This presentation was given at a Royal Society of Chemistry Industrial Biotechnology Group Meeting on the 17th September 2013. The presentation covers the key aspects of the biobased chemical market and introduces the support for innovation given by the European Interreg IVB Bio Base NWE project.
Biobased Chemicals, Industrial Sugar and the development of BiorefineriesNNFCC
This presentation, developed as part of the Interreg NWE Bio Base NWE project, was presented at the UK Institute of Food Research Annual Food and Health Symposium. It provides an overview of developments in the biobased chemicals market and how the UK in developing an ecosystem for the development of Industrial Biotechnology including the potential for knowledge exchange in North West Europe.
This presentation was given at a Royal Society of Chemistry Industrial Biotechnology Group Meeting on the 17th September 2013. The presentation covers the key aspects of the biobased chemical market and introduces the support for innovation given by the European Interreg IVB Bio Base NWE project.
Biobased Chemicals, Industrial Sugar and the development of BiorefineriesNNFCC
This presentation, developed as part of the Interreg NWE Bio Base NWE project, was presented at the UK Institute of Food Research Annual Food and Health Symposium. It provides an overview of developments in the biobased chemicals market and how the UK in developing an ecosystem for the development of Industrial Biotechnology including the potential for knowledge exchange in North West Europe.
Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow u...Aaron Sloman
There is a sequel to this, with more emphasis on 'toddler theorems' and kinds of child science here:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#toddler
It is not yet stable enough to be uploaded to slideshare.
Per l'ottavo incontro della serie “Complessità in azione – 8 leve per cambiare il mondo”, organizzata dal Complexity Institute in collaborazione con Complexity Education Project, Pier Luigi Gentili ha svolto un dialogo insieme a Luigi Ferrata sul tema:
La trasformazione eco-sistemica
Towards smart modeling of mechanical properties of a bio composite based on ...IJECEIAES
The main interest in many research problems in polymer bio composites and machine learning (ML) is the development of predictive models to one or several variables of interest by the use of suitable independent inputs or variables. Nevertheless, these fields have generally adopted several approaches, while bio composite behavior modeling is usually based on phenomenological theories and physical models. These latter are more robust and precise, but they are generally under the restricted predictive ability due to the particular set of conditions. On the other hand, Machine learning models can be highly efficient in the modeling phase by allowing the management of high and massive dimensional sets of data to predict the best behavior of bio composites. In this situation, biomaterial scientists would like to benefit from the comprehension and implementation of the powerful ML models to characterize or predict the bio composites. In this study, we implement a smart methodology employing supervised neural network models to predict the bio composites properties presenting more significant environmental and economic advantages than composites reinforced by synthetic fibers.
Introduction to Systemics with focus on Systems BiologyMrinal Vashisth
The core content discusses the terminology used in Systems Sciences, the systems thinking/approach or Systemics. Focus is kept on Systems Biology for the most part of the presentations where it is compared with other disciplines and examples of Systems Biology approach and challenges of systems science are also discussed.
The sad thing about uploading this to Slide Share is that animations don't work.
Essay On College Education. 24 Greatest College Essay Examples RedlineSPMelissa Otero
College Essay Examples - 9 in PDF Examples. College and Education - Free Essay Example PapersOwl.com. Essay websites: Essay on the importance of college education. College Education: Should Education be Free Essay. St Joseph Hospital: College Application Essay. Importance of college education essay. Free importance of education .... 004 Essay Example Why Is College Important On Importance Of Education .... College Essay Format: Simple Steps to Be Followed. FREE 11 Sample College Essay Templates in MS Word PDF. Argumentative essay on college education. Sample College Application Essay 5. 021 10067 Thumb College Education Essay Thatsnotus. How to Write In College Essay Format OCC NJ. College Admissions Essay Workshop - 9 Types of Supplemental Essays .... Admission essay: Being a college student essay. This is How You Write a College Essay College application essay .... College Essay: Graduate school essay sample. Why College Should Be Cheaper Essay. Essay On The Importance Of College Education. 24 Greatest College Essay Examples RedlineSP. Why Do You Think College Education Is Important Essay. Impressive Essay On Education Thatsnotus. Essay for education - College Homework Help and Online Tutoring.. College education essay - 24/7 Homework Help.. Education in College - Free Essay Example PapersOwl.com. Everyone Should Enjoy a Free College Education - Free Essay Example .... 26 Outstanding College Essay Examples / - Example of a college essay .... Writing An Essay To Get Into College - Writing a strong college .... College essay: Importance of college education essay. Essay on why college education is important Essay On College Education Essay On College Education. 24 Greatest College Essay Examples RedlineSP
Practices for drawing biological networks using the SBGN standardVasundra Touré
Presented at the University of Rostock research seminar the 25th July 2016.
Abstract:
In Systems Biology, the visualization of data as networks support researchers in analyzing and understanding the biological system under study. Since 2009, creating an easy-to-understand and exchangeable network is possible using a standard called SBGN, the Systems Biology Graphical Notation. In this talk, I will show some good practices for generating biological networks using SBGN. After a short presentation of the Systems Biology Graphical Notation, I will give a demo on drawing SBGN maps using the SBGN-ED software.
Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow u...Aaron Sloman
There is a sequel to this, with more emphasis on 'toddler theorems' and kinds of child science here:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#toddler
It is not yet stable enough to be uploaded to slideshare.
Per l'ottavo incontro della serie “Complessità in azione – 8 leve per cambiare il mondo”, organizzata dal Complexity Institute in collaborazione con Complexity Education Project, Pier Luigi Gentili ha svolto un dialogo insieme a Luigi Ferrata sul tema:
La trasformazione eco-sistemica
Towards smart modeling of mechanical properties of a bio composite based on ...IJECEIAES
The main interest in many research problems in polymer bio composites and machine learning (ML) is the development of predictive models to one or several variables of interest by the use of suitable independent inputs or variables. Nevertheless, these fields have generally adopted several approaches, while bio composite behavior modeling is usually based on phenomenological theories and physical models. These latter are more robust and precise, but they are generally under the restricted predictive ability due to the particular set of conditions. On the other hand, Machine learning models can be highly efficient in the modeling phase by allowing the management of high and massive dimensional sets of data to predict the best behavior of bio composites. In this situation, biomaterial scientists would like to benefit from the comprehension and implementation of the powerful ML models to characterize or predict the bio composites. In this study, we implement a smart methodology employing supervised neural network models to predict the bio composites properties presenting more significant environmental and economic advantages than composites reinforced by synthetic fibers.
Introduction to Systemics with focus on Systems BiologyMrinal Vashisth
The core content discusses the terminology used in Systems Sciences, the systems thinking/approach or Systemics. Focus is kept on Systems Biology for the most part of the presentations where it is compared with other disciplines and examples of Systems Biology approach and challenges of systems science are also discussed.
The sad thing about uploading this to Slide Share is that animations don't work.
Essay On College Education. 24 Greatest College Essay Examples RedlineSPMelissa Otero
College Essay Examples - 9 in PDF Examples. College and Education - Free Essay Example PapersOwl.com. Essay websites: Essay on the importance of college education. College Education: Should Education be Free Essay. St Joseph Hospital: College Application Essay. Importance of college education essay. Free importance of education .... 004 Essay Example Why Is College Important On Importance Of Education .... College Essay Format: Simple Steps to Be Followed. FREE 11 Sample College Essay Templates in MS Word PDF. Argumentative essay on college education. Sample College Application Essay 5. 021 10067 Thumb College Education Essay Thatsnotus. How to Write In College Essay Format OCC NJ. College Admissions Essay Workshop - 9 Types of Supplemental Essays .... Admission essay: Being a college student essay. This is How You Write a College Essay College application essay .... College Essay: Graduate school essay sample. Why College Should Be Cheaper Essay. Essay On The Importance Of College Education. 24 Greatest College Essay Examples RedlineSP. Why Do You Think College Education Is Important Essay. Impressive Essay On Education Thatsnotus. Essay for education - College Homework Help and Online Tutoring.. College education essay - 24/7 Homework Help.. Education in College - Free Essay Example PapersOwl.com. Everyone Should Enjoy a Free College Education - Free Essay Example .... 26 Outstanding College Essay Examples / - Example of a college essay .... Writing An Essay To Get Into College - Writing a strong college .... College essay: Importance of college education essay. Essay on why college education is important Essay On College Education Essay On College Education. 24 Greatest College Essay Examples RedlineSP
Practices for drawing biological networks using the SBGN standardVasundra Touré
Presented at the University of Rostock research seminar the 25th July 2016.
Abstract:
In Systems Biology, the visualization of data as networks support researchers in analyzing and understanding the biological system under study. Since 2009, creating an easy-to-understand and exchangeable network is possible using a standard called SBGN, the Systems Biology Graphical Notation. In this talk, I will show some good practices for generating biological networks using SBGN. After a short presentation of the Systems Biology Graphical Notation, I will give a demo on drawing SBGN maps using the SBGN-ED software.
Biological Apps: Rapidly Converging Technologies for Living Information Proce...Natalio Krasnogor
This is a plenary talk I gave at the 2018 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems in Cadiz, Spain
Plenary Speaker slides at the 2016 International Workshop on Biodesign Automa...Natalio Krasnogor
In this talk I discuss recent work done in my lab and with collaborators abroad that contributes towards accelerating the specify -> design -> model -> build -> test & iterate biological engineering cycle. This will describe advances in biological programming languages for specifying combinatorial DNA libraries, the utilisation of off-the-shelf microfluidic devices to build the DNA libraries as well as data analysis techniques to accelerate computational simulations
Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this talk we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. We then fast forward to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years.
Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and S...Natalio Krasnogor
In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-‐organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin’s wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.
These slides were used for a tutorial I gave at GECCO 2010. These are similar, yet not identical, to the other tutorials. The keynote file is too large for slideshare but if anybody needs the original I would be happy to provide a url from where to download it.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
A Genetic Programming Challenge: Evolving the Energy Function for Protein Str...Natalio Krasnogor
In this talk I introduce a computational challenge for GP researchers, namely, the automated synthesis of energy functions for protein structure prediction.
