UMBC Individualized Study Program (INDS)

Permanent URI for this collection

http://hdl.handle.net/11603/14962

The Individualized Study Program awards both BA and BS degrees to UMBC undergraduates who have used the INDS 335 class to assemble a degree proposal and had this proposal approved by the Individualized Study Committee (ISC).

Each of our students works with INDS staff and two hand-picked members drawn from the broader UMBC faculty and/or professional world to create this individualized education program. In addition to providing a core curriculum to help students combine courses from different disciplines, we also encourage internships and independent research.

From 1969-1979, INDS was called Option II. From 1979-2018, INDS was called Interdisciplinary Studies. In 2018, it was renamed Individualized Study.

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Now showing 1 - 16 of 16

SEEe Immersive Analytics System: Enhancing Data Analysis Experience within Complex Data Visualization Environments
(ACM, 2024-06-07) Rajasagi, Damaruka Priya; Boot, Lee; Wilson, Lucy; King, Tristan; Zuber, James; Stockwell, Ian; Komlodi, Anita
The current state-of-the-art 2D data visualizations fall short in capturing the intricate complexity and depth of available information crucial for integrated decision-making. In response to this limitation, the Systems Exploration and Engagement environment (SEEe) emerges as a cutting-edge virtual immersive analytics data experience. We developed this system through a user-centered design process involving an interdisciplinary design and development team. Through virtual reality, SEEe seamlessly integrates geo-referenced spatial data, abstract data visualization, and qualitative data encompassing text, images, videos, and conceptual diagrams to support sensemaking from large amounts of multiformat data and integrated decision making. We aim to redefine the experience of analyzing extensive amounts of abstract data by creating an environment that accommodates both quantitative and qualitative data for visualization and analysis. How these novel immersive analytics experiences fit into data analysis workflows in various domains have not been studied widely. We carried out a user study with 10 public health graduate students to test the usability, learnability, and utility of the SEEe experience and to explore how these immersive data visualization experiences can fit into traditional data analysis processes. While SEEe is designed to be adaptable across various domains, we evaluated its performance within the public health context. The results of the evaluation affirm that SEEe is not only usable and useful but also provides a learnable environment conducive to immersive analytics.
Language Evolution in Humans and Ancient Microbes: What can human language acquisition tell us about the origin of genetic information?
(Stockholm University, 2011) Freeland, Stephen J.; Ilardo, Melissa
This paper seeks to encourage dialog on a question with a deceptively simple surface. When we find linguistics and genetics using the same vocabulary to describe their central phenomena, is this because the phenomena are meaningfully similar? Are we encountering a superficial analogy, enjoying the benefits of a good metaphor or recognizing some deeper principles of information organization and transfer? We approach this broad topic by focusing attention on the ancient evolutionary events that created a system of genetic coding, soon after the origin of life on our planet. Specifically we examine a progression of three topics: whether genetic code-words are arbitrary signifiers for the objects they encode (amino acids); how evolutionary biologists have deduced clues about the evolution of genetic coding by studying the complex end-product; and the current scientific paradigm for the origin of genetic information. Our suggested points of connection suggest encouraging insights from each side (linguistics to evolutionary biochemistry and vice versa), though our primary aim is to ask for further help exploring how linguistics can reshape thinking within evolutionary biology.
Load minimization of the genetic code: history does not explain the pattern
(The Royal Society, 1998-11-07) Freeland, S. J.; Hurst, L. D.
