Nicholas Flann | Utah State University (original) (raw)
Papers by Nicholas Flann
—Multiscale models that link sub-cellular, cellular and multicellular components offer powerful i... more —Multiscale models that link sub-cellular, cellular and multicellular components offer powerful insights in disease development. Such models need a realistic set of parameters to represent the physical and chemical mechanisms at the sub-cellular and cellular levels to produce high fidelity multicellular outcomes. However, determining correct values for some of the parameters is often difficult and expensive using high-throughput microfluidic approaches. This work presents an alternative approach that estimates cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. Specifically, we apply a search technique to an integrated cellular and multicellular model of retinal pigment epithelial (RPE) cells to estimate the binding rate and auto-regulation rate of vascular endothelial growth factor (VEGF). Understanding VEGF regulation is critical in treating age-related macular degeneration and many other diseases. The method successfully identifies realistic values for autoregulatory cellular parameters that reproduce the spatiotemporal in vitro experimental data.
This chapter demonstrates an application of agent-based selection dynamics to the traffic assignm... more This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable a adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then show how this data can be employed to define the conditions for evolutionary stability and Nash equilibria. The validity of this method is demonstrated by studies in traffic network modeling, including an integrated application using geographic information systems applied to a complex road network in the San Francisco Bay area.
Lecture Notes in Computer Science, 1994
Abstract. Simulated annealing and genetic algorithms represent pow-erful optimization methods wit... more Abstract. Simulated annealing and genetic algorithms represent pow-erful optimization methods with complementary strengths and weak-nesses. Hence, there is an interest in identifying hybrid methods (which combine features of both SA and GA) that exhibit performance superior ...
Unmanned Ground Vehicle Technology VII, 2005
Autonomous Solutions has developed Chaos, a small unmanned ground vehicle with four modular runni... more Autonomous Solutions has developed Chaos, a small unmanned ground vehicle with four modular running gear receptacles. Running gear attached to the vehicle can include any combination of wheels, tracks, articulated and shape-shifting tracks, and legs. Each unit is independently controllable and field changeable. This modular design allows Chaos to combine the strengths of traditional and bio-inspired locomotion. The vehicle offers unprecedented mobility potential including walking, stair climbing, clambering over obstacles, steep slope traversal and extrication. To fully exploit the vehicle's mobility potential, intelligent behaviors must be integrated to reduce the complexity of vehicle operation. Mobility behaviors include operator assisted tele-operation, adaptive gaits, obstacle characterization, traversal, and extrication. This paper will describe the design and development of Chaos including running gear and intelligent mobility behaviors.
ABSTRACT The transition in breast cancer from ductal carcinoma in situ to invasive ductal carcino... more ABSTRACT The transition in breast cancer from ductal carcinoma in situ to invasive ductal carcinoma marks a significant drop in patient survival and is one of the leading causes of death in women. This work presents a new 3D model of the breast duct in which the previous 2D duct section model is extended in the lateral dimension to capture the complete duct morphology. The resulting increase in fidelity due to lateral extension is necessary for the discovery of effective interventions since interactions among the duct wall, interstitial fluid and the surrounding stroma are significantly changed. Specifically, the new model can capture the relief of pressure along the duct and the complex morphology of tumor invasion by the breaching of the basement membrane and epithelial tight junction wall. The model represents multiple biochemical and biomechanical interactions among the epithelia, tumor cells, diffusible signals, and stromal components such as fibroblasts, myofibroblasts and extracellular matrix in the ductal microenvironments. Understanding how the interplay among these components drives the dynamics of metastasis and invasion may lead to new therapeutic approaches to breast cancer. We introduced a 3D multicellular agent-based model of DCIS growth and invasion that includes ductal, stromal and tumor cell types acting along with microenvironmental components such as matrix metalloproteinases (MMP), Lysyl oxidase(LOX), nutrients, TGFβ and extracellular matrix (ECM) protein assemblies. We are investigating a wide range of parameters and assumptions in our model that can lead to changes in the model outcomes. The model explicitly determines mechanical tensional and compressive forces within the developing tissue.
