-
Notifications
You must be signed in to change notification settings - Fork 35
/
publications.html
490 lines (453 loc) · 35.1 KB
/
publications.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Publications</title>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Scientific publications that have come out of OpenWorm research.">
<!-- REPLICATE THIS IN ALL PAGES -->
<!-- Le styles -->
<link href="css/bootstrap.css" rel="stylesheet">
<link rel="stylesheet" href="css/font-awesome.css">
<link href="css/bootstrap-responsive.css" rel="stylesheet">
<link href="css/main.css" rel="stylesheet">
<link href="css/docs.css" rel="stylesheet">
<!-- HTML5 shim, for IE6-8 support of HTML5 elements -->
<!--[if lt IE 9]>
<script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script>
<![endif]-->
<!-- Fav and touch icons - this code is outdated -->
<link rel="shortcut icon" href="favicon.ico">
<link rel="apple-touch-icon-precomposed" sizes="144x144" href="../assets/ico/apple-touch-icon-144-precomposed.png">
<link rel="apple-touch-icon-precomposed" sizes="114x114" href="../assets/ico/apple-touch-icon-114-precomposed.png">
<link rel="apple-touch-icon-precomposed" sizes="72x72" href="../assets/ico/apple-touch-icon-72-precomposed.png">
<link rel="apple-touch-icon-precomposed" href="../assets/ico/apple-touch-icon-57-precomposed.png">
<div class="navbar navbar-inverse navbar-fixed-top">
<div class="navbar-inner" id="head-nav">
</div>
</div>
<script src="js/jquery-3.6.0.js"></script>
<script>
// get head-nav and foot-nav with jquery (get is async)
$.get('header-content.html', function(data) {
$('#head-nav').html(data);
})
$.get('footer-content.html', function(data) {
$('#foot-nav').html(data);
})
</script>
</head>
<body>
<div id="pjax-content">
<!-- END REPEAT-->
<header class="jumbotron subhead" id="overview">
<div class="container">
<h1>Publications</h1>
<p class="lead">
A list of OpenWorm scientific publications.
</p>
</div>
</header>
<div class="container">
<div class="marketing">
<br/>
<i class="fa fa-book fa-xl"></i>
<div class="large-spacer"></div>
<div class="large-spacer"></div>
<div>
<h2>Periodicity in the Embryo: emergence of order in space, diffusion of order in time</h2>
<div class="clearer"></div>
<p>
6 January 2021, <i>Biosystems</i>, 204, 104405. doi:10.1016/j.biosystems.2021.104405
</p>
<p class="authors">
Bradly Alicea, Jesse Parent, Ujjwal Singh
</p>
<p>
Does embryonic development exhibit characteristic temporal features? This is apparent in evolution, where evolutionary change has been shown to occur in bursts of activity. Using two animal models (Nematode, Caenorhabditis elegans and Zebrafish, Danio rerio) and simulated data, we demonstrate that temporal heterogeneity exists in embryogenesis at the cellular level, and may have functional consequences. Cell proliferation and division from cell tracking data is subject to analysis to characterize specific features in each model species. Simulated data is then used to understand what role this variation might play in producing phenotypic variation in the adult phenotype. This goes beyond a molecular characterization of developmental regulation to provide a quantitative result at the phenotypic scale of complexity.
<br/>
<a href="https://www.sciencedirect.com/science/article/pii/S0303264721000629" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Data-theoretical Synthesis of the Early Developmental Process</h2>
<div class="clearer"></div>
<p>
22 December 2020, <i>Neuroinformatics</i>, doi:10.1007/s12021-020-09508-1.
</p>
<p class="authors">
Bradly Alicea, Richard Gordon, Thomas E. Portegys
</p>
<p>
Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system <i>Caenorhabditis elegans</i>, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 min post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.
