The document discusses modeling and simulation of active soft matter physics models using interactive simulations and knowledge sharing. It describes modeling biological systems using objects like cells and processes like chemical reactions, transport, growth and motion. It presents the Mechanica modeling language which describes the physical world in terms of objects and processes, and uses a simple syntax based on TypeScript to define computational models in a way that is close to physical reality. The language aims to facilitate model interchange and be easily learned based on physical concepts.
Algorithmic Dynamics of Cellular AutomataHector Zenil
Original presentation prepared for the opening keynote of the meeting in celebration of Prof. Harold McIntosh. This talk covers aspects of the complexity and behaviour of cellular automata and their emergent dynamic patterns and information dynamics of events such as particle collisions.
FUZZY IMAGE SEGMENTATION USING VALIDITY INDEXES CORRELATIONijcsit
This paper introduces an algorithm for image segmentation using a clustering technique; the technique is
based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters.
Furthermore, simultaneously five validity indexes are calculated and their information is correlated to
determine the optimal number of clusters in order to segment an image, results and simulations are shown
in the paper.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Complexity and Computation in Nature: How can we test for Artificial Life?Hector Zenil
Invited Guest Lecture on Complexity and Natural Computing for the ShangAI Lectures on Natural and Artificial Intelligence. Broadcasted simultaneously to about 20 universities: UC3 Madrid; Zurich; Pisa, Humboldt, Berlin; Plymouth & Salford, UK; China and Japan, November 7, 2013. Video available at https://cast.switch.ch/vod/clips/2m1xxl21ej/
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
Algorithmic Dynamics of Cellular AutomataHector Zenil
Original presentation prepared for the opening keynote of the meeting in celebration of Prof. Harold McIntosh. This talk covers aspects of the complexity and behaviour of cellular automata and their emergent dynamic patterns and information dynamics of events such as particle collisions.
FUZZY IMAGE SEGMENTATION USING VALIDITY INDEXES CORRELATIONijcsit
This paper introduces an algorithm for image segmentation using a clustering technique; the technique is
based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters.
Furthermore, simultaneously five validity indexes are calculated and their information is correlated to
determine the optimal number of clusters in order to segment an image, results and simulations are shown
in the paper.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Complexity and Computation in Nature: How can we test for Artificial Life?Hector Zenil
Invited Guest Lecture on Complexity and Natural Computing for the ShangAI Lectures on Natural and Artificial Intelligence. Broadcasted simultaneously to about 20 universities: UC3 Madrid; Zurich; Pisa, Humboldt, Berlin; Plymouth & Salford, UK; China and Japan, November 7, 2013. Video available at https://cast.switch.ch/vod/clips/2m1xxl21ej/
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
Chemical dynamics and rare events in soft matter physicsBoris Fackovec
Talk for the Trinity Math Society Symposium. First summarises the approximations leading from Dirac equation to molecular description and then the synthesis towards non-equilibrium statistical mechanics. The relaxation approach to projection of a molecular system to a Markov jump process is discussed.
Single fault detection and diagnosis technique for digital micro fluidic base...csandit
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-
fluidic based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
SINGLE FAULT DETECTION AND DIAGNOSIS TECHNIQUE FOR DIGITAL MICRO-FLUIDIC BASE...cscpconf
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-fluidic
based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
SINGLE FAULT DETECTION AND DIAGNOSIS TECHNIQUE FOR DIGITAL MICRO-FLUIDIC BASE...csandit
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-
fluidic based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
ANALYSIS OF ELEMENTARY CELLULAR AUTOMATA BOUNDARY CONDITIONSijcsit
We present the findings of analysis of elementary cellular automata (ECA) boundary conditions. Fixed and variable boundaries are attempted. The outputs of linear feedback shift registers (LFSRs) act as continuous inputs to the two boundaries of a one-dimensional (1-D) Elementary Cellular Automata (ECA) are analyzed and compared. The results show superior randomness features and the output string has passed the Diehard statistical battery of tests. The design has strong correlation immunity and it is inherently amenable for VLSI implementation. Therefore it can be considered to be a good and viable candidate for parallel pseudo random number generation
TOMOGRAPHY OF HUMAN BODY USING EXACT SIMULTANEOUS ITERATIVE RECONSTRUCTION AL...cscpconf
In this paper an Exact Simultaneous Iterative Reconstruction Algorithm is developed and applied on a large semi human size normal biological model and a diseased model (liver region affected) to verify the efficiency of the algorithm. The algorithm is successfully reconstructed the normal model having 15%-20% perturbation i.e. change in permittivity during disease. In diseased case, reconstructed imaginary part of complex permittivity clearly detects the affected zone and it may help the medical diagnosis. Hence it may be a powerful tool for early detection of cancerous tumors as the interrogating wave is a noninvasive one at the ultra high frequency range. The resolution of this system is increased with the reduction of
wavelength by immersing the antenna system and the model in saline water region. The advantage of this algorithm is that the calculation of cofactor are done offline to save the computational time and cofactors are expressed as a function of distances irrespective of their positions
This poster was created in LaTeX on a Dell Inspiron laptop with a Linux Fedora Core 4 operating system. The background image and the animation snapshots are dxf meshes of elastic waveform solutions, rendered on a Windows machine using 3D Studio Max.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Chemical dynamics and rare events in soft matter physicsBoris Fackovec
Talk for the Trinity Math Society Symposium. First summarises the approximations leading from Dirac equation to molecular description and then the synthesis towards non-equilibrium statistical mechanics. The relaxation approach to projection of a molecular system to a Markov jump process is discussed.
