot be predicted from nucleotide or amino acid sequences.
A second type of protein epitope known as a continuous epitope is defined as any short peptide fragment of a protein that is able to bind to antibodies raised against the protein. Such peptides are not faithful copies of epitopes present at the surface of native proteins but are useful because they can replace the full protein in diagnostic immunoassays that use antibodies obtained from hosts infected with a pathogen.
The prediction of epitopes in proteins is important for various immunological applications but presents numerous challenges to the bioinformatics community (Vita et al 2006; Greenbaum et al 2007). Many immunological databases exist that list thousands of continuous epitopes but their usefulness is limited because of the context dependence of epitope binding activity (3D structure, assay conditions, immunogenic activity depends on the biological context of the host and cannot be extrapolated from antigenic activity) (Van Regenmortel 2006). So far, none of the machine learning algorithms and other prediction tools showed a high degree of accuracy in epitope prediction (Greenbaum et al 2007).
One of the major aims of epitope prediction is to predict which linear peptides will be able to elicit antibodies that recognize an intact protein or a vaccine target, thereby providing protection against infection. However, the structural context of an epitope bound to a free antibody in serum is not the same as that of an epitope when it recognizes a B cell epitope during the immunization process (Van Regenmortel 2009). Another difficulty lies in the fact that epitopes and paratopes are relational entities that do not exist as such in the antigen or antibody, i.e. outside the relational nexus that allows the binding sites to be recognized in a functional assay. This relational dependence means that analyzing the antigenicity of a protein amounts to analyzing the size of the immunological repertoire of the host immunized with that protein. The failure, after decades of intensive research efforts, to develop a single peptide vaccine, approved for human use, demonstrates the limitations of currently available immunological datasets.
A further complication is that biological phenomena cannot be explained by involving a single overriding cause, as is often done in physics and chemistry. Only contributory causes exist in the sense that a multiplicity of conditions are necessary but not sufficient for a biological phenomenon to occur. Analysis in terms of a single causal factor is inadequate since this requires introducing an unrealistic ceteris paribus (other things being equal) clause for hundreds of undefined but essential contributory conditions (Van Regenmortel 2007). This makes it impossible to design vaccines by design.
Biological events are always context-dependent and since they find their origin in complex networks of interactions, the underlying causal links are obscured by negative feedback, feed-forward control, interference, cooperatively, synergy, downward causation etc. Computer simulations are needed to predict how a biological system is likely to behave in terms of probabilistic propensities. If subsequent experimental observations agree with the prediction, some measure of successful intervention or control becomes possible even in the absence of genuine human understanding.
References :
Greenbaum et al (2007) J Molecular Recognition 20, 75-82.
Van Regenmortel MHV (2006) J Molecular Recognition 19, 1-5.
Van Regenmortel MHV (2009) The Open Vaccine Journal 2, 33-44.
Van Regenmortel MHV (2007) Proteomics 7, 965-975.
Vita et al (2006) BMC Bioinformatics 7, 341.
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| Wimsatt, William C |
| Nature at the edge: simplifying a complex system by natural means*, or one way to reason with messy systems and get away with it? |
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How and when can a simple model (or family of models) reveal deep truths?
a. when you are lucky?
b. when it is based on robust results.
c. when it is one of a family of models of increasing but still PAINFULLY limited complexity and realism?
d. when it is studied under conditions that are most revealing (like near a boundary where nature’s dynamics are vastly simplified)?
e. (Here is much of the luck:) when nature is confined to stay near that boundary much or most of the time?
f. when constraints from diverse disciplines can be utilized simultaneously?
g. when there is a large body of not necessarily formal theory that can be organized by it to run a long way quickly?
h. when there is at least some rich data for comparative reach to diverse cases?
i. and at least some crisp data - for accurate anchoring (e.g., of key parameters)?
j. when there are multiple representations to help to capture the richness that is not exhausted by any one of them?
k. all of the above?
This talk describes results of Monte Carlo Simulations with complex large (up to 260 loci) multi-locus population genetic models (technically, mutation-selection balance models with truncation selection, and alleles in different fitness classes) that are however far too simple to justify the conclusions that I believe robustly follow from them. How is this possible? I believe they satisfy all of the conditions on the above list to a significant degree (and will illustrate how). Broader empirical applications should enrich the support for several of them. I do not yet entirely understand why this case seems so powerful (and perhaps I have missed the need for crucial unsatisfied premises), but I will try to provide enough grounds for discussion. My analysis of these simulations and the phenomena they model draws on important methodological or theoretical work by Richard Levins, Herbert Simon, Richard Lewontin, James Crow, Stuart Kauffman and others (e.g., Peter Schuster) - though, as you will see, sometimes with qualifications or more rarely, disagreements. A .pdf of the slides should be available by July 19.