Lecture Notes in Computer Science, 2005
Genetic regulatory networks (GRNs) control gene expression and are responsible for establishing t... more Genetic regulatory networks (GRNs) control gene expression and are responsible for establishing the regular cellular patterns that constitute an organism. This paper introduces a model of biological development that generates cellular patterns via chemical interactions. GRNs for protein expression are generated and evaluated for their effectiveness in constructing 2D patterns of cells such as borders, patches, and mosaics. Three types of searches were performed: (a) a Monte Carlo search of the GRN space using a utility function based on spatial interestingness; (b) a hill climbing search to identify GRNs that solve specific pattern problems; (c) a search for combinatorial codes that solve difficult target patterns by running multiple disjoint GRNs in parallel. We show that simple biologically realistic GRNs can construct many complex cellular patterns. Our model provides an avenue to explore the evolution of complex GRNs that drive development.
Unmanned Ground Vehicle Technology V, 2003
In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State ... more In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) was funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). As part of our research, we presented the use of a grammar-based approach to enabling intelligent behaviors in autonomous robotic vehicles. With the growth of the number of available resources on the robot, the variety of the generated behaviors and the need for parallel execution of multiple behaviors to achieve reaction also grew. As continuation of our past efforts, in this paper, we discuss the parallel execution of behaviors and the management of utilized resources. In our approach, available resources are wrapped with a layer (termed services) that synchronizes and serializes access to the underlying resources. The controlling agents (called behavior generating agents) generate behaviors to be executed via these services. The agents are prioritized and then, based on their priority and the availability of requested services, the Control Supervisor decides on a winner for the grant of access to services. Though the architecture is applicable to a variety of autonomous vehicles, we discuss its application on T4, a mid-sized autonomous vehicle developed for security applications.
Open Access Bioinformatics, 2014
Cell types are defined at the molecular level during embryogenesis by a process called pattern fo... more Cell types are defined at the molecular level during embryogenesis by a process called pattern formation and created by the selective utilization of combinations of sequence specific transcription factors. Developmental programs define the sets of genes that are available to each particular cell type, and real-time biochemical signaling interactions define the extent to which these sets are used at any given time and place. Gene expression is regulated through the integrated action of many cis-regulatory elements, including core promoters, enhancers, silencers, and insulators. The chromatin state in developing body parts provides a code to cellular populations that direct their cell fates. Chromatin profiling has been a method of choice for mapping regulatory sequences in cells that go through developmental transitions. We used antibodies against histone H3 lysine 4 trimethylations (H3K4me3) a modification associated with promoters and open/active chromatin, histone H3 lysine 27 trimethylations (H3K27me3) associated with Polycomb-repressed regions and RNA polymerase II (Pol2) associated with transcriptional initiation to identify the chromatin state signature of the mouse forelimb during mid-gestation, at embryonic day 12 (E12). The families of genes marked included those related to transcriptional regulation and embryogenesis. One third of the marked genes were transcriptionally active while only a small fraction were bivalent marked. Sequence specific transcription factors that were activated were involved in cell specification including bone and muscle formation. Our results demonstrate that embryonic limb cells do not exhibit the plasticity of the ES cells but are rather programmed for a finer tuning for cell lineage specification.
Bioinformatics (Oxford, England), 2014
Biological system behaviors are often the outcome of complex interactions among a large number of... more Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biologic...
2011 IEEE Congress of Evolutionary Computation (CEC), 2011
... Variable Size Individuals Ahmadreza Ghaffarizadeh, Kamilia Ahmadi and Nicholas S. Flann Compu... more ... Variable Size Individuals Ahmadreza Ghaffarizadeh, Kamilia Ahmadi and Nicholas S. Flann Computer Science Department, Utah State University, Logan Utah 84322-4205 Email: {ghaffarizadeh, k.ahmadi}@aggiemail.usu.edu, nick.flann@usu.edu ... Page 2. (a) (b) (c) Fig. ...
2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2008
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that bl... more Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating rationally-designed treatments prevent the discovery of effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the cost of developing the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding interventions that are currently in use and more interestingly, discovering some new approaches that are counter intuitive yet effective.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
Area coverage operations such as plowing a field or mowing a lawn can be performed faster if mult... more Area coverage operations such as plowing a field or mowing a lawn can be performed faster if multiple vehicles are involved. To use a team of automated vehicles safely and effectively they must be coordinated to avoid collisions and deadlock situations. Unexpected events may occur during the operation which may affect vehicles' velocities, so the coordination method must be robust with respect to these events. In this paper, a path coordination method is introduced which delays decisions about mission coordination as long as possible during mission execution so such unexpected situations are efficiently handled. The method's computation speed and solution quality are evaluated through simulation, and compared with two other methods based on common path coordination techniques.