Single fault detection and diagnosis technique for digital micro fluidic base...csandit
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-
fluidic based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
SINGLE FAULT DETECTION AND DIAGNOSIS TECHNIQUE FOR DIGITAL MICRO-FLUIDIC BASE...cscpconf
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-fluidic
based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
SINGLE FAULT DETECTION AND DIAGNOSIS TECHNIQUE FOR DIGITAL MICRO-FLUIDIC BASE...csandit
This paper presents an integrated offline testing of Single-Fault detection technique for themicro-
fluidic based biochips and also diagnosis single defects in order to achieve higherthroughput
and less time complexity of the execution process. In the field operation, this is to be used to
increase chip dependability and reusability of the biochips. In this paper, a pipelined technique
is presented to traverse all the edges in much more less time in comparison with some previous
techniques.
ANALYSIS OF ELEMENTARY CELLULAR AUTOMATA BOUNDARY CONDITIONSijcsit
We present the findings of analysis of elementary cellular automata (ECA) boundary conditions. Fixed and variable boundaries are attempted. The outputs of linear feedback shift registers (LFSRs) act as continuous inputs to the two boundaries of a one-dimensional (1-D) Elementary Cellular Automata (ECA) are analyzed and compared. The results show superior randomness features and the output string has passed the Diehard statistical battery of tests. The design has strong correlation immunity and it is inherently amenable for VLSI implementation. Therefore it can be considered to be a good and viable candidate for parallel pseudo random number generation
TOMOGRAPHY OF HUMAN BODY USING EXACT SIMULTANEOUS ITERATIVE RECONSTRUCTION AL...cscpconf
In this paper an Exact Simultaneous Iterative Reconstruction Algorithm is developed and applied on a large semi human size normal biological model and a diseased model (liver region affected) to verify the efficiency of the algorithm. The algorithm is successfully reconstructed the normal model having 15%-20% perturbation i.e. change in permittivity during disease. In diseased case, reconstructed imaginary part of complex permittivity clearly detects the affected zone and it may help the medical diagnosis. Hence it may be a powerful tool for early detection of cancerous tumors as the interrogating wave is a noninvasive one at the ultra high frequency range. The resolution of this system is increased with the reduction of
wavelength by immersing the antenna system and the model in saline water region. The advantage of this algorithm is that the calculation of cofactor are done offline to save the computational time and cofactors are expressed as a function of distances irrespective of their positions
This poster was created in LaTeX on a Dell Inspiron laptop with a Linux Fedora Core 4 operating system. The background image and the animation snapshots are dxf meshes of elastic waveform solutions, rendered on a Windows machine using 3D Studio Max.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
1. Interactive Simulation and
Knowledge Sharing of Active
Soft-Matter Physics Models
• Endre Somogyi Ph.D,
Lecturer, Dept. Computer Science, Indiana University
andy.somogyi@gmail.com, somogyie@indiana.edu
http://mechanica.org
@AndySomogyi
2. • Background
• Modeling and Simulation
• Problem Domain
• Existing Solutions / challenges
• Model Specification / Knowledge Capture / Sharing
• Compiler: transform model specification into
executable model
• Numerical Simulation Environment
Outline
3. • CU School of Medicine — Center for Human Simulation
• Real-Time haptic enabled surgery simulations
• 300 FPS in 2002
Background
4. • Software Engineer / Systems Engineer
• Sequoia Software → Citrix Systems
• Rules Engine for Medical Records
• Galileo International → Cendant → Travelport
• Reverse Engineering
• Customizable Workflows
• Distributed Processing Dependencies
Background
5. Background
Compilation and simulation of biochemical models Speed, Speed, Speed – Just In Time compilation
Linear Scaling
have simple behavior. As the RoadRunner state vector rate is calculated with JIT compil
code, this function could have at most 63 BNE (branch on negative) instructions. The othe
all use interpreters which results in significantly longer run time.