The average effect of errors acting on a genetic code (the change in amino-acid meaning resulting from point mutation and mistranslation) may be quantified as its 'load'. The natural genetic code shows a clear property of minimizing this load when compared against randomly generated variant codes. Two hypotheses may be considered to explain this property. First, it is possible that the natural code is the result of selection to minimize this load. Second, it is possible that the property is an historical artefact. It has previously been reported that amino acids that have been assigned to codons starting with the same base come from the same biosynthetic pathway. This probably reflects the manner in which the code evolved from a simpler code, and says more about the physicochemical mechanisms of code assembly than about selection. The apparent load minimization of the code may therefore follow as a consequence of the fact that the code could not have evolved any other way than to allow biochemically related amino acids to have related codons. Here then, we ask whether this 'historical' force alone can explain the efficiency of the natural code in minimizing the effects of error. We therefore compare the error-minimizing ability of the natural code with that of alternative codes which, rather than being a random selection, are restricted such that amino acids from the same biochemical pathway all share the same first base. We find that although on average the restricted set of codes show a slightly higher efficiency than random ones, the real code remains extremely efficient relative to this subset P = 0.0003. This indicates that for the most part historical features do not explain the load- minimization property of the natural code. The importance of selection is further supported by the finding that the natural code's efficiency improves relative to that of historically related codes after allowance is made for realistic mutational and mistranslational biases. Once mistranslational biases have been considered, fewer than four per 100,000 alternative codes are better than the natural code.
Testing a biosynthetic theory of the genetic code: Fact or artifact?
(PNAS, 2000-11-21) Ronneberg, Terres A.; Landweber, Laura F.; Freeland, Stephen J.
It has long been conjectured that the canonical genetic code evolved from a simpler primordial form that encoded fewer amino acids [e.g., Crick, F. H. C. (1968) J. Mol. Biol. 38, 367–379]. The most influential form of this idea, “code coevolution” [Wong, J. T.-F. (1975) Proc. Natl. Acad. Sci. USA 72, 1909–1912], proposes that the genetic code coevolved with the invention of biosynthetic pathways for new amino acids. It further proposes that a comparison of modern codon assignments with the conserved metabolic pathways of amino acid biosynthesis can inform us about this history of code expansion. Here we re-examine the biochemical basis of this theory to test the validity of its statistical support. We show that the theory's definition of “precursor-product” amino acid pairs is unjustified biochemically because it requires the energetically unfavorable reversal of steps in extant metabolic pathways to achieve desired relationships. In addition, the theory neglects important biochemical constraints when calculating the probability that chance could assign precursor-product amino acids to contiguous codons. A conservative correction for these errors reveals a surprisingly high 23% probability that apparent patterns within the code are caused purely by chance. Finally, even this figure rests on post hoc assumptions about primordial codon assignments, without which the probability rises to 62% that chance alone could explain the precursor-product pairings found within the code. Thus we conclude that coevolution theory cannot adequately explain the structure of the genetic code.
An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices
(BMC, 2006-07-03) Bulka, Blazej; desJardins, Marie; Freeland, Stephen J.
Background: Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors. Results: We demonstrate the three fundamental types of question that our software can address by taking as a specific example the connections between 49 measures of amino acid biophysical properties (e.g., size, charge and hydrophobicity), a generalized model of amino acid substitution (as represented by the PAM74-100 matrix), and the mutational distance that separates amino acids within the standard genetic code (i.e., the number of point mutations required for interconversion during protein evolution). We show that our software allows a user to recapture the insights from several key publications on these topics in just a few minutes. Conclusion: Our software facilitates rapid, interactive exploration of three interconnected topics: (i) the multidimensional molecular descriptors of the twenty proteinaceous amino acids, (ii) the correlation of these biophysical measurements with observed patterns of amino acid substitution, and (iii) the causal basis for differences between any two observed patterns of amino acid substitution. This software acts as an intuitive bioinformatic exploration tool that can guide more comprehensive statistical analyses relating to a diverse array of specific research questions.
On the evolution of the standard amino-acid alphabet
(Springer, 2006-02-01) Lu, Yi; Freeland, Stephen