2007 IEEE Symposium on Artificial Life, 2007
We used a computational approach to examine three questions at the intersection of developmental ... more We used a computational approach to examine three questions at the intersection of developmental biology and evolution: 1) What is the space available for evolutionary exploration for genetic regulatory networks (GRNs) able to solve developmental patterning problems? 2) If different GRNs exist that can solve a particular pattern, are there differences between them that might lead to the selection of one over another? 3) What are the possibilities for co-opting GRN subcircuits or even entire GRNs evolved to solve one pattern for use in the solution of another pattern? We used a Monte Carlo strategy to search for simulated GRNs composed of nodes (proteins) and edges (regulatory interactions between proteins) capable of solving one of three striped cellular patterning problems. These GRNs were subjected to a knockout procedure akin to gene knock-outs in genetic research. Knockout was continued until all individual network components of the reduced GRN were shown to be essential for function. This GRN was termed irreducible. We found many different unique irreducible GRNs that were able to solve each patterning problem. Since any functional GRN must include an irreducible GRN as a core or subgraph, the space for evolutionary exploration of pattern-forming GRNs is large. Irreducible GRNs that solve a particular pattern differed widely in their robustness - the ability to solve a target pattern under different initial conditions. These differences may offer a target for selection to winnow out less robust GRNs from the set of GRNs found in nature. Finally, subgraph isomorphism analysis revealed great potential for co-option during evolution. Some irreducible GRNs appear in their entirety within larger GRNs that solve different patterning problems. At much higher frequency, subcycles are shared widely among irreducible GRNs, including those that solve different patterns. Irreducible GRNs may form the core elements of GRNs found in biological systems and provide insight int- o their evolution
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2000
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that bl... more Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding interventions that are currently in use, and more interestingly, discovering potential new approaches that are nonintuitive yet effective.
IEEE Transactions on Parallel and Distributed Systems, 1998
Many significant engineering and scientific problems involve optimization of some criteria over a... more Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively-simulated annealing (SA) and genetic algorithms (GA)-do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a selection process that requires global coordination. This paper introduces a new hybrid algorithm that inherits those aspects of GA that lend themselves to parallelization, and avoids serial bottle-necks of GA approaches by incorporating elements of SA to provide a completely parallel, easily scalable hybrid GA/SA method. This new method, called Genetic Simulated Annealing, does not require parallelization of any problem specific portions of a serial implementation-existing serial implementations can be incorporated as is. Results of a study on two difficult combinatorial optimization problems, a 100 city traveling salesperson problem and a 24 word, 12 bit error correcting code design problem, performed on a 16K PE MasPar MP-1, indicate advantages over previous parallel GA and SA approaches. One of the key results is that the performance of the algorithm scales up linearly with the increase of processing elements, a feature not demonstrated by any previous parallel GA or SA approaches, which enables the new algorithm to utilize massive parallel architecture with maximum effectiveness. Additionally, the algorithm does not require careful choice of control parameters, a significant advantage over SA and GA.
Control Engineering Practice, 2002
... This research has resulted in the so-called T-series of omni-directional (ODV) robots ( Moore... more ... This research has resulted in the so-called T-series of omni-directional (ODV) robots ( Moore &amp;amp; Flann, 2000). The distinguishing feature of these robots is that both the speed and direction of each smart wheel can be independently controlled through dedicated processors. ...
Computational and Mathematical Methods in Medicine, 2014
Understanding cellular differentiation is critical in explaining development and for taming disea... more Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool called CellDiff3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network.