Figure 3: Run time performace relative to system size for the multiple Brusselator
The second set of tests were the models used in the origin SOSLib paper [REF]. The
Table 4 was evaluated by us using the above testing procedure. The lower block is repr
SOSLib web site located at http://www.tbi.univie.ac.at/~raim/odeSolver/doc/benc
This second set of data is strictly here for the sake of completeness and should not be com
the first block, as we have no information on the test procedures or the type of hardware / o
although we suspect that the original SOSLib tests were most likely performed on a MS Wi
Endre Somogyi November 15, 2014
• SBML JIT Compiler
libRoadRunner
• Fastest known chemical
kinetics engine
• Embeddable Library
• Fast / Professional API
• Easy to Integrate
• Virtual Liver
• USC Neuroengineering
• CompuCell3D, etc.
6. Modeling and Simulation
@tx1 =f1(x1, x1, · · · , xn)
@tx2 =f2(x1, x1, · · · , xn)
...
@txn =fn(x1, x1, · · · , xn)
G = U + pV TS
f = m
dv
dt
20
30
40
xHtL
10
20
30
40
50
yHtL
10
20
30
40
zHtL
7. Cellular and Tissue Biology
• Physical System - Physics,
Chemistry, Biology
• Highly Dynamic
• Thermally driven by random noise
• Wiggly, Wobbly, Timey, Wimey
• Everything occurs all at once
Problem Domain
8. • Everything lives in a fluid
environment
• Membrane separates
cells, fluid inside, outside
• Chemical signals diffuse
into and sensed from
environment
• Mechanical connections
with other cells and
environment
• Direct signaling
Cytoplasm
Membrane
Extra-Cellular Medium
Problem Domain
12. partmentalized by these polygonal faces.
To express the multicellular dynamics within aggregates,
an equation for the motion of the ith vertex is introduced by
η
dri
dt
= −
∂U
∂ri
. (1)
The left-hand side of Eq. (1) indicates a frictional force
exerted on the ith vertex, where η is a friction coefficient
and ri is the position vector of the ith vertex. The right-hand
side of Eq. (1) indicates a conservative force acting on the ith
vertex, where U is potential energy that represents a cell’s
potential energy. In addition, cell rearrangements within an
aggregate are expressed by reconnecting local network pat-
terns (Okuda et al. 2012).
2.2 Proliferative cell behaviors
Proliferative cell behaviors are characterized by cell division
and growth (Fig. 1b, c). In particular, cell division behav-
iorsarecharacterizedbythreequantities:timing,intracellular
is expressed by potential energy that is a function of individual cell times
within their respective cell cycles, U(tc
i ). g Two polyhedrons represent-
ing two daughter cells shortly after a single mother cell has divided.
Cell division (increase in cell number after a cell cycle) is represented
by dividing a polyhedron at a dividing plane where a new polygonal
face is introduced (brown area). h Dividing plane. The dividing plane
of the ith cell is normal to the direction of the vector dcell div
i and passes
through the position of the vector ccell div
i
Ucell
tc
i =
cell
j
ucell
j rk, tc
j δ∗
j , (3)
Ucell–cell
tc
i =
cell
j
cell
k
ucell–cell
jk rl, tc
j , tc
k δ∗
j δ∗
k , (4)
Ucell–ext
tc
i =
cell
j
ucell–ext
j rk, tc
j δ∗
j , (5)
where δ∗
j = δ⌊tc
j /τcell div
j ⌋0. The functions δαβ and ⌊. . .⌋ indi-
cate Kronecker’s delta and floor functions, respectively. In
Eqs. (3), (4), and (5), cell
indicates summations for all
cells. Potential energy ucell
j represents some energy of the
jth cell, such as volume elasticity, surface elasticity, api-
cal constriction, and other effects of intracellular structures
and activities. The potential energy ucell–cell
jk indicates some
energy between the jth and kth cells, such as cell–cell adhe-
sions at intercellular junctions. The potential energy ucell–ext
j
indicates some energy between the jth cell and extracellular
components, such as extracellular matrixes, basement mem-
branes, and solvent liquids. Assuming that a cell’s potential
Modeling cell proliferation 989
g h
di
cell div
ci
cell div
Network
Polyhedron
Vertex
Edge
Polygonal face
Aggregate Cell
a
d
Cell cell boundaries
Cell division
Polyhedron division
Cell growth
Potential energy as a function of
f
b c
e
Dividing plane
&
&
individual cell times,U(ti
)c
Fig. 1 Modeling cell proliferation based on a RNR framework. a
Cell aggregate in which cells are tightly packed and adhere at cell–
cell boundaries. b Single cell embedded within an aggregate. c Two
deforming daughter cells accompanied by cell growth throughout the
cell cycle. d Network representing an aggregate in a RNR model frame-
work. The network comprises vertices and edges (solid lines). Cells are
compartmentalized by polygonal faces (gray area) that represent cell–
cell boundaries. e Single polyhedron representing a single cell. f Two
polyhedrons. Cell growth (increase in cell volume during the cell cycle)
is expressed by potential energy that is a function of individual cell times
within their respective cell cycles, U(tc
i ). g Two polyhedrons represent-
ing two daughter cells shortly after a single mother cell has divided.