<br/>
<a href="https://link.springer.com/epdf/10.1007/s12021-020-09508-1?sharing_token=SfbCril4XIxa5dgqd7LItve4RwlQNchNByi7wbcMAY77bKFVCUdSZ0ZaLpXFrJ62d0wsNV70TpSD7PMHFW2pjlOQt8qjlwvfr5BFDdTMwhzQj9AbfORflfPVm45t-7xEoVn2UKvEnNnJSZYmt67DEQlxSUqkL4lxeCDFwUJpVYA%3D" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Raising the Connectome: the emergence of neuronal activity and behavior in <i>C. elegans</i></h2>
<div class="clearer"></div>
<p>
15 September 2020, <i>Frontiers in Cellular Neuroscience</i>, 14, 524791. doi:10.3389/fncel.2020.524791
</p>
<p class="authors">
Bradly Alicea
</p>
<p>
The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of <i>Caenorhabditis elegans</i> will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.
<br/>
<a href="https://www.frontiersin.org/articles/10.3389/fncel.2020.524791/full" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>The Emergent Connectome in <i>Caenorhabditis elegans</i> Embryogenesis</h2>
<div class="clearer"></div>
<p>
25 September 2018, <i>Biosystems</i>, 173, 247-255. doi:10.1016/j.biosystems.2018.09.016
</p>
<p class="authors">
Bradly Alicea, DevoWorm Group
</p>
<p>
The relatively new field of connectomics provides us with a unique window into nervous system function. In the model organism <i>Caenorhabditis elegans</i>, this promise is even greater due to the relatively small number of cells (302) in its nervous system. While the adult <i>C. elegans</i> connectome has been characterized, the emergence of these networks in development has yet to be established. In this paper, we approach this problem using secondary data describing the birth times of terminally-differentiated cells as they appear in the embryo and a connectomics model for nervous system cells in the adult hermaphrodite. By combining these two sources of data, we can better understand patterns that emerge in an incipient connectome. This includes identifying at what point in embryogenesis the cells of a connectome first comes into being, potentially observing some of the earliest neuron-neuron interactions, and making comparisons between the formally-defined connectome and developmental cell lineages. An analysis is also conducted to root terminally-differentiated cells in their developmental cell lineage precursors. This reveals subnetworks with different properties at 300 min of embryogenesis. Additional investigations reveal the spatial position of neuronal cells born during pre-hatch development, both within and outside the connectome model, in the context of all developmental cells in the embryo. Overall, these analyses reveal important information about the birth order of specific cells in the connectome, key building blocks of global connectivity, and how these structures correspond to key events in early development.
<br/>
<a href="https://www.sciencedirect.com/science/article/pii/S0303264718302120" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Cell differentiation processes as spatial networks: Identifying four-dimensional structure in embryogenesis</h2>
<div class="clearer"></div>
<p>
20 September 2018, <i>Biosystems</i>, 173, 235-246. doi:10.1016/j.biosystems.2018.09.009
</p>
<p class="authors">
Bradly Alicea, Richard Gordon
</p>
<p>
One overarching principle of eukaroytic development is the generative spatial emergence and self-organization of cell populations. As cells divide and differentiate, they and their descendents form a spatiotemporal explicit and increasingly compartmentalized complex system. Yet despite this comparmentalization, there is selective functional overlap between these structural components. While contemporary tools such as lineage trees and molecular signaling networks prvide a window into this complexity, they do not characterize embryogenesis as a global process. Using a four-dimensional spatial representation, major features of the developmental process are revealed. To establish the role of developmental mechanisms that turn a spherical embryo into a highly asymmetrical adult phenotype, we can map the outcomes of the cell division process to a complex network model. This representational model provides information about the top-down mechanisms relevant to the differentiation process. In a complementary manner, looking for phenomena such as superdiffusive positioning and sublineage-based anatomical clustering incorporates dynamic information to our parallel view of embryogenesis. Characterizing the spatial organization and geometry of embryos in this way allows for novel indicators of developmental patterns both within and between organisms.