Building Executable Biology Models for Synthetic BiologyNatalio Krasnogor
The leveraging of today's unprecedented capability to manipulate biological systems by state-of-the-art computational, mathematical and engineering techniques , may profoundly affect the way we approach the solution to pressing grand challenges such as the development of sustainable green energy, next generation healthcare, etc. The conceptual cornerstone of Synthetic Biology a field very much on its infancy- is that methodologies commonly used to design and construct non-biological artefacts (e.g. computer programs, airplanes, bridges, etc) might also be mastered to create designer living entities. Computational methods for modeling in Synthetic Biology consist of a list of instructions detailing an algorithm that can be executed and whose computation resembles the behavior of the biological system under study. This computational approach to modelling biological systems has been termed executable biology. In this talk I will describe current approaches for the automated generation and testing of executable biology models for synthetic biology.
This was a colloquioum talk at the Computer Science Department, Ben-Gurion University of the Negev, Israel (30/June/2009)
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimen...Natalio Krasnogor
In this talk we demonstrate an ECGA and LCS pipeline for reducing protein alphabets from the standard 20 to 5 or less symbols without significant loss of information. The pipeline tailors the reduction to different problems thus resulting on different optimal minimal alphabets.
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
1. Synthetic Biology: Modelling and
Optimisation
Natalio Krasnogor
ASAP - Interdisciplinary Optimisation Laboratory
School of Computer Science
Centre for Integrative Systems Biology
School of Biology
Centre for Healthcare Associated Infections
Institute of Infection, Immunity & Inflammation
University of Nottingham
Copyright is held by the author/owner(s).
GECCO’09, July 8–12, 2009, Montréal Québec, Canada.
ACM 978-1-60558-505-5/09/07.
1 /203
Thursday, 9 July 2009
2. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
2 /203
Thursday, 9 July 2009
3. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
3 /203
Thursday, 9 July 2009
4. Synthetic Biology
• Aims at designing, constructing and developing artificial biological
systems
•Offers new routes to ‘genetically modified’ organisms, synthetic living
entities, smart drugs and hybrid computational-biological devices.
• Potentially enormous societal impact, e.g., healthcare, environmental
protection and remediation, etc
• Synthetic Biology's basic assumption:
• Methods commonly used to build non-biological systems could
also be use to specify, design, implement, verify, test and deploy
novel synthetic biosystems.
• These method come from computer science, engineering and
maths.
• Modelling and optimisation run through all of the above.
4 /203
Thursday, 9 July 2009
5. Models and Reality
•The use of models is intrinsic to any
scientific activity.
•Models are abstractions of the real-world
that highlight some key features while
ignoring others that are assumed to be not
relevant.
•A model should not be seen or presented
as representations of the truth, but instead
as a statement of our current knowledge.
5 /203
Thursday, 9 July 2009
6. What is modelling?
• Is an attempt at describing in a
precise way an understanding of the
elements of a system of interest, their
states and interactions
• A model should be operational, i.e. it
should be formal, detailed and
“runnable” or “executable”.
6 /203
Thursday, 9 July 2009
7. •“feature selection” is the first issue one
must confront when building a model
•One starts from a system of interest
and then a decision should be taken as
to what will the model include/leave out
•That is, at what level the model will be
built
7 /203
Thursday, 9 July 2009
8. The goals of Modelling
•To capture the essential features of
a biological entity/phenomenon
•To disambiguate the understanding
behind those features and their
interactions
•To move from qualitative knowledge
towards quantitative knowledge
8 /203
Thursday, 9 July 2009
9. •There is potentially a distinction between modelling for Synthetic Biology
and Systems Biology:
•Systems Biology is concerned with Biology as it is
•Synthetic Biology is concerned with Biology as it could be
“Our view of engineering biology focuses on the abstraction and
standardization of biological components” by R. Rettberg @ MIT newsbite
August 2006.
“Well-characterized components help lower the barriers to modelling. The
use of control elements (such as temperature for a temperature-sensitive
protein, or an exogenous small molecule affecting a reaction) helps model
validation” by Di Ventura et al, Nature, 2006
9 /203
Thursday, 9 July 2009
10. •There is potentially a distinction between modelling for Synthetic Biology
and Systems Biology:
•Systems Biology is concerned with Biology as it is
•Synthetic Biology is concerned with Biology as it could be
“Our view of engineering biology focuses on the abstraction and
standardization of biological components” by R. Rettberg @ MIT newsbite
August 2006.
“Well-characterized components help lower the barriers to modelling. The
use of control elements (such as temperature for a temperature-sensitive
protein, or an exogenous small molecule affecting a reaction) helps model
validation” by Di Ventura et al, Nature, 2006
Co-design of parts and their models hence improving
and making both more reliable
9 /203
Thursday, 9 July 2009
11. Thus, Multi-Scale Modelling in the 2 SBs seek
to produce computable understanding
integrating massive datasets at various levels
of details simultaneously
Progress
Organ Individual
Cell colony
Cells
Regulatory
Networks
Proteins
DNA/RNA
Time
10 /203
Thursday, 9 July 2009
12. The Pragmalogical Problem of
Modelling in XXI century Biology
• XXI century Biology brings to the fore the ubiquitous philosophical
questions in complex systems, that of emergent behavior and the
tension between reductionism and holistic approaches to science.