Cell division (increase in cell number after a cell cycle) is represented
by dividing a polyhedron at a dividing plane where a new polygonal
face is introduced (brown area). h Dividing plane. The dividing plane
of the ith cell is normal to the direction of the vector dcell div
i and passes
through the position of the vector ccell div
i
First, for topological modeling of the cell division process,
each polyhedron is divided at a single plane, hereafter called
dividing plane (Fig. 1g), and a new polygonal face is intro-
duced on the dividing plane inside the polyhedron. The divid-
ing plane is defined as passing through a position vector,
ccell div
i , and is normal to a direction vector, dcell div
i (Fig. 1h).
Here, the position vector ccell div
i is defined as being inside of
the ith cell volume, and the direction vector dcell div
i is defined
as a unit vector normal to the dividing plane.
Under procedure for dividing a polyhedron, the dividing
plane intersects the polyhedron at several edges (Fig. 1g).
These edges are sequentially linked as a ring-like strand on
the plane and compose a single polygon, which is defined as a
new polygonal face. This polygonal face separates the poly-
hedron into two polyhedrons, which are defined as daughter
cells.
Ucell
tc
i =
cell
j
ucell
j rk, tc
j δ∗
j , (3)
Ucell–cell
tc
i =
cell
j
cell
k
ucell–cell
jk rl, tc
j , tc
k δ∗
j δ∗
k , (4)
Ucell–ext
tc
i =
cell
j
ucell–ext
j rk, tc
j δ∗
j , (5)
where δ∗
j = δ⌊tc
j /τcell div
j ⌋0. The functions δαβ and ⌊. . .⌋ indi-
cate Kronecker’s delta and floor functions, respectively. In
Eqs. (3), (4), and (5), cell
indicates summations for all
cells. Potential energy ucell
j represents some energy of the
jth cell, such as volume elasticity, surface elasticity, api-
Time (t)
Thenumberofcells(nc
)
a
t = 2827
(nc
= 500)
t = 2827
(nc
= 500)
t = 2224
(nc
= 250)
b
t = 0
(nc
= 18)
0
250
500
18
τ
ave=
37.5
c
τ
ave=
75
c
τ
ave=
150
c
τ
ave=
300
c
τ
ave=
600
c
30001500
Local regulation
t = 2224
(nc
= 250)
t = 0
(nc
= 18)
y
z
x
y
z
x
Time (t)
Thenumberofcells(nc
)
0
250
500
18
τ
ave=
37.5
c
τ
ave=
75
c
τ
ave=
150
c
τ
ave=
300
c
τ
ave=
600
c
Global regulation
30001500
Multicellular Mechanical Relations: Satoru Okuda
13. • SBML works well for CWSC — shareable, but no
geometry or mechanics.
• Molecular Dynamics works well for atomistic —
sharable, but not multi-scale, too small.
• Comsol / SolidWorks works well for continuum, but
no structural rearrangement, too large.
• We add spatial awareness, dynamic structure,
connectivity, and physical conservation laws.
Existing Approaches
14. • MATLAB hard-coded simulations. Difficult to extract
original meaning. Virtually impossible to run with
different computational back-ends. Difficult to
parallelize
• Example: “Mechanically-coupled Reaction-Diffusion
model of Glioma Growth” Abler, Büchler, Universität
Bern
Current State of the Art
MATLAB, Cellular
Automata
COMSOL,
Finite Element,
Reaction/Diffusion
Files
Files
15. • Made great strides in data standards: PDB/PSF, microscopy, MultiCellDS,
Mesh
• Dynamics is hard: SBML is one of few ways of exchanging dynamics.