BMC Bioinformatics, 2014
Background: Analysis of cellular processes with microscopic bright field defocused imaging has th... more Background: Analysis of cellular processes with microscopic bright field defocused imaging has the advantage of low phototoxicity and minimal sample preparation. However bright field images lack the contrast and nuclei reporting available with florescent approaches and therefore present a challenge to methods that segment and track the live cells. Moreover, such methods must be robust to systemic and random noise, variability in experimental configuration, and the multiple unknowns in the biological system under study. Results: A new method called maximal-information is introduced that applies a non-parametric information theoretic approach to segment bright field defocused images. The method utilizes a combinatorial optimization strategy to select specific defocused images from each image stack such that set complexity, a Kolmogorov complexity measure, is maximized. Differences among these selected images are then applied to initialize and guide a level set based segmentation algorithm. The performance of the method is compared with a recent approach that uses a fixed defocused image selection strategy over an image data set of embryonic kidney cells (HEK 293T) from multiple experiments. Results demonstrate that the adaptive maximal-information approach significantly improves precision and recall of segmentation over the diversity of data sets. Conclusions: Integrating combinatorial optimization with non-parametric Kolmogorov complexity has been shown to be effective in extracting information from microscopic bright field defocused images. The approach is application independent and has the potential to be effective in processing a diversity of noisy and redundant high throughput biological data.
Biosystems, 2013
The tissues of multicellular organisms are made of differentiated cells arranged in organized pat... more The tissues of multicellular organisms are made of differentiated cells arranged in organized patterns. This organization emerges during development from the coupling of dynamic intra-and intercellular regulatory networks. This work applies the methods of information theory to understand how regulatory network structure both within and between cells relates to the complexity of spatial patterns that emerge as a consequence of network operation. A computational study was performed in which undifferentiated cells were arranged in a two dimensional lattice, with gene expression in each cell regulated by identical intracellular randomly generated Boolean networks. Cell-cell contact signalling between embryonic cells is modeled as coupling among intracellular networks so that gene expression in one cell can influence the expression of genes in adjacent cells. In this system, the initially identical cells differentiate and form patterns of different cell types. The complexity of network structure, temporal dynamics and spatial organization is quantified through the Kolmogorov-based measures of normalized compression distance and set complexity. Results over sets of random networks that operate in the ordered, critical and chaotic domains demonstrate that: (1) ordered and critical networks tend to create the most information-rich patterns; (2) signalling configurations in which cell-to-cell communication is non-directional mostly produce simple patterns irrespective of the internal network domain; and (3) directional signalling configurations, similar to those that function in planar cell polarity, produce the most complex patterns, but only when the intracellular networks function in non-chaotic domains.
—Multiscale models that link sub-cellular, cellular and multicellular components offer powerful i... more —Multiscale models that link sub-cellular, cellular and multicellular components offer powerful insights in disease development. Such models need a realistic set of parameters to represent the physical and chemical mechanisms at the sub-cellular and cellular levels to produce high fidelity multicellular outcomes. However, determining correct values for some of the parameters is often difficult and expensive using high-throughput microfluidic approaches. This work presents an alternative approach that estimates cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. Specifically, we apply a search technique to an integrated cellular and multicellular model of retinal pigment epithelial (RPE) cells to estimate the binding rate and auto-regulation rate of vascular endothelial growth factor (VEGF). Understanding VEGF regulation is critical in treating age-related macular degeneration and many other diseases. The method successfully identifies realistic values for autoregulatory cellular parameters that reproduce the spatiotemporal in vitro experimental data.
This chapter demonstrates an application of agent-based selection dynamics to the traffic assignm... more This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable a adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then show how this data can be employed to define the conditions for evolutionary stability and Nash equilibria. The validity of this method is demonstrated by studies in traffic network modeling, including an integrated application using geographic information systems applied to a complex road network in the San Francisco Bay area.
Lecture Notes in Computer Science, 1994
Abstract. Simulated annealing and genetic algorithms represent pow-erful optimization methods wit... more Abstract. Simulated annealing and genetic algorithms represent pow-erful optimization methods with complementary strengths and weak-nesses. Hence, there is an interest in identifying hybrid methods (which combine features of both SA and GA) that exhibit performance superior ...
Unmanned Ground Vehicle Technology VII, 2005
Autonomous Solutions has developed Chaos, a small unmanned ground vehicle with four modular runni... more Autonomous Solutions has developed Chaos, a small unmanned ground vehicle with four modular running gear receptacles. Running gear attached to the vehicle can include any combination of wheels, tracks, articulated and shape-shifting tracks, and legs. Each unit is independently controllable and field changeable. This modular design allows Chaos to combine the strengths of traditional and bio-inspired locomotion. The vehicle offers unprecedented mobility potential including walking, stair climbing, clambering over obstacles, steep slope traversal and extrication. To fully exploit the vehicle's mobility potential, intelligent behaviors must be integrated to reduce the complexity of vehicle operation. Mobility behaviors include operator assisted tele-operation, adaptive gaits, obstacle characterization, traversal, and extrication. This paper will describe the design and development of Chaos including running gear and intelligent mobility behaviors.