• Synthetic Biology (and SysBio) has, however, a very pragmatic
agenda: the engineering and control of novel biological systems
• The pragmalogical problem: If each subcomponent of a living system
(and processes/components therein) are understood… Can we say that
the system is understood? That is, can we assume that the system =
∑parts ?
• More importantly: can we control that biosystem?
11 /203
Thursday, 9 July 2009
13. The Pragmalogical Problem of
Modelling in XXI century Biology
• XXI century Biology brings to the fore the ubiquitous philosophical
questions in complex systems, that of emergent behavior and the
tension between reductionism and holistic approaches to science.
& Integrative
• Synthetic Biology (and SysBio) has, however, a very pragmatic
agenda: the engineering and control of novel biological systems
• The pragmalogical problem: If each subcomponent of a living system
(and processes/components therein) are understood… Can we say that
the system is understood? That is, can we assume that the system =
∑parts ?
• More importantly: can we control that biosystem?
11 /203
Thursday, 9 July 2009
14. Modelling relies on rigorous computational,
engineering and mathematical tools &
techniques
However, the act of modelling remains at the
interface between art and science
Undoubtedly, a multidisciplinary endeavour
12 /203
Thursday, 9 July 2009
15. Modelling as a constrained
scientific art
Although modelling lies at the interface of art
and science there are guidelines we can
follow
Some examples:
The scale separation map [Hoekstra et al, LNCS 4487, 2007]
Tools suitability & cost [Goldberg, 2002]
13 /203
Thursday, 9 July 2009
16. The Scale Separation Map
The Scale Separation Map is an
abstraction recently proposed by Hoekstra
and co-workers [Hoekstra et al, LNCS
4487, 2007]
Introduced in the context of Multi-scale
modelling with cellular automata but the
core concepts still valid for other modelling
techniques
14 /203
Thursday, 9 July 2009
17. The Scale Separation Map
A Cellular Automata is defined as:
C= < A(Δx, Δt,L,T), S, R, G, F >
A is a spatial domain made of cells of size Δx with a total size of L
The simulation clock ticks every Δt units for a total of T units
T
We can simulate processes: Δt
as fast as Δt for as long as T units
ranging from Δx to L sizes. Δx
L
L
15 /203
Thursday, 9 July 2009
18. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
1 0 B ξB
A ξA τB
Spatial scale (log)
τA
3.1 2 3.2
Temporal scale (log)
16 /203
Thursday, 9 July 2009
19. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
B ξB temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
τB coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
16 /203
Thursday, 9 July 2009
20. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
B ξB temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
τB coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
16 /203
Thursday, 9 July 2009
21. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
B ξB • Region 3.2: ξA > ξB ^ τB >
τB τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
16 /203
Thursday, 9 July 2009
22. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
B ξB
τA small and slow
τB process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
16 /203
Thursday, 9 July 2009
23. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
B ξB • Region 3.2: ξA > ξB ^ τB >
τB τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
16 /203
Thursday, 9 July 2009
24. Even within a single cell the space & time
scale separations are important
E.g.:
• Within a cell the dissociation
constants of DNA/ transcription
factor binding to specific/non-
specific sites differ by 4-6 orders of
magnitude
• DNA protein binding occurs at 1-10s
time scale very fast in comparison
to a cell’s life cycle.
[F.J. Romero Campero, 2007]
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25. The Scale Separation Map
• With sufficient data each process can be
assigned its space-time region
unambiguously
Couplings, e.g. F • A given process may well have its Δx
(respectively Δt) > than another’s ξA
(respectively τA)
Spatial scale (log)
• Hence different processes in the SSM might
require different modelling techniques
Temporal scale (log)
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26. Modelling Approaches
There exist many modelling approaches, each with its
advantages and disadvantages.
Macroscopic, Microscopic and Mesoscopic
Quantitative and qualitative
Discrete and Continuous
Deterministic and Stochastic
Top-down or Bottom-up
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27. Modelling Frameworks
•Denotational Semantics Models:
Set of equations showing relationships between molecular
quantities and how they change over time.
They are approximated numerically.
(I.e. Ordinary Differential Equations, PDEs, etc)
•Operational Semantics Models:
Algorithm (list of instructions) executable by an abstract
machine whose computation resembles the behaviour of the
system under study. (i.e. Finite State Machine)
Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249
(2008)
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28. Tools Suitability and Cost
From [D.E Goldberg, 2002] (adapted):
“Since science and math are in the description
business, the model is the thing…The engineer
or inventor has much different motives. The
engineered object is the thing”
ε, error
Synthetic Biologist
Computer Scientist/Mathematician
C, cost of modelling
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29. Tools Suitability and Cost
Low cost/ High cost/
High error Low error
Adapted from [Goldberg 2002]
Unarticulated Articulated Dimensional Facetwise Equations
wisdom Qualitative models models Of motion
models
Chemical Bioinformatic Biopolimer Microarrays and G.E.