• Spatial models nearly always written in custom code. Usually by grad students
with no training in software engineering.
• Large time cost for custom coding.
• Results in single-use models that are not sharable, nearly always poor
performance (physical domain does not map cleanly to computational domain).
• Low level language ≠ high performance (usually worse in the hands of the
untrained)
• Knowledge is lost when student leaves
• Writability, Shareability, Modularity, Runnability, Interoperability
Problems with existing approaches
16. Mechanica is NOT
• A theorem proving language (Sorry five-color problem)
• A magic, automatically parallelizing programming language
• A graphical user interface toolkit
• A language for numerical linear algebra (Fortran, MATLAB,
Julia, etc…)
• A language for symbolic computer algebra (Mathematica,
Maxima, etc…)
• A Discrete Event Simulation language or environment
• A General Universal PDE solver
17. The Mechanica System
Mechanica System
View
Model
AST
ControllerPropagator
n:nucleus , b:body)
dist(n,b) < 6)
(dist(n,b) 5)};
pfrc(a:Particle , b:Particle)
dist(a,b) < 2)
(dist(a,b) 1) };
ody, body);
ody, surface);
dX
dt
= f(X, t) + ⇠(t), x(t0) = X0, X 2 RN
(t) = eiLt
(0)
solvent.A) > (b:solvent.A)
(dist(a,b) < 5) { k ⇤ (a b)};
Red.A) > (b:Red.A)
link pfrc(a:Particle , b:Particle)
if (dist(a,b) < 2)
{ k ⇤ (dist(a,b) 1) };
pfrc(body, body);
pfrc(body, surface);
}
dX
dt
= f(X, t) + ⇠(t), x(t0) = X0, X 2 RN
(t) = eiLt
(0)
proc (a:solvent.A) > (b:solvent.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
proc (a:Red.A) > (b:Red.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
proc (a:solvent.A) > (b:Red.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
Mechanica
Models
(Source Code)
Compiler
persistance
18. Model Loading (Compiler)
JScript
like syntax
source
Python like
syntax
source
Abstract
Syntax
Tree (AST)
JScript
like parser
JScript like
generator
Python like
parser /
generator
Semantic Analyzer
• Analyzes the mathematical and network
structure of the AST.
• Performs transformations on the AST.
• Transforms rules into systems of
equations and events
• Transforms the declarative, rule-based
input source into a procedural, C-like
output.
Code Generator
• Transforms procedural
data definitions into
machine-specific data
structures and
executable code.
Compiled
Model
External Code Generator
• Generates C/C++ code for
different physics engines
• (PhysiCell / BioFVM)
SBML
SBML
importer /
generator
Data Sources
(MultiCellDS)
Data
importer /
generator
19. Formalizing Physical Knowledge
• We observe that that everything occurring in the
physical world can naturally be described in terms of
objects and processes
• Objects are the ‘things’, such as molecules, proteins,
membranes, cells, fluids or materials
• Processes are what makes objects change,
processes act on objects and cause changes in state.
• processes can operate concurrently and
continuously, or (very rarely) can operate discretely
in response to an event.
20. Objects and Processes
• Chemical Reaction or Transport processes act on
chemicals - reactants are consumed and products
are produced
• Membrane Transport (active or passive) processes
acts on chemicals
• Cell Division or Death processes act on cells
• Growth, Adhesion or Motion processes act on cells
• Diffusion processes act on chemicals
21. • Build Mechanistic models of physical phenomena
• Based on Physics, Chemistry and Biology rather than
traditional machine abstractions or models of
computation (lambda calculus)
• Computational concepts are hard to teach, harder than
physical or chemical concepts
• Von-Neuman architecture is not close to physical reality
• Describe the world in terms of physical objects and
processes
Mechanica Modeling Language
22. Mechanica Modeling Language
• Start with the basic syntax of TypeScript (strongly typed
JavaScript)
• Familiar to most users, statically typed, easy to parse and extend.
• Simplify, eliminate unnecessary features to ensure a simple AST
which is easy to modify
• Side-effect free. Only the runtime changes state.