Although one standard amino-acid 'alphabet' is used by most organisms on Earth, the evolutionary cause(s) and significance of this alphabet remain elusive. Fresh insights into the origin of the alphabet are now emerging from disciplines as diverse as astrobiology, biochemical engineering and bioinformatics.
SGDB: a database of synthetic genes re-designed for optimizing protein over-expression
(Oxford University Press, 2006-11-07) Wu, Gang; Zheng, Yuanpu; Qureshi, Imran; Zin, Htar Thant; Beck, Tyler; Bulka, Blazej; Freeland, Stephen J.
Here we present the Synthetic Gene Database (SGDB): a relational database that houses sequences and associated experimental information on synthetic (artificially engineered) genes from all peer-reviewed studies published to date. At present, the database comprises information from more than 200 published experiments. This resource not only provides reference material to guide experimentalists in designing new genes that improve protein expression, but also offers a dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that influence gene expression. The SGDB was built under MySQL database management system. We also offer an XML schema for standardized data description of synthetic genes. Users can access the database at http://www.evolvingcode.net/codon/sgdb/index.php, or batch downloads all information through XML files. Moreover, users may visually compare the coding sequences of a synthetic gene and its natural counterpart with an integrated web tool at http://www.evolvingcode.net/codon/sgdb/aligner.php, and discuss questions, findings and related information on an associated e-forum at http://www.evolvingcode.net/forum/viewforum.php?f=27.
Optimal encoding rules for synthetic genes: the need for a community effort
(EMBO Press, 2007-09-18) Wu, Gang; Dress, Laura; Freeland, Stephen J.
The Evolutionary Origins of Genetic Information
(The American Scientific Affliciation, 2011-12) Freeland, Stephen
Any living branch of science achieves progress by testing new ideas. The results of these tests determine whether each new idea is accepted as a change to what we thought we knew, is dismissed as incorrect, or simply stagnates, owing to a lack of clear evidence. For evolutionary theory, one such proposition is that some features of genetic information cannot evolve through natural processes unless we allow a role for an intelligent designer. This proposition claims testability by defining information in a way that is usually reserved for human creations, such as computer program ming code. The argument is that since we know that intelligent beings create computer code, then perhaps similar features found within genetic information indicate a similar origin. However, many biologists perceive that they are able to understand exactly where life’s genetic information comes from (the local environment) by thinking in terms of more fundamental and well-established definitions of information that do not involve intelligent design. Current science does not have a detailed, widely accepted description for how a genetic information system evolved in the first place. Intelligent design (ID) proponents suggest that this is a key weakness of existing evolutionary theory, consistent with the need for an intelligent designer. I describe the progress that mainstream science has made toward understanding the origin of genetic information ever since the molecular basis of genetic information was first understood, encouraging readers to reach their own conclusions.
Boron Enrichment in Martian Clay
(PLOS, 2013-06-06) Stephenson, James D.; Hallis, Lydia J.; Nagashima, Kazuhide; Freeland, Stephen J.
We have detected a concentration of boron in martian clay far in excess of that in any previously reported extra-terrestrial object. This enrichment indicates that the chemistry necessary for the formation of ribose, a key component of RNA, could have existed on Mars since the formation of early clay deposits, contemporary to the emergence of life on Earth. Given the greater similarity of Earth and Mars early in their geological history, and the extensive disruption of Earth's earliest mineralogy by plate tectonics, we suggest that the conditions for prebiotic ribose synthesis may be better understood by further Mars exploration.
Unearthing the Root of Amino Acid Similarity
(Springer, 2013-06-07) Stephenson, James D.; Freeland, Stephen J.
Similarities and differences between amino acids define the rates at which they substitute for one another within protein sequences and the patterns by which these sequences form protein structures. However, there exist many ways to measure similarity, whether one considers the molecular attributes of individual amino acids, the roles that they play within proteins, or some nuanced contribution of each. One popular approach to representing these relationships is to divide the 20 amino acids of the standard genetic code into groups, thereby forming a simplified amino acid alphabet. Here, we develop a method to compare or combine different simplified alphabets, and apply it to 34 simplified alphabets from the scientific literature. We use this method to show that while different suggestions vary and agree in non-intuitive ways, they combine to reveal a consensus view of amino acid similarity that is clearly rooted in physico-chemistry.