ABSTRACT The transition in breast cancer from ductal carcinoma in situ to invasive ductal carcino... more ABSTRACT The transition in breast cancer from ductal carcinoma in situ to invasive ductal carcinoma marks a significant drop in patient survival and is one of the leading causes of death in women. This work presents a new 3D model of the breast duct in which the previous 2D duct section model is extended in the lateral dimension to capture the complete duct morphology. The resulting increase in fidelity due to lateral extension is necessary for the discovery of effective interventions since interactions among the duct wall, interstitial fluid and the surrounding stroma are significantly changed. Specifically, the new model can capture the relief of pressure along the duct and the complex morphology of tumor invasion by the breaching of the basement membrane and epithelial tight junction wall. The model represents multiple biochemical and biomechanical interactions among the epithelia, tumor cells, diffusible signals, and stromal components such as fibroblasts, myofibroblasts and extracellular matrix in the ductal microenvironments. Understanding how the interplay among these components drives the dynamics of metastasis and invasion may lead to new therapeutic approaches to breast cancer. We introduced a 3D multicellular agent-based model of DCIS growth and invasion that includes ductal, stromal and tumor cell types acting along with microenvironmental components such as matrix metalloproteinases (MMP), Lysyl oxidase(LOX), nutrients, TGFβ and extracellular matrix (ECM) protein assemblies. We are investigating a wide range of parameters and assumptions in our model that can lead to changes in the model outcomes. The model explicitly determines mechanical tensional and compressive forces within the developing tissue.
Lecture Notes in Computer Science, 2005
Genetic regulatory networks (GRNs) control gene expression and are responsible for establishing t... more Genetic regulatory networks (GRNs) control gene expression and are responsible for establishing the regular cellular patterns that constitute an organism. This paper introduces a model of biological development that generates cellular patterns via chemical interactions. GRNs for protein expression are generated and evaluated for their effectiveness in constructing 2D patterns of cells such as borders, patches, and mosaics. Three types of searches were performed: (a) a Monte Carlo search of the GRN space using a utility function based on spatial interestingness; (b) a hill climbing search to identify GRNs that solve specific pattern problems; (c) a search for combinatorial codes that solve difficult target patterns by running multiple disjoint GRNs in parallel. We show that simple biologically realistic GRNs can construct many complex cellular patterns. Our model provides an avenue to explore the evolution of complex GRNs that drive development.
Unmanned Ground Vehicle Technology V, 2003
In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State ... more In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) was funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). As part of our research, we presented the use of a grammar-based approach to enabling intelligent behaviors in autonomous robotic vehicles. With the growth of the number of available resources on the robot, the variety of the generated behaviors and the need for parallel execution of multiple behaviors to achieve reaction also grew. As continuation of our past efforts, in this paper, we discuss the parallel execution of behaviors and the management of utilized resources. In our approach, available resources are wrapped with a layer (termed services) that synchronizes and serializes access to the underlying resources. The controlling agents (called behavior generating agents) generate behaviors to be executed via these services. The agents are prioritized and then, based on their priority and the availability of requested services, the Control Supervisor decides on a winner for the grant of access to services. Though the architecture is applicable to a variety of autonomous vehicles, we discuss its application on T4, a mid-sized autonomous vehicle developed for security applications.