• Simple: Facilitate model interchange, designed to be extended,
think of it as Physics Assembler. Language spec is 5 pages. (C++
is ~750)
• Extensible: Homoiconic, incorporates Lisp’s macro system to build
higher level concepts
23. Particles
• Particles can be simple, complex, large, small, carry field
variables
• Based on tDPD, verified to be the fastest computational
fluid dynamics scheme, O(n)
• Rigorously verified to reproduce Navier-Stokes
24. Chemical Reactions
ning a functional language is because we would like to
and proven theory, in this case the lambda calculus
A
k1A
! X
X + 2 Y
k2XY 2
! 3 X
B + X
k3BX
! Y + D
X
k4X
! E
) { k1 ⇤ A}
> (3 X) { k2 ⇤ X ⇤ Y ⇤⇤2}
(Y, D) { k3 ⇤ B ⇤ X}
) { k4 ⇤ X}
= S · ⌫
2
6
1 0 0 1 0 0
0 0 0 0 1 2
3
7
2
6
6
6
⌫A
⌫B
⌫D
3
7
7
7
The rationale for designing a functional language is because w
base it on a well known and proven theory, in this case the lamb
A
k1A
! X
X + 2 Y
k2XY 2
! 3 X
B + X
k3BX
! Y + D
X
k4X
! E
proc (A) > (X) { k1 ⇤ A}
proc (X, 2 Y) > (3 X) { k2 ⇤ X ⇤ Y ⇤ ⇤ 2}
proc (B, X) > (Y, D) { k3 ⇤ B ⇤ X}
proc (X) > (E) { k4 ⇤ X}
dX
dt
= S · ⌫
2
6
6
6
6
6
6
4
dA/dt
dB/dt
dD/dt
dE/dt
dX/dt
dY/dt
3
7
7
7
7
7
7
5
=
2
6
6
4
1 0 0 1 0 0
0 0 0 0 1 2
0 1 1 1 1 1
0 0 0 1 1 0
3
7
7
5 ·
2
6
6
6
6
6
6
4
⌫A
⌫B
⌫D
⌫E
⌫X
⌫Y
force (r:Red, b:Blue) { k ⇤ dist(r,b)}
m to point
entally important biological processes. A chemical reaction process in
and produces a set of products, in effect, the process describes the trans-
a set of products. Modeling chemical reactions is one of the most common
processes. Transformation process syntax is similar to the conventional
based on Microsoft Typescript type specification. For example, to add a
atial region, one would simply write a set of transformation processes in
ansformation processes begin with the proc keyword, have an an optional
roducts and the reaction rate expression as in Fig. 1. If the spatial region
2
6
6
6
6
6
6
4
dA/dt
dB/dt
dD/dt
dE/dt
dX/dt
dY/dt
3
7
7
7
7
7
7
5
=
2
6
6
6
6
6
6
4
1 0 0 0
0 0 1 0
0 0 1 0
0 0 0 1
1 1 1 1
0 2 1 0
3
7
7
7
7
7
7
5
·
2
6
6
4
n1
n2
n3
n4
3
7
7
5
25. Generalized Transformation Process
• Generalize the concept of “chemical reactions” to
enable transformation of discrete things.
• Same syntax as continuous substrates, except when the
inputs are discrete, the process body is interpreted as a
probability
• Enables us to model stochastic reactions and stochastic
cellular automata
pfrc(body, body);
pfrc(body, surface);
}
dX
dt
= f(X, t) + ⇠(t), x(t0) = X0, X 2 RN
(t) = eiLt
(0)
proc (a:solvent.A) > (b:solvent.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
proc (a:Red.A) > (b:Red.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
proc (a:solvent.A) > (b:Red.A)
when (dist(a,b) < 5) { k ⇤ (a b)};
proc my reaction(a:integer , b:integer) >
(c:integer) { exp( k ⇤ (a⇤ b)/T)}
force(a:nucleus , b:surface) {
k ⇤ (
26. Fields are represented as attributes on particles
• Extremely Simple Syntactically
• Compiler automatically generates internal data
structures
• Completely self-consistent for solids, melts, liquids
• Get body forces, diffusion, transport for free
• No re-meshing, ever!