<br/>
<a href="https://www.sciencedirect.com/science/article/pii/S030326471830220X" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>OpenWorm: overview and recent advances in integrative biological simulation of <i>Caenorhabditis elegans</i></h2>
<div class="clearer"></div>
<p>
10 September 2018, <i>Phil. Trans. R. Soc. B</i>, DOI: 10.1098/rstb.2017.0382
</p>
<p class="authors">
Gopal P. Sarma, Chee Wai Lee, Tom Portegys, Vahid Ghayoomie, Travis Jacobs, Bradly Alicea, Matteo Cantarelli, Michael Currie, Richard C. Gerkin, Shane Gingell, Padraig Gleeson, Richard Gordon, Ramin M. Hasani, Giovanni Idili, Sergey Khayrulin, David Lung, Andrey Palyanov, Mark Watts and Stephen D. Larson </p>
<p>
The adoption of powerful software tools and computational methods from the software industry by the scientific research
community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve
the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm
Foundation is an independent research organization working towards an integrative simulation of the nematode <i>Caenorhabditis
elegans</i>, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental
biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent
sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future
directions.
<br/>
<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0382" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Towards systematic, data-driven validation of a collaborative, multi-scale model of <i>Caenorhabditis elegans</i></h2>
<div class="clearer"></div>
<p>
10 September 2018, <i>Phil. Trans. R. Soc. B</i>, DOI: 10.1098/rstb.2017.0381
</p>
<p class="authors">
Richard C. Gerkin, Russell J. Jarvis and Sharon M. Crook
</p>
<p>
The OpenWorm Project is an international open-source collaboration to create a multi-scale model of the organism
<i>Caenorhabditis elegans</i>. At each scale, including subcellular, cellular, network and behaviour, this project employs
one or more computational models that aim to recapitulate the corresponding biological system at that scale.
This requires that the simulated behaviour of each model be compared with experimental data both as the model is
continuously refined and as new experimental data become available. Here we report the use of SciUnit, a software framework
for model validation, to attempt to achieve these goals. During project development, each model is continuously subjected to
data-driven ‘unit tests’ that quantitatively summarize model-data agreement, identifying modelling progress and highlighting
particular aspects of each model that fail to adequately reproduce known features of the biological organism and its components.
This workflow is publicly visible via both GitHub and a web application and accepts community contributions to ensure that modelling
goals are transparent and well-informed.
<br/>
<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0381" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Geppetto: a reusable modular open platform for exploring neuroscience data and models</h2>
<div class="clearer"></div>
<p>
10 September 2018, <i>Phil. Trans. R. Soc. B</i>, DOI: 10.1098/rstb.2017.0380
</p>
<p class="authors">
Matteo Cantarelli, Boris Marin, Adrian Quintana, Matt Earnshaw, Robert Court, Padraig Gleeson, Salvador Dura-Bernal, R. Angus Silver and Giovanni Idili
</p>
<p>
Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools
for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications,
including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize
computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for <i>Drosophila
melanogaster</i> neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data.
Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a
Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent
their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way.
The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly
customizable frontend.
<br/>
<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0380" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>c302: a multiscale framework for modelling the nervous system of <i>Caenorhabditis elegans</i></h2>
<div class="clearer"></div>
<p>
10 September 2018, <i>Phil. Trans. R. Soc. B</i>, doi:10.1098/rstb.2017.0379
</p>
<p class="authors">
Padraig Gleeson, David Lung, Radu Grosu, Ramin Hasani and Stephen D. Larson
</p>
<p>
The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode <i>Caenorhabditis elegans</i>.
A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate
level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined.
For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating
varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools
developed in the OpenWorm modelling toolchain.
<br/>
<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0379" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Three-dimensional simulation of the <i>Caenorhabditis elegans</i> body and muscle cells in liquid and gel environments for behavioural analysis</h2>
<div class="clearer"></div>
<p>
10 September 2018, <i>Phil. Trans. R. Soc. B</i>, doi:10.1098/rstb.2017.0376
</p>
<p class="authors">
Andrey Palyanov, Sergey Khayrulin and Stephen D. Larson
</p>
<p>
To better understand how a nervous system controls the movements of an organism, we have created a three-dimensional computational
biomechanical model of the <i>Caenorhabditis elegans</i> body based on real anatomical structure. The body model is created with a particle
system–based simulation engine known as Sibernetic, which implements the smoothed particle–hydrodynamics algorithm. The model includes
an elastic body-wall cuticle subject to hydrostatic pressure. This cuticle is then driven by body-wall muscle cells that contract and
relax, whose positions and shape are mapped from <i>C. elegans anatomy</i>, and determined from light microscopy and electron micrograph data.