Markup Language Sequence Markup Markup Language Markup Language
(CML) Language (BSML) (BioML) (MAGEML)
Cell Systems Biology Mathematics
Markup Language Markup Language Markup Language
(MathML) (SBML) (MathML)
22 /203
Thursday, 9 July 2009
30. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
23 /203
Thursday, 9 July 2009
31. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
23 /203
Thursday, 9 July 2009
32. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
23 /203
Thursday, 9 July 2009
34. Stochasticity in Cellular Systems
Most commonly recognised sources of noise in cellular system are low
number of molecules and slow molecular interactions.
Over 80% of genes in E. coli express fewer than a hundred proteins per cell.
Mesoscopic, discrete and stochastic approaches are more suitable:
Only relevant molecules are taken into account.
Focus on the statistics of the molecular interactions and how often they
take place.
Mads Karn et al. Stochasticity in Gene Expression: From Theories to Phenotypes. Nature Reviews, 6,
451-464 (2005)
Purnananda Guptasarma. Does replication-induced transcription regulate synthesis of the myriad low
copy number poteins of E. Coli. BioEssays, 17, 11, 987-997
25 /203
Thursday, 9 July 2009
35. Towards Executable Modells for SBs
“Although the road ahead is long and winding, it leads to a
future where biology and medicine are transformed into
precision engineering.” - Hiroaki Kitano.
Synthetic Biology and Systems biology promise more than
integrated understanding: it promises systematic control of
biological systems:
1. From an experimental viewpoint: Improved data acquisition
2. From a bioinformatics viewpoint: Improved data analysis tools
3. From a conceptual viewpoint: move from a science of mass-action/
energy-conversion to a science of information processing through
multiple heterogeneous medium
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Thursday, 9 July 2009
36. There are good reasons to think that information
processing is a key viewpoint to take when modeling
Life as we know is:
• coded in discrete units (DNA, RNA, Proteins)
• combinatorially assembles interactions (DNA-RNA, DNA-
Proteins,RNA-Proteins , etc) through evolution and self-organisation
• Life emerges from these interacting parts
• Information is:
• transported in time (heredity, memory e.g. neural, immune
system, etc)
• transported in space (molecular transport processes, channels,
pumps, etc)
• Transport in time = storage/memory a computational process
• Transport in space = communication a computational process
• Signal Transduction = processing a computational process
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37. It thus makes sense to use methodologies
designed to cope with complex,
concurrent, interactive systems of parts as
found in computer sciences (e.g.):
Petri Nets
Process Calculi
P-Systems
28 /203
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38. InfoBiotics
www.infobiotic.net
•The utilisation of cutting-edge information
processing techniques for biological modelling and
synthesis
•The understanding of life itself as multi-scale
(Spatial/Temporal) information processing systems
•Composed of 3 key components:
•Executable Biology (or other modeling
techniques)
•Automated Model and Parameter Estimation
•Model Checking (and other formal analysis)
29 /203
Thursday, 9 July 2009
39. Modeling in Systems & Synthetic Biology
Systems Biology Synthetic Biology
Colonies
• Understanding •Control
• Integration • Design
• Prediction • Engineering
• Life as it is •Life as it could be
Cells
Computational modelling to Computational modelling to
elucidate and characterise engineer and evaluate
modular patterns exhibiting possible cellular designs
robustness, signal filtering, exhibiting a desired
amplification, adaption, behaviour by combining well
error correction, etc. studied and characterised
Networks cellular modules
30 /203
Thursday, 9 July 2009
40. Model Design in Systems/Synthetic Biology
• It is a hard process to design suitable models in systems/
synthetic biology where one has to consider the choice of the
model structure and model parameters at different points
repeatedly.
• Some use of computer simulation has been mainly focused on
the computation of the corresponding dynamics for a given
model structure and model parameters.
• Ultimate goal: for a new biological system (spec) one would like
to estimate the model structure and model parameters (that
match reality/constructible) simultaneously and automatically.
• Models should be clear & understandable to the biologist
31 /203
Thursday, 9 July 2009
41. How you select features, disambiguate and
quantify depends on the goals behind your
modelling enterprise.
Basic goal: to clarify current understandings by
formalising what the constitutive elements of a system
Systems Biology
are and how they interact
Intermediate goal: to test current understandings
Synthetic Biology
against experimental data
Advanced goal: to predict beyond current
understanding and available data
Dream goal:
(1) to combinatorially combine in silico well-understood
components/models for the design and generation of novel
experiments and hypothesis and ultimately
(2) to design, program, optimise & control (new) biological
systems
32 /203
Thursday, 9 July 2009
42. Model Development
From [E. Klipp et al, Systems Biology in Practice,
2005]
1. Formulation of the problem
2. Verification of available information
3. Selection of model structure
4. Establishing a simple model
5. Sensitivity analysis
6. Experimental tests of the model predictions
7. Stating the agreements and divergences between
experimental and modelling results
8. Iterative refinement of model
33 /203
Thursday, 9 July 2009
43. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
34 /203
Thursday, 9 July 2009
44. Executable Biology with P systems
Field of membrane computing initiated by
Gheorghe Păun in 2000
Inspired by the hierarchical membrane structure
of eukaryotic cells
A formal language: precisely defined and
machine processable
An executable biology methodology
35 /203
Thursday, 9 July 2009
45. Functional Entities
Container
• A boundary defining self/non-self (symmetry breaking).