*The simulations and most of their interpretation was done with Jeffrey C. Schank of the Dept. of Psychology at UCDavis (jcschank@ucdavis.edu), and jumps off from our joint paper (Wimsatt and Schank 2004) initiated in our original (Schank and Wimsatt 1986). Though he has not yet seen the interpretive extensions argued here, I expect that Jeff would be in full agreement with them.
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| Wolkenhauer, Olaf |
| The (re)construction of realities in systems biology |
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The most commonly used route to a mathematical model in molecular and cell biology begins with an `interaction graph' - a diagram that lists the molecular components involved in a system and hypothesized interactions between them. A central task in systems biology is then to interpret such diagrams with the help of mathematical models. I shall first demonstrate the translation of cell-biological systems into interaction graphs, and then discuss a principal limitation of interpreting these diagrams with ordinary differential equations.
In this context, I shall define systems biology as "the art of making appropriate assumptions". My arguments support the view that the overwhelming complexity of cell-biological systems renders every attempt for comprehensive mathematical models futile. This does however not imply that we cannot improve our understanding of natural systems through mathematical modelling. Modelling in systems biology is a creative process by which different entailment structures are brought into congruence. The model is formulated to correspond in some useful way to observations made in experiments. However, due to the complexity of biological/living systems precision and certainty are impossible to attain. In order to succeed with this phenomenological outlook, it is important to discuss and investigate the modelling process itself.
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Participants
| Name |
Institution |
| Abrahamsen, Adele |
University of California, San Diego |
| Al-husari, Maymona |
University of Strathclyde |
| Al-Nuaimi, Yusur |
University of Manchester |
| Aleem, Hosam |
University of Manchester |
| Augusto, Sofia |
University of Lisbon & Centre for Environmental Biology |
| Bechtel, William |
University of California at San Diego |
| Benkirane, Soufiene |
University of Stirling |
| Bertolaso, Marta |
Università Campus Bio-Medico di Roma |
| Blinov, Michael |
University of Connecticut Health Center |
| Danos, Vincent |
University of Edinburgh |
| Davies, Jamie |
University of Edinburgh |
| Degasperi, Andrea |
University of Glasgow |
| Dunster, Joe |
University of Nottingham |
| Faeder, James R |
University of Pittsburgh |
| Galpin, Vashti |
University of Edinburgh |
| Goebel, Britta |
University of Luebeck |
| Goodfellow, Marc |
University of Manchester |
| Grinfeld, Michael |
University of Strathclyde |
| Guerriero, Maria Luisa |
University of Edinburgh |
| Gunawardena, Jeremy |
Harvard Medical School |
| Hillston, Jane |
University of Edinburgh |
| Hirt, Bartholomaeus |
University of Nottingham |
| Johnson, Colin |
University of Kent |
| Just, Winfried |
Ohio University |
| Kirkwood, Tom |
Newcastle University |
| Kritz, Maurício |
LNCC/MCT |
| Lander, Arthur D |
University of California, Irvine |
| Laubenbacher, Reinhard |
Virginia Tech |
| Leung, Siu-wai |
University of Macau |
| MacMillan, Hugh |
Clemson University |
| McCaig, Chris |
University of Stirling |
| Mohd Siam, Fuaada |
University of Strathclyde |
| Myers, Chris |
Cornell University |
| Nguyen, Lan |
Lincoln University, Christchurch |
| Ollivier, Julien |
University of Edinburgh |
| Pang, Jiayun |
University of Manchester |
| Popovic, Nikola |
University of Edinburgh |
| Proctor, Carole |
Newcastle University |
| Prokopiou, Sotiris |
University of Nottingham |
| Raman, Karthik |
University of Zurich |
| Reynolds, Jennifer |
Heriot-Watt University |
| Rinkevich, Baruch |
Israel Oceanographic Research Institute |
| Roberts, Fiona |
University of Strathclyde |
| Sainsbury, Chris |
University of Glasgow |
| Schuster, Peter |
University of Vienna |
| Schwartz, Jean-Marc |
University of Manchester |
| Shankland, Carron |
Univerisity of Stirling |
| Shanley, Daryl |
Newcastle University |
| Smith, Graham |
Newcastle University |
| Soto, Ana M |
Tufts University & University of Ulster at Coleraine |
| Van Regenmortel, Marc |
University of Strasbourg |
| Watson, Michael |
Heriot-Watt University |
| Webb, Steven |
University of Strathclyde |
| Wimsatt, William C |
University of Chicago |
| Wolkenhauer, Olaf |
University of Rostock |
Emerging Modelling Methodologies in Medicine and Biology