We show that by using different muscle activation patterns, this model is capable of producing C. elegans-like behaviours,
including crawling and swimming locomotion in environments with different viscosities, while fitting multiple additional known biomechanical
properties of the animal.
<br/>
<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0376" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Unit Testing, Model Validation, and Biological Simulation</h2>
<div class="clearer"></div>
<p>
August 10 2016, <i>F1000Research</i>, 5:1946 (2016), DOI: 10.12688/f1000research.9315.1
</p>
<p class="authors">
Gopal P. Sarma, Travis W. Jacobs, Mark D. Watts, S. Vahid Ghayoomie, Stephen D. Larson, and Rick C. Gerkin
</p>
<p>
The growth of the software industry has gone hand in hand with the development of tools
and cultural practices for ensuring the reliability of complex pieces of software.
These tools and practices are now acknowledged to be essential to the management of
modern software. As computational models and methods have become increasingly common
in the biological sciences, it is important to examine how these practices can accelerate
biological software development and improve research quality. In this article, we give a
focused case study of our experience with the practices of unit testing and test-driven
development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans.
We identify and discuss the challenges of incorporating test-driven development into a
heterogeneous, data-driven project, as well as the role of model validation tests, a
category of tests unique to software which expresses scientific models.
<br/>
<a href="https://f1000research.com/articles/5-1946/v1" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development Via Differentiation Trees of Embryos</h2>
<div class="clearer"></div>
<p>
18 August 2016, <i>Biology</i>, 5(3), 33. doi:10.3390/biology5030033
</p>
<p class="authors">
Bradly Alicea and Richard Gordon
</p>
<p>
Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of <i>Ciona intestinalis</i> and <i>Caenorhabditis elegans</i>. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.
<br/>
<a href="https://www.mdpi.com/2079-7737/5/3/33" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Application of smoothed particle hydrodynamics to modeling mechanisms of biological tissue</h2>
<div class="clearer"></div>
<p>
March 08 2016, <i>Adv. Eng. Software</i>, DOI: 10.1016/j.advengsoft.2016.03.002
</p>
<p class="authors">
Andrey Palyanov, Sergey Khayrulin, and Stephen Larson
</p>
<p>
A prerequisite for simulating the biophysics of complex biological tissues and whole organisms are computational descriptions of biological matter that are flexible and can interface with materials of different viscosities, such as liquid. The landscape of software that is easily available to do such work is limited and lacks essential features necessary for combining elastic matter with simulations of liquids. Here we present an open source software package called Sibernetic, designed for the physical simulation of biomechanical matter (membranes, elastic matter, contractile matter) and environments (liquids, solids and elastic matter with variable physical properties). At its core, Sibernetic is built as an extension to Predictive–Corrective Incompressible Smoothed Particle Hydrodynamics (PCISPH). Sibernetic is built on top of OpenCL, making it possible to run simulations on CPUs or GPUs, and has 3D visualization
support built on top of OpenGL. Several test examples of the software running and reproducing physical experiments, as well as performance benchmarks, are presented and future directions are discussed.