• Maintain concentration gradients and avoid environmental damage.
Metabolism
• Confining raw materials to be processed.
• Maintenance of internal structures (autopoiesis).
Information
• Sensing environmental signals / release of signals.
• Genetic information
36 /203
Thursday, 9 July 2009
46. Distributed and parallel rewritting systems in
compartmentalised hierarchical structures.
Objects
Compartments
Rewriting Rules
• Computational universality and efficiency.
• Modelling Framework
37 /203
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47. Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.
the classic P system diagram appearing in most papers
(Păun)
38 /203
Thursday, 9 July 2009
48. Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.
formally equivalent to a tree:
1
2 4
3
7
5 6
the classic P system diagram appearing in most papers
(Păun)
8 9
38 /203
Thursday, 9 July 2009
49. Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.
formally equivalent to a tree:
1
2 4
3
7
5 6
the classic P system diagram appearing in most papers
(Păun)
8 9
• a string of matching parentheses: [ 1 [2 ] 2 [ 3 ] 3 [4 [5 ] 5 [6 [ 8 ] 8 [9 ] 9 ]6
[7 ]7 ]4 ]1
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Thursday, 9 July 2009
50. P-Systems: Modelling Principles
Molecules Objects
Structured Molecules Strings
Molecular Species Multisets of objects/
strings
Membranes/organelles Membrane
Biochemical activity rules
Biochemical transport Communication rules
39 /203
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52. Rewriting Rules
used by Multi-volume Gillespie’s algorithm
41 /203
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53. Molecular Species
A molecular species can be represented using
individual objects.
A molecular species with relevant internal structure
can be represented using a string.
42 /203
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54. Molecular Interactions
Comprehensive and relevant rule-based schema
for the most common molecular interactions taking
place in living cells.
Transformation/Degradation
Complex Formation and Dissociation
Diffusion in / out
Binding and Debinding
Recruitment and Releasing
Transcription Factor Binding/Debinding
Transcription/Translation
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55. Compartments / Cells
Compartments and regions are explicitly
specified using membrane structures.
44 /203
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56. Colonies / Tissues
Colonies and tissues are representing as
collection of P systems distributed over a lattice.
Objects can travel around the lattice through
translocation rules.
v
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66. Translation as Rewriting Rules on
Multisets of Objects and Strings
55 /203
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67. Post-Transcriptional Processes
For each protein in the system, post-transcriptional processes like
translational initiation, messenger and protein degradation, protein
dimerisation, signal sensing, signal diffusion etc are represented using
modules of rules.
Modules can have also as parameters the stochastic kinetic constants
associated with the corresponding rules in order to allow us to explore
possible mutations in the promoters and ribosome binding sites in order to
optimise the behaviour of the system.
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68. Scalability through Modularity
Cellular functions arise from orchestrated
interactions between motifs consisting of
many molecular interacting species.
A P System model is a set of rules
representing molecular interactions motifs
that appear in many cellular systems.
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69. Basic P System Modules Used
58 /203
Thursday, 9 July 2009
70. Modularity in Gene Regulatory
Networks
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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71. Modularity in Gene Regulatory
Networks AHL
LuxR CI
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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Thursday, 9 July 2009
72. Modularity in Gene Regulatory
Networks AHL
LuxR CI
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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73. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
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74. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
75. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
76. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
77. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
78. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
79. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
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80. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
61 /203
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81. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
61 /203
Thursday, 9 July 2009
82. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
A
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83. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
A
61 /203
Thursday, 9 July 2009
84. Representing synthetic
transcriptional networks
The genes used to instantiate variables in our modules can
codify other TFs that interact with other modules or promoters
producing a synthetic gene regulatory network.
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85. Representing synthetic
transcriptional networks
The genes used to instantiate variables in our modules can
codify other TFs that interact with other modules or promoters
producing a synthetic gene regulatory network.
62 /203
Thursday, 9 July 2009
86. Stochastic P Systems
Gillespie Algorithm (SSA) generates trajectories of a stochastic
system consisting of modified for multiple compartments/volumes:
1) A stochastic constant is associated with each rule.
2) A propensity is computed for each rule by multiplying the
stochastic constant by the number of distinct possible
combinations of the elements on the left hand side of the rule.
3) The rule to apply j0 and the waiting time τ for its application
are computed by generating two random numbers r1,r2 ~ U(0,1)
and using the formulas:
F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor.
Modular assembly of cell systems biology models using p systems. International Journal of
Foundations of Computer Science, 2009
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101. Using P systems modules one can model a large variety of
commonly occurring BRN:
Gene Regulatory Networks
Signaling Networks
Metabolic Networks
This can be done in an incremental way.
F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor.