Open Access Bioinformatics, 2014
Cell types are defined at the molecular level during embryogenesis by a process called pattern fo... more Cell types are defined at the molecular level during embryogenesis by a process called pattern formation and created by the selective utilization of combinations of sequence specific transcription factors. Developmental programs define the sets of genes that are available to each particular cell type, and real-time biochemical signaling interactions define the extent to which these sets are used at any given time and place. Gene expression is regulated through the integrated action of many cis-regulatory elements, including core promoters, enhancers, silencers, and insulators. The chromatin state in developing body parts provides a code to cellular populations that direct their cell fates. Chromatin profiling has been a method of choice for mapping regulatory sequences in cells that go through developmental transitions. We used antibodies against histone H3 lysine 4 trimethylations (H3K4me3) a modification associated with promoters and open/active chromatin, histone H3 lysine 27 trimethylations (H3K27me3) associated with Polycomb-repressed regions and RNA polymerase II (Pol2) associated with transcriptional initiation to identify the chromatin state signature of the mouse forelimb during mid-gestation, at embryonic day 12 (E12). The families of genes marked included those related to transcriptional regulation and embryogenesis. One third of the marked genes were transcriptionally active while only a small fraction were bivalent marked. Sequence specific transcription factors that were activated were involved in cell specification including bone and muscle formation. Our results demonstrate that embryonic limb cells do not exhibit the plasticity of the ES cells but are rather programmed for a finer tuning for cell lineage specification.
Bioinformatics (Oxford, England), 2014
Biological system behaviors are often the outcome of complex interactions among a large number of... more Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biologic...
2011 IEEE Congress of Evolutionary Computation (CEC), 2011
... Variable Size Individuals Ahmadreza Ghaffarizadeh, Kamilia Ahmadi and Nicholas S. Flann Compu... more ... Variable Size Individuals Ahmadreza Ghaffarizadeh, Kamilia Ahmadi and Nicholas S. Flann Computer Science Department, Utah State University, Logan Utah 84322-4205 Email: {ghaffarizadeh, k.ahmadi}@aggiemail.usu.edu, nick.flann@usu.edu ... Page 2. (a) (b) (c) Fig. ...
2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2008
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that bl... more Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating rationally-designed treatments prevent the discovery of effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the cost of developing the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding interventions that are currently in use and more interestingly, discovering some new approaches that are counter intuitive yet effective.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
Area coverage operations such as plowing a field or mowing a lawn can be performed faster if mult... more Area coverage operations such as plowing a field or mowing a lawn can be performed faster if multiple vehicles are involved. To use a team of automated vehicles safely and effectively they must be coordinated to avoid collisions and deadlock situations. Unexpected events may occur during the operation which may affect vehicles' velocities, so the coordination method must be robust with respect to these events. In this paper, a path coordination method is introduced which delays decisions about mission coordination as long as possible during mission execution so such unexpected situations are efficiently handled. The method's computation speed and solution quality are evaluated through simulation, and compared with two other methods based on common path coordination techniques.
2007 IEEE Symposium on Artificial Life, 2007
We used a computational approach to examine three questions at the intersection of developmental ... more We used a computational approach to examine three questions at the intersection of developmental biology and evolution: 1) What is the space available for evolutionary exploration for genetic regulatory networks (GRNs) able to solve developmental patterning problems? 2) If different GRNs exist that can solve a particular pattern, are there differences between them that might lead to the selection of one over another? 3) What are the possibilities for co-opting GRN subcircuits or even entire GRNs evolved to solve one pattern for use in the solution of another pattern? We used a Monte Carlo strategy to search for simulated GRNs composed of nodes (proteins) and edges (regulatory interactions between proteins) capable of solving one of three striped cellular patterning problems. These GRNs were subjected to a knockout procedure akin to gene knock-outs in genetic research. Knockout was continued until all individual network components of the reduced GRN were shown to be essential for function. This GRN was termed irreducible. We found many different unique irreducible GRNs that were able to solve each patterning problem. Since any functional GRN must include an irreducible GRN as a core or subgraph, the space for evolutionary exploration of pattern-forming GRNs is large. Irreducible GRNs that solve a particular pattern differed widely in their robustness - the ability to solve a target pattern under different initial conditions. These differences may offer a target for selection to winnow out less robust GRNs from the set of GRNs found in nature. Finally, subgraph isomorphism analysis revealed great potential for co-option during evolution. Some irreducible GRNs appear in their entirety within larger GRNs that solve different patterning problems. At much higher frequency, subcycles are shared widely among irreducible GRNs, including those that solve different patterns. Irreducible GRNs may form the core elements of GRNs found in biological systems and provide insight int- o their evolution
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2000
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that bl... more Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding interventions that are currently in use, and more interestingly, discovering potential new approaches that are nonintuitive yet effective.