<br/>
<a href="https://drive.google.com/file/d/0B_t3mQaA-HaMWEJ4aG5fVm9QUWc/view" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>The OpenWorm Project: currently available resources and future plans</h2>
<div class="clearer"></div>
<p>
July 18 2015, <i>BMC Neuroscience</i>, DOI: 10.1186/1471-2202-16-S1-P141
</p>
<p class="authors">
Padraig Gleeson, Matteo Cantarelli, Michael Currie, Jim Hokanson, Giovanni Idili, Sergey Khayrulin, Andrey Palyanov, Balazs Szigeti and Stephen Larson
</p>
<p>
<br/>
<a href="http://www.biomedcentral.com/1471-2202/16/S1/P141" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>OpenWorm: an open-science approach to modeling Caenorhabditis elegans</h2>
<div class="clearer"></div>
<p>
November 03 2014, Front. Comput. Neurosci., DOI: 10.3389/fncom.2014.00137
</p>
<p class="authors">
Balázs Szigeti, Padraig Gleeson, Michael Vella, Sergey Khayrulin, Andrey Palyanov, Jim Hokanson, Michael Currie, Matteo Cantarelli, Giovanni Idili and Stephen Larson
</p>
<p>
OpenWorm is an international collaboration with the aim of understanding how the behavior of Caenorhabditis elegans (C. elegans)
emerges from its underlying physiological processes. The project has developed a modular simulation engine to create computational
models of the worm. The modularity of the engine makes it possible to easily modify the model, incorporate new experimental data and
test hypotheses. The modeling framework incorporates both biophysical neuronal simulations and a novel fluid-dynamics-based soft-tissue
simulation for physical environment-body interactions. The project's open-science approach is aimed at overcoming the difficulties of
integrative modeling within a traditional academic environment. In this article the rationale is presented for creating the OpenWorm
collaboration, the tools and resources developed thus far are outlined and the unique challenges associated with the project are discussed.
<br/>
<a href="http://journal.frontiersin.org/article/10.3389/fncom.2014.00137/full" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Beyond the connectome hairball: Rational visualizations and analysis of the C. elegans connectome as a network graph using hive plots</h2>
<div class="clearer"></div>
<p>
July 11 2013, Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. DOI: 10.3389/conf.fninf.2013.09.00032
</p>
<p class="authors">
Pedro Tabacof, Tim Busbice, Stephen D. Larson
(OpenWorm.org)
</p>
<p>
The C. elegans connectome (White et al., 1986) is currently the most detailed connectome data set at the neuronal circuit level that is publicly available. Represented as a network graph, it consists of edges that distinguish between gap junctions and chemical synapses, weighted by synapse count, with nodes that represent neurons whose identities are unambiguous and well known.
We have found exploration of the C. elegans connectome using hive plots to lead to the discovery of interesting qualitative structure that was previously not obvious, enabling this structure to be further pursued quantitatively using complex network mathematics.
<br/>
<a href="http://www.frontiersin.org/10.3389/conf.fninf.2013.09.00032/event_abstract" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Integration of predictive-corrective incompressible SPH and Hodgkin-Huxley based models in the OpenWorm in silico model of C. elegans</h2>
<div class="clearer"></div>
<p>
July 8 2013, BMC Neuroscience 2013, 14(Suppl 1):P209 DOI:10.1186/1471-2202-14-S1-P209
</p>
<p class="authors">
Michael Vella (Department of Physiology, Development and Neuroscience, University of Cambridge), Andrey Palyanov, Sergey Khayrulin
(A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Acad. Lavrentjev pr., Russia)
</p>
<p>
OpenWorm is an international collaboration with the aim of producing an integrative computational model of Caenorhabditis elegans to further the understanding of how macroscopic behaviour of the organism emerges from aggregated biophysical processes. A core component of the project involves the integration of electrophysiological modelling and predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) to model how neuronal and muscle dynamics effect the nematode's behaviour.
<br/>
<a href="http://www.biomedcentral.com/1471-2202/14/S1/P209" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Towards a virtual C. elegans: A framework for simulation and visualization of the neuromuscular system in a 3D physical environment</h2>
<div class="clearer"></div>
<p>
Aug 2012, In Silico Biology, 11(3):137-147 DOI:10.3233/ISB-2012-0445
</p>
<p class="authors">
Andrey Palyanov, Sergey Khayrulin, Stephen D Larson, Alexander Dibert
A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Acad. Lavrentjev pr., Russia.
</p>
<p>
The nematode C. elegans is the only animal with a known neuronal wiring diagram, or "connectome". During the last three decades, extensive studies of the C. elegans have provided wide-ranging data about it, but few systematic ways of integrating these data into a dynamic model have been put forward. Here we present a detailed demonstration of a virtual C. elegans aimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current demonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord. Our virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal activity to groups of motor neurons. To account for the relatively slow propagation velocity and the attenuation of neuronal signals, we introduced "pseudo neurons" into our model to simulate simplified neuronal dynamics. The pseudo neurons also provide a good way of visualizing the nervous system's structure and activity dynamics.