Modular assembly of cell systems biology models using p systems. International Journal of
Foundations of Computer Science, 2009
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102. InfoBiotics
Pipeline
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108. Multi-component negative-
feedback oscillator
Oscillations caused by time-delayed negative-feedback:
Negative-feedback: gene-product that represses it's gene
Time-delay: mRNA export, translation and repressor import
Novak & Tyson: Design Principles of Biochemical Oscillators. Nat. Rev. Mol. Cell. Biol. 9: 981-991 (2008)
72 /203
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109. Multi-component negative-
feedback oscillator
Mathematical model
− Xc = [mRNA in cytosol]
− Yc = [protein in cytosol]
− Xn = [mRNA in nucleus]
− Yn = [protein in nucleus]
− E = [total protease]
− p = “integer indicating
whether Y binds to DNA as a
monomer, trimer, or so on”
Executable Biology makes this more obvious:
we can vary the value of p and the sequence of binding...
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111. When repression is weak
(dissociation rate = 10)
No obvious oscillatory behaviour in single simulation
75 /203
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112. When repression is weak
(dissociation rate = 10)
Mean of 100 runs shows convergence to steady state
76 /203
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113. When repression is strong
(dissociation rate = 0.1)
Oscillations evident in single simulation
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114. When repression is strong
(dissociation rate = 0.1)
Averging 100 runs dampens oscillations due to different
phases but observable. Protein levels steady.
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115. Repressor binding sequence
When p=2 there are two possible scenarios:
– First protein binds to second protein weakly then
protein-dimer binds to gene strongly
– First protein binds to gene weakly then second
protein binds to protein-gene dimer strongly
In the following only the model structure is
changed, not the parameters
First dissociation rate = 10
Second dissociation rate = 0.1
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119. 2. Proteins repress cooperatively
target
Oscillations are steady and protein levels are controlled
83 /203
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120. An example: Ron Weiss' Pulse Generator
Two different bacterial strains carrying specific synthetic
gene regulatory networks are used.
The first strain produces a diffusible signal AHL.
The second strain possesses a synthetic gene regulatory
network which produces a pulse of GFP after AHL sensing.
These two bacterial strains and their respective synthetic
networks are modelled as a combination of modules.
S. Basu, R. Mehreja, et al. (2004) Spatiotemporal control of gene expression with pulse
generating networks, PNAS, 101, 6355-6360
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132. Pulse Generating Cells
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux
cI
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133. Pulse Generating Cells
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
CI
Plux
cI
86 /203
Thursday, 9 July 2009
134. Pulse Generating Cells
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
…
CI
Diff({X=AHL},…)
Plux
cI
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135. Spatial Distribution of Senders
and Pulse Generators
AHL
GFP AHL
LuxR
Pconst PluxOR1
luxR gfp LuxI AHL
CI Pconst luxI
Plux
cI
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136. AHL
Spatial Distribution of Senders
and Pulse Generators
AHL
GFP AHL
LuxR
Pconst PluxOR1
luxR gfp LuxI AHL
CI Pconst luxI
Plux
cI
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137. Wave propagation
simulation I
SIMULATION I
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138. Pulse Generating Cells
AHLWith Relay
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
CI
Plux
cI
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139. Pulse Generating Cells
AHLWith Relay
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
89 /203
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140. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
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Thursday, 9 July 2009
141. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
89 /203
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142. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
89 /203
Thursday, 9 July 2009
143. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
Plux CI
luxI
LuxI
Plux
cI
AHL
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147. Wave propagation
simulation II
SIMULATION II
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36
Thursday, 9 July 2009
148. AHL
Spatial Distribution of
Pulse Generators and Seed
AHL
LuxR GFP
Pconst PluxOR1
luxR gfp
Plux CI
luxI
Plux
cI
LuxI
AHL
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Thursday, 9 July 2009
149. Wave propagation with
Four Droplets of Signal
SIMULATION III
92 /203
38
Thursday, 9 July 2009
153. Inversion Through a
Propagating Wave
SIMULATION IV
95 /203
41
Thursday, 9 July 2009
154. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
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Thursday, 9 July 2009
155. Probabilistic Model Checking
A precise computational/mathematical model allows
us to perform formal verification techniques:
Probabilistic model checking.
Properties are expressed formally using temporal
logic and analysed.
The fundamental components of the PRISM language
are modules, variables and commands.
• A model is composed of a number of modules which can
interact with each other.
• A module contains a number of local variables and commands.
97 /203
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156. P Systems and PRISM
P System Component PRISM Component
Membrane Module
Multisets of Objects Local Variables
Rewriting rules Commands
Rewards/Costs are associated with states and transitions representing
the number of objects and the application of rules.
Some Properties:
Expected Number of objects over time: R = ? [ I = T ]
Expected Number of rule application over time: R = ? [ C <= T ]
Expected Time to reach a state: R = ? [ F molec_1 = K ]
Transient properties: P = ? [ true U[t_1 t_2] molec_1 >= K_1 ]
Steady State/Long run properties: S = ? [ molec_1 >= K_1]
PRISM is used as an example. Other model checkers are more appropriate for larger systems
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159. Positive Regulation
[ TF + gene ] b [ TF.gene ] b con
[ TF.gene ] b [ TF + gene ] b coff
[ gene ]b [ gene + rna ]b ctrc
[ rna ]b [ ]b c2
[ rna ]b [ rna + Protein ]b ctrl
[ Protein ]b [ ]b c4
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160. Positive Regulation
R = ? [ C <= 100 ] R = ? [ C <= 100 ]
P = ? [ true U[60,60] Proteins = N ]
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161. Model Checking on the Pulse
Generator
The simulation of the Pulse Generator show some interesting
properties that were subsequently analysed using model checking.