IEEE Transactions on Parallel and Distributed Systems, 1998
Many significant engineering and scientific problems involve optimization of some criteria over a... more Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively-simulated annealing (SA) and genetic algorithms (GA)-do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a selection process that requires global coordination. This paper introduces a new hybrid algorithm that inherits those aspects of GA that lend themselves to parallelization, and avoids serial bottle-necks of GA approaches by incorporating elements of SA to provide a completely parallel, easily scalable hybrid GA/SA method. This new method, called Genetic Simulated Annealing, does not require parallelization of any problem specific portions of a serial implementation-existing serial implementations can be incorporated as is. Results of a study on two difficult combinatorial optimization problems, a 100 city traveling salesperson problem and a 24 word, 12 bit error correcting code design problem, performed on a 16K PE MasPar MP-1, indicate advantages over previous parallel GA and SA approaches. One of the key results is that the performance of the algorithm scales up linearly with the increase of processing elements, a feature not demonstrated by any previous parallel GA or SA approaches, which enables the new algorithm to utilize massive parallel architecture with maximum effectiveness. Additionally, the algorithm does not require careful choice of control parameters, a significant advantage over SA and GA.
Control Engineering Practice, 2002
... This research has resulted in the so-called T-series of omni-directional (ODV) robots ( Moore... more ... This research has resulted in the so-called T-series of omni-directional (ODV) robots ( Moore &amp;amp; Flann, 2000). The distinguishing feature of these robots is that both the speed and direction of each smart wheel can be independently controlled through dedicated processors. ...
Computational and Mathematical Methods in Medicine, 2014
Understanding cellular differentiation is critical in explaining development and for taming disea... more Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool called CellDiff3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network.
BMC Bioinformatics, 2014
Background: Analysis of cellular processes with microscopic bright field defocused imaging has th... more Background: Analysis of cellular processes with microscopic bright field defocused imaging has the advantage of low phototoxicity and minimal sample preparation. However bright field images lack the contrast and nuclei reporting available with florescent approaches and therefore present a challenge to methods that segment and track the live cells. Moreover, such methods must be robust to systemic and random noise, variability in experimental configuration, and the multiple unknowns in the biological system under study. Results: A new method called maximal-information is introduced that applies a non-parametric information theoretic approach to segment bright field defocused images. The method utilizes a combinatorial optimization strategy to select specific defocused images from each image stack such that set complexity, a Kolmogorov complexity measure, is maximized. Differences among these selected images are then applied to initialize and guide a level set based segmentation algorithm. The performance of the method is compared with a recent approach that uses a fixed defocused image selection strategy over an image data set of embryonic kidney cells (HEK 293T) from multiple experiments. Results demonstrate that the adaptive maximal-information approach significantly improves precision and recall of segmentation over the diversity of data sets. Conclusions: Integrating combinatorial optimization with non-parametric Kolmogorov complexity has been shown to be effective in extracting information from microscopic bright field defocused images. The approach is application independent and has the potential to be effective in processing a diversity of noisy and redundant high throughput biological data.
Biosystems, 2013
The tissues of multicellular organisms are made of differentiated cells arranged in organized pat... more The tissues of multicellular organisms are made of differentiated cells arranged in organized patterns. This organization emerges during development from the coupling of dynamic intra-and intercellular regulatory networks. This work applies the methods of information theory to understand how regulatory network structure both within and between cells relates to the complexity of spatial patterns that emerge as a consequence of network operation. A computational study was performed in which undifferentiated cells were arranged in a two dimensional lattice, with gene expression in each cell regulated by identical intracellular randomly generated Boolean networks. Cell-cell contact signalling between embryonic cells is modeled as coupling among intracellular networks so that gene expression in one cell can influence the expression of genes in adjacent cells. In this system, the initially identical cells differentiate and form patterns of different cell types. The complexity of network structure, temporal dynamics and spatial organization is quantified through the Kolmogorov-based measures of normalized compression distance and set complexity. Results over sets of random networks that operate in the ordered, critical and chaotic domains demonstrate that: (1) ordered and critical networks tend to create the most information-rich patterns; (2) signalling configurations in which cell-to-cell communication is non-directional mostly produce simple patterns irrespective of the internal network domain; and (3) directional signalling configurations, similar to those that function in planar cell polarity, produce the most complex patterns, but only when the intracellular networks function in non-chaotic domains.