<br/>
<a href="https://dl.dropbox.com/u/6318167/fulltext.pdf" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>The NeuroML C. elegans Connectome</h2>
<div class="clearer"></div>
<p>
September 11 2012 - Neuroinformatics 2012 Abstract Book
</p>
<p class="authors">
Tim Busbice (Interintelligence Research), Padraig Gleeson (University College London), Sergey Khayrulin (A.P. Ershov Institute of Informatics Systems), Matteo Cantarelli (OpenWorm.org), Alexander Dibert (A.P. Ershov Institute of Informatics Systems), Giovanni Idili (OpenWorm.org), Andrey Palyanov (A.P. Ershov Institute of Informatics Systems), Stephen Larson (OpenWorm.org)
</p>
<p>
We have merged and extended the C. elegans connectome (Varshney et al., 2006) and a three-dimensional cellular anatomy model (Grove & Sternberg, 2011) in the context of the OpenWorm project, an open source project to build a data integration and simulation framework for the C. elegans.
<br/>
<a href="http://www.neuroinformatics2012.org/abstracts/the-neuroml-c.-elegans-connectome" target="_blank">Read more</a>.
</p>
</div>
<hr class="soften">
<div>
<h2>Managing Complexity in Multi-Algorithm, Multi-Scale Biological Simulations: An Integrated Software Engineering and Neuroinformatics Approach</h2>
<div class="clearer"></div>
<p>
September 4 2011 - Neuroinformatics 2012 Abstract Book
</p>
<p class="authors">
Giovanni Idili (OpenWorm.org), Matteo Cantarelli (OpenWorm.org), Marius Buibas (Department of Bioengineering, University of California, San Diego, La Jolla, CA), Tim Busbice (InterIntelligence Research, Los Angeles, CA), Jay Coggan (Salk Institute, La Jolla, CA), Christian Grove (WormBase, California Institute of Technology, Pasadena CA), Sergey Khayrulin (A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Novosibirsk, Russia), Andrey Palyanov (A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Novosibirsk, Russia), Stephen Larson (Whole Brain Project, University of California, San Diego, La Jolla, CA)
</p>
<p>
Computational biology is asserting itself as an important approach to understanding complex biological systems.
In order to be able to effectively manage the complexity that comes with integrating and maintaining coarse-grained architectures, tools, digital information artifacts and code-bases, it is important for computational biology to fully embrace software engineering methodologies and best practices and follow the lead of the simulation based research in the physical sciences.
Taking cues from pioneering projects in computational neuroscience that are addressing this problem (MOOSE, http://j.mp/gSZZNF, Clones; http://j.mp/gzC5CP), we describe our approach to the integration of close-to-the-metal massively parallel simulations with high-level abstractions through the use of design patterns, including emerging paradigms for GPU-based parallel programming.
<br/>
<a href="http://www.neuroinformatics2011.org/abstracts/managing-complexity-in-multi-algorithm-multi-scale-biological-simulations-an-integrated-software-engineering-and-neuroinformatics-approach" target="_blank">Read more</a>.
</p>
</div>
</div>
</div>
<!-- FOOT: DUPLICATE THE FOLLOWING IN ALL PAGES -->
</div><!-- end pjax content -->
<!-- footer
================================================== -->
<div id="foot-nav"></div>
<!-- foot-nav is filled with script in head -->
<!-- Le javascript
================================================== -->
<!-- at end of the document so the pages load faster -->
<!-- load other resources asynchronously: -->
<script src="js/bootstrap.js" async></script>
<script src="js/jquery.parss.uncompressed.js" async></script>
<script src="http://platform.twitter.com/widgets.js" async></script>
<!-- execute main (initialize resources above + carousel/donate controls -->
<script src="js/jquery.pjax.js"></script>
<script src="js/main.js" defer></script>
</body>
</html>