Due to the complexity of the system (state space explosion) we
perform approximate model checking with a precision of 0.01 and a
confidence of 0.001 which needed to run 100000 simulations.
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162. Model Checking on the Pulse
Generator
The simulations show that although the number of signals
reaches eventually the same level in all the cells in the lattice
those cells that are far from the sending cells produce fewer
number of GFP molecules.
The difference between cells close to and far from the
sending cells is the rate of increase of the signal AHL.
We study the effect of the rate of increase of the signal AHL
in the number of GFP produced.
S. Basu, R. Mehreja, et al. Spatiotemporal control of gene expression with pulse generating
networks, PNAS, 101, 6355-6360
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163. We studied the expected number of GFP molecules produced over time for
different increase rates of AHL.
R = ? [ I = 60 ]
rewards
molecule = 1 : proteinGFP;
endrewards
The system is expected to
produce longer pulses with
lower amplitudes for slow
increase rates of AHL
signals.
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164. In order to get a clearer idea, the probability distribution of the number of
GFP molecules at 60 minutes was computed.
P = ? [ true U[60,60] ((proteinGFP > N) & (proteinGFP <= (N + 10))) ]
Note that for slow
increase rates of AHL
the probability of having
NO GFP molecules at
all is high.
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165. Finally, assuming that for a cell to be fluorescence it needs to have a given
number of GFP for an appreciable period of time we studied the expected
amount of time a cell have more than 50 GFP molecules during the first 60
minutes after the signals arrive to the cell.
R = ? [ C <= 60 ]
rewards
true : proteinGFP;
endrewards
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166. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
108 /203
Thursday, 9 July 2009
167. A (Proto)Cell as an Information
Processing Device
LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007)
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168. Towards a synthetic cell from
the bottom up
Biocompatible vesicles as long-circulating carriers
Polymer self-assembly into higher-order structures
Cell-mimics with hydrophobic ‘cell-wall’ and glycosylated
surfaces
Potential for cross-talk with biological cells
Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850
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169. Vesicle Biorecognition
Pasparakis, G. et al, Angew Chem Int Ed. 2008 47 (26), 4847-4850
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170. ‘Talking’ to cell-vesicle aggregates
Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850
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171. Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•A pinch of Model Checking
•Modeling for the Bottom Up SB
•Dissipative Particle Dynamics
•Automated Model Synthesis and Optimisation
•Conclusions
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172. Dissipative Particle Dynamics
Simulate movement of particles which represent several
atoms / molecules
Calculate forces acting on particles, integrate equations of
motion
Used extensively for investigating the self-assembly of lipid
membrane structures at the mesoscale
Typical simulations contain ~105-106 particles, for ~105-106 time
steps
Particles interact with each other within a finite radius much
smaller than the simulation space, algorithmic optimisations of
force calculations are possible
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173. Dissipative Particle Dynamics
First introduced by Hoogerbrugge and Koelmann in 1992.
Statistical mechanics of the model derived by espanol and warren in
1995.
A coarse graining approach is used so that one simulation particle
represents a number of real molecules of a given type.
Since the timescale at which interactions occur is longer than in MD,
fewer time-steps are required to simulation the same period of real time.
The short force cut-off radius enables optimisation of the force calculation
code to be performed.
O
H H W
O O
H H H H
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174. Dissipative Particle Dynamics
Conservative Force
i W
P
Dissipative Force
j W
P
Random Force
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175. Dissipative Particle Dynamics
Polymers
A number of simulation beads are tied together to
represent the original molecule.
Two new forces are introduced between polymer
particles, a Hookean spring force and a bond angle
force.
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178. Case Study One: Vesicle Diffusion
Polar heads
Non polar tails
Pores
J. Smaldon, J. Blake, D. Lancet, and N. Krasnogor. A multi-scaled approach to artificial life simulation
with p systems and dissipative particle dynamics. In Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2008), ACM Publisher, 2008.
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179. Case Study One: Vesicle
Diffusion
The regions were formed by allowing vesicles to self-
assemble from phospholipids in the presence of pore
inclusions
Pores are simple channels with an exterior mimicking
the hydrophobic/hydrophilic profile of the bilayer
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181. Case Study One: Vesicle Diffusion
Tagged solvent particles were placed within the liposome inner
volume, the change in concentration due to diffusion of solvent
through the membrane pores was measures
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182. Case Study Two: Liposome
Logic
The behaviour of some prokaryotic RNA
transcription motifs matches that of
boolean logic gates[1]
DPD was extended with mesoscale
collision based reactions.
transcriptional logic gates were simulated
in bulk solvent and within a liposome core
volume.
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