Extraordinarily Adaptive Properties of the Genetically Encoded Amino Acids
(Nature, 2015-03-24) Ilardo, Melissa; Meringer, Markus; Freeland, Stephen; Rasulev, Bakhtiyor; Cleaves, H. James II
Using novel advances in computational chemistry, we demonstrate that the set of 20 genetically encoded amino acids, used nearly universally to construct all coded terrestrial proteins, has been highly influenced by natural selection. We defined an adaptive set of amino acids as one whose members thoroughly cover relevant physico-chemical properties, or “chemistry space.” Using this metric, we compared the encoded amino acid alphabet to random sets of amino acids. These random sets were drawn from a computationally generated compound library containing 1913 alternative amino acids that lie within the molecular weight range of the encoded amino acids. Sets that cover chemistry space better than the genetically encoded alphabet are extremely rare and energetically costly. Further analysis of more adaptive sets reveals common features and anomalies and we explore their implications for synthetic biology. We present these computations as evidence that the set of 20 amino acids found within the standard genetic code is the result of considerable natural selection. The amino acids used for constructing coded proteins may represent a largely global optimum, such that any aqueous biochemistry would use a very similar set.
Rethinking Abiogenesis: Part 1, Continuity of Life through Time
(The American Scientific Affliciation, 2020-03) Boring, Emily; Stump, J. B.; Freeland, Stephen
Evolution teaches that any particular organism, population, or species is a point on a continuous lineage that extends back to life’s origins. Apparent discontinuities (for example, species) often reflect subjective, human decisions as much or more than objective measurements. In the same way, no intrinsic, objective reason identifies any particular moment in the development of biochemical complexity as the origin of life other than the origin of the universe itself. There is no natural breakpoint presented by the physical universe. Focusing excessively on any other points robs science of impor tant context and is detrimental to future progress—for example, by failing to extend our view one notch further back in order to understand how and why this particular point emerged. We advocate, instead, a view of abiogenesis that stresses continuity over particular “starting points.” This way invites rich resonances with strands of historical and contemporary theology
Evolution as a Guide to Designing xeno Amino Acid Alphabets
(MDPI, 2021-03-10) Mayer-Bacon, Christopher; Agboha, Neyiasuo; Muscalli, Mickey; Freeland, Stephen
Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis
The Dangers of Anti-Science Stephen Hawking's Fear of Dangerous Aliens
(2010-05) Freeland, Stephen
Adaptive Properties of the Genetically Encoded Amino Acid Alphabet Are Inherited from Its Subsets
(natureresearch, 2019-08-28) Ilardo, Melissa; Bose, Rudrarup; Meringer, Markus; Rasulev, Bakhtiyor; Grefenstette, Natalie; Stephenson, James; Freeland, Stephen; Gillams, Richard J.; Butch, Christopher J.; Cleaves II, James
Life uses a common set of 20 coded amino acids (CAAs) to construct proteins. This set was likely canonicalized during early evolution; before this, smaller amino acid sets were gradually expanded as new synthetic, proofreading and coding mechanisms became biologically available. Many possible subsets of the modern CAAs or other presently uncoded amino acids could have comprised the earlier sets. We explore the hypothesis that the CAAs were selectively fixed due to their unique adaptive chemical properties, which facilitate folding, catalysis, and solubility of proteins, and gave adaptive value to organisms able to encode them. Specifically, we studied in silico hypothetical CAA sets of 3–19 amino acids comprised of 1913 structurally diverse α-amino acids, exploring the adaptive value of their combined physicochemical properties relative to those of the modern CAA set. We find that even hypothetical sets containing modern CAA members are especially adaptive; it is difficult to find sets even among a large choice of alternatives that cover the chemical property space more amply. These results suggest that each time a CAA was discovered and embedded during evolution, it provided an adaptive value unusual among many alternatives, and each selective step may have helped bootstrap the developing set to include still more CAAs.

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