29. Dynamic Changes In Morphology
• Forces are one of the fundamental concepts in
physical systems
30. Polymeric fluid:Polymeric fluid: mesoscopicmesoscopic mm
exc
cor
pre
finit
Litvinov et al., Phys.Rev. E 77, 66703 (2008)
Forces
• Mass-Spring systems have been in molecular dynamics,
games for years
• Difficult to program and usually static connectivity
31. Forces
• Same suite of links as MD: bond, angle, dihedral…
• Working on surface links: bending, shear…
6
6
6
4
dE/dt
dX/dt
dY/dt
7
7
7
5
= 6
4 0 1 1 1 1 1
0 0 0 1 1 0
7
5
orce (r:Red, b:Blue) { k ⇤ dist(r
orce (a:Red, b:Red) { k2 ⇤ dist(a
F = ma
dv
dt
=
1
m
F
dx
dt
=
1
v ⇠(t)
dX
dt
= S · ⌫
2
6
6
6
6
6
6
4
dA/dt
dB/dt
dD/dt
dE/dt
dX/dt
dY/dt
3
7
7
7
7
7
7
5
=
2
6
6
4
1 0 0 0 1 0
0 0 0 0 1 2
0 1 1 0 1 1
0 0 0 1 1 0
3
7
7
5 ·
2
6
6
6
6
6
6
4
⌫A
⌫B
⌫D
⌫E
⌫X
⌫Y
3
7
7
7
7
7
7
5
link (r:Red, b:Blue) { k ⇤ dist(r,b)}
link (a:Red, b:Red) { k2 ⇤ dist(a,b)}
F = ma
dv
dt
=
1
m
F
dx
dt
=
1
v ⇠(t)
32. Forces
• Forces can be defined between individual
instances (one to one), individual instances and
collections (one to many), or collections and
collections (many to many).
• The type specifier determines how forces are
applied.
• Links may have ‘if’, ‘when’, or ‘while’
clauses, these define cut-off, activation and
deactivation conditions. May be asymmetric.
33. Links: bonded forces
type MyCell : SpatialRegion {
nucleus : Particle { radius:2};
surface : Sphere { radius:2; resolution :1};
body : fill(type:Particle { radius:0.3} , density:20);
link (n:nucleus , b:body)
if (dist(n,b) < 6)
{ k ⇤ (dist(n,b) 5)};
link pfrc(a:Particle , b:Particle)
if (dist(a,b) < 2)
{ k ⇤ (dist(a,b) 1) };
pfrc(body, body);
pfrc(body, surface);
}
dX
= f(X, t) + ⇠(t), x(t0) = X0, X 2 RN
34. Connecting Physics and Chemistry
• link body definitions can contain arbitrary, user specified
functional forms.
• link (and reaction rate) bodies can reference local
chemical concentrations at the location of the link instance
Rh
RhRh
Rh
Rh
Rh
Rh
Rh
Rh
dt
= S · ⌫
2
6
6
6
6
6
6
4
dA/dt
dB/dt
dD/dt
dE/dt
dX/dt
dY/dt
3
7
7
7
7
7
7
5
=
2
6
6
4
1 0 0 0 1 0
0 0 0 0 1 2
0 1 1 0 1 1
0 0 0 1 1 0
3
7
7
5 ·
2
6
6
6
6
6
6
4
⌫A
⌫B
⌫D
⌫E
⌫X
⌫Y
3
7
7
7
7
7
7
5
link (r:Red, b:Blue) { k ⇤ dist(r,b)}
link (a:Red, b:Red) { k2 ⇤ dist(a,b)}
link(a:mycell.nucleus , b:mycell.surface) {
k ⇤ (dist(a,b) restLength + k2 ⇤ Rho);
}
F = ma
dv
dt
=
1
m
F
dx
dt
=
1
v ⇠(t) Spatial Scoping
35. Cell Motion with Forces
• Combine Previous Ideas
• Spatial Scoping
force pfrc(a:Point, b:Point)
if (dist(a,b) < 2)
{ k ⇤ (dist(a,b) 1) };
pfrc(body, body);
pfrc(body, surface);
}
dX
dt
= f(X, t) + ⇠(t), x(t0) = X0, X 2 R
force(a:nucleus , b:surface) {
k ⇤ (
dist(a,b)
(restLength + b.ChemicalSignal)
)
}
36. Time Propagation
ous state vector C(t). Each process definition body can access the present
icle attributes, and constant parameters to calculate the process transforma-
processes defines a system of ordinary differential equations (ODEs) in the
d
dt
C =
˙Cf
˙Cr
=
N·n(C,r,v,p)
f(C,r,v,p)
. (1)
partitions: Cf is the vector of independent continuous variables (which par-
ocesses, i.e., a reaction network), and Cr is a vector of variables which are
vector p consists of time independent parameters. The reaction network of
ansformation processes defines the m⇥n stoichiometry matrix N. Each stoi-
et number of continuous variables i produced or consumed in transformation
ation process definitions combine to form the transformation rate function n.
tor, Cr, is a set of variables which form a system of conventional ODEs, i.e.
defined by a rate process. Only the Mechanica runtime can directly change
is the sum of the linked, FL, conservative, FC, random, FR, dissipative, FD,
nked force is the sum of the linked relationships between the particles. The
raction which ensures volume exclusion and keeps materials from interpene-
presents the effects of viscosity, and the random force represents the effects
rces. The linked force is the sum of the linked relationships between the pa
s a soft interaction which ensures volume exclusion and keeps materials from
ive force represents the effects of viscosity, and the random force represent
ons. Integration time steps can be large because these inter-particle forces
eractions. More details on the conservative, random and dissipative forces c
The time evolution of particle positions, r and velocities, v is defined as
d2ri
dt2
=
dvi
dt
=
1
mi
Fi =
1
mi
Â
i6=j
FL
ij +FC
ij +FD
ij +FR
ij +Fext
i
!
