Gregory J. Podgorski

Regular mosaic pattern development: A study of the interplay between lateral inhibition, apoptosis and differential adhesion

BackgroundA significant body of literature is devoted to modeling developmental mechanisms that create patterns within groups of initially equivalent embryonic cells. Although it is clear that these mechanisms do not function in isolation, the timing of and interactions between these mechanisms during embryogenesis is not well known. In this work, a computational approach was taken to understand how lateral inhibition, differential adhesion and programmed cell death can interact to create a mosaic pattern of biologically realistic primary and secondary cells, such as that formed by sensory (primary) and supporting (secondary) cells of the developing chick inner ear epithelium.ResultsFour different models that interlaced cellular patterning mechanisms in a variety of ways were examined and their output compared to the mosaic of sensory and supporting cells that develops in the chick inner ear sensory epithelium. The results show that: 1) no single patterning mechanism can create a 2-dimensional mosaic pattern of the regularity seen in the chick inner ear; 2) cell death was essential to generate the most regular mosaics, even through extensive cell death has not been reported for the developing basilar papilla; 3) a model that includes an iterative loop of lateral inhibition, programmed cell death and cell rearrangements driven by differential adhesion created mosaics of primary and secondary cells that are more regular than the basilar papilla; 4) this same model was much more robust to changes in homo- and heterotypic cell-cell adhesive differences than models that considered either fewer patterning mechanisms or single rather than iterative use of each mechanism.ConclusionPatterning the embryo requires collaboration between multiple mechanisms that operate iteratively. Interlacing these mechanisms into feedback loops not only refines the output patterns, but also increases the robustness of patterning to varying initial cell states.

Biological development of cell patterns: characterizing the space of cell chemistry genetic regulatory networks

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.

Discovering novel cancer therapies: A computational modeling and search approach

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.

Multistable switches and their role in cellular differentiation networks

BackgroundCellular differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types.ResultsTo explore this question a new methodology is developed and applied to study (a) the multistable switch-containing GRN for hematopoiesis and (b) a large set of random boolean networks (RBNs) in which multistable switches were embedded systematically. In this work, each network attractor is taken to represent a distinct cell type. The GRNs were seeded with one or two identical copies of each multistable switch and the effect of these additions on two key aspects of network dynamics was assessed. These properties are the barrier to movement between pairs of attractors (separation) and the degree to which one direction of movement between attractor pairs is favored over another (directionality). Both of these properties are instrumental in shaping the structure of lineage trees. We found that adding one multistable switch of any type had a modest effect on increasing the proportion of well-separated attractor pairs. Adding two identical switches of any type had a much stronger effect in increasing the proportion of well-separated attractors. Similarly, there was an increase in the frequency of directional transitions between attractor pairs when two identical multistable switches were added to GRNs. This effect on directionality was not observed when only one multistable switch was added.ConclusionsThis work provides evidence that the occurrence of multistable switches in networks that control cellular differentiation contributes to the structure of lineage trees and to the stabilization of cell types.

Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies

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.

Kolmogorov complexity of epithelial pattern formation: The role of regulatory network configuration

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.

Isolation of a cDNA Encoding a Portion of the Cyclic Nucleotide Phosphodiesterase of Dictyostelium discoideum

The cyclic nucleotide phosphodiesterase (phosphodiesterase) of Dictyostelium discoideum is one of a group of developmentally regulated proteins which enable cells to aggregate by chemotaxis during the early stages of development. We report the identification and DNA sequence of a cDNA clone encoding the amino-terminal region of the phosphodiesterase. The clone, pPD-3, was selected from a cDNA library created by priming first strand synthesis using a set of oligonucleotides with sequences predicted from the amino-terminal amino acid sequence of purified phosphodiesterase. The DNA sequence of pPD-3 encodes perfectly the available phosphodiesterase amino acid sequence, and pPD-3 selects an mRNA which can be translated into material recognized by phosphodiesterase antisera. The nucleotide sequence of pPD-3 indicates there are 49 amino acids, which contain a segment possessing the characteristics of a signal peptide, that separate the amino-terminal residue identified in the purified protein from the methionine codon at which translation originates. DNA blot analysis demonstrates that the phosphodiesterase gene exists as a single copy in the nuclear genome. Analysis of RNA indicates that the phosphodiesterase transcript is 2.1 kb long, which is approximately 0.8 kb more than the minimum required to encode this protein.

Bridging the multiscale gap: Identifying cellular parameters from multicellular data

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.

Modular genetic regulatory networks increase organization during pattern formation.

Studies have shown that genetic regulatory networks (GRNs) consist of modules that are densely connected subnetworks that function quasi-autonomously. Modules may be recognized motifs that comprise of two or three genes with particular regulatory functions and connectivity or be purely structural and identified through connection density. It is unclear what evolutionary and developmental advantages modular structure and in particular motifs provide that have led to this enrichment. This study seeks to understand how modules within developmental GRNs influence the complexity of multicellular patterns that emerge from the dynamics of the regulatory networks. We apply an algorithmic complexity to measure the organization of the patterns. A computational study was performed by creating Boolean intracellular networks within a simulated epithelial field of embryonic cells, where each cell contains the same network and communicates with adjacent cells using contact-mediated signaling. Intracellular networks with random connectivity were compared to those with modular connectivity and with motifs. Results show that modularity effects network dynamics and pattern organization significantly. In particular: (1) modular connectivity alone increases complexity in network dynamics and patterns; (2) bistable switch motifs simplify both the pattern and network dynamics; (3) all other motifs with feedback loops increase multicellular pattern complexity while simplifying the network dynamics; (4) negative feedback loops affect the dynamics complexity more significantly than positive feedback loops.

Co-option and Irreducibility in Regulatory Networks for Cellular Pattern Development

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 into their evolution