.
tes the time evolution of the model via time slicing – time is partitioned int
Each time step has three phases: (A) evaluate and apply the when and whil
esses determine if they should be triggered, and apply them. Links are a
1 according to their when and while predicates. (B) integrate the cont
ording to eqn. 1 and (C) integrate the particle positions according to eqn. 2.
Implemented in mdcore, Mechanica’s stand-alone
particle dynamics engine. Combined with CVODE.
37. Neighbors
Update when particle
displacement (rskin - rcut)/2
• Force calculations are very expensive
• Without Optimization, would be O(N2)
• Cell List• Verlet List
Most efficient is to use
both
40. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using Gauss-
Seidel method
Rest position
41. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using Gauss-
Seidel method
The particles are displaced by external forces
42. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using Gauss-
Seidel method
The constraints are solved sequentially
43. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using
Gauss-Seidel method
The constraints are solved sequentially
44. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using
Gauss-Seidel method
The constraints are solved sequentially
45. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using
Gauss-Seidel method
The constraints are solved sequentially
46. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using
Gauss-Seidel method
The constraints are solved sequentially
47. Constraints
• Gauss Seidel Method
• Fast to converge, but serial
Example: Distance
constraint
Approximate solution to a system of constraints using
Gauss-Seidel method
Iteration 1
48. Constraints
• Jacobi Method easier to parallelize, but converges
slower
Example: Distance
constraint
Approximate solution to a system of constraints using
Jacobi method
The constraints are solved in parallel
49. Constraints
• Jacobi Iteration 1
Example: Distance
constraint
Approximate solution to a system of constraints using
Jacobi method
Iteration 1
50. Constraints
• Jacobi Iteration 2
Example: Distance
constraint
Approximate solution to a system of constraints using
Jacobi method
Iteration 2
51. Constraints
• Jacobi Iteration 3
Example: Distance
constraint
Approximate solution to a system of constraints using
Jacobi method
Iteration 3
52. Constraints
• Jacobi Iteration 4
Example: Distance
constraint
Approximate solution to a system of constraints using
Jacobi method
Iteration 4
54. Cellular Automata
• Seamlessly integrate particle
systems and cellular automata
• Use Mechanica’s Python
binding to integrate with
existing CC3D scripts
• Automatically update AABB
tree as CA voxels change
• As CA boundary changes,
automatically updates
constraints on particles
• Momentum Conserving
55. Application Interoperability
• Core design goal
• Interoperability with existing
applications
• Same binary object layout as Python
— auto-boxing
• zero-overhead Python binding
• Python script can import a
Mechanica model just like any other
Python module
• Integrate into CompuCell3D models
with zero CompuCell3D changes
• C API — cross language compatible
56. Collaborations
• Priyom Adhyapok (IU): CompuCell3D integration,
somite formation.
• Mike Hosek (IU): erythrocyte cortical dynamics
• Jean-Marie Bouteiller (USC): Synaptic junction
reaction-transport
• Amit Hagar (IU): Liver carcinogenesis and toxicology
risk assessment, CompuCell3D integration, reaction/
transport of carcinogen in a liver sinusoid
57. Outlook
• Find tenure-track job (or funded post-doc), continue
to develop and apply Mechanica.
• Secure Funding (NIH, NSF, …)
• Applications / Collaborators (Experimental
Biologists)
• Different backends (GPU, MPI, …)
• Multi-scale factorization — combine micro and
mesoscale time evolution
• Visual Model Editing
58. Acknowledgements and Disclosure
• A number of concepts presented here are patent
pending.
• All code will be freely licensed under Common
Development and Distribution License (CDDL)
• We acknowledge generous financial support the
National Institutes of Health, National Institute of
General Medical Sciences, grants R01 GM076692
and R01 GM077138.
59. Conclusions
• Modeling description language that naturally
expresses physical constructs and captures
biological knowledge.
• Built on physical, chemical and biological
constructs.
• Enables biologists to model systems using natural
concepts.
• somogyie@indiana.edu
• http://mechanica.org