Avida: a software platform for research in computational evolutionary biology

Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Algorithmic simulation in system design and innovation

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-63).This thesis explores the use of genetic programming as a tool in the system design and innovation process. Digital circuits are used as a proxy for complex technological designs. Circuit construction is simulated through a computer algorithm which assembles circuit designs in an attempt to reach specified design goals. Complex designs can be obtained by repeatedly combining simpler components, often called building blocks, which were created earlier in the algorithm's progression. This process is arguably a reflection of the traditional development path of systems engineering and technological innovation. The choice of algorithm used to guide this process is crucial. This thesis considers two general types of algorithms-a blind random search method, and a genetic programming search method-with variations applied to each. The research focused on comparing these algorithms in regard to: 1) the successful creation of multiple complex designs; 2) resources utilized in achieving a design of a given complexity; and 3) the inferred time dependence of technological improvement resulting from the process. Also of interest was whether these algorithms would exhibit exponential rates of improvement of the virtual technologies being created, as is seen in real-world innovation. The starting point was the hypothesis that the genetic programming approach might be superior to the random search method. The results found however that the genetic programming algorithm did not outperform the blind random search algorithm, and in fact failed to produce the desired circuit design goals. This unexpected outcome is believed to result from the structure of the circuit design process, and from certain shortcomings in the genetic programming algorithm used. This work also examines the relationship of issues and considerations (such as cost, complexity, performance, and efficiency) faced in these virtual design realms to managerial strategy and how insights from these experiments might be applied to real-world engineering and design challenges. Algorithmic simulation approaches, including genetic programming, are found to be powerful tools, having demonstrated impressive performance in bounded domains. However, their utility to systems engineering processes remains unproven. Therefore, use of these algorithmic tools and their integration into the human creative process is discussed as a challenge and an area needing further research.by Timothy Harsh.S.M.in Engineering and Managemen

Fetal programming and parent-of-origin effects of type 2 diabetes and insulin secretion

Abstract Type 2 diabetes mellitus (T2DM) is a heterogeneous and a complex disease defined by hyperglycemia. Thepancreas and its islets are central for glucose homeostasis and healthy adipose tissue. In turn, lipid levels in the bloodare crucial for glucose level stability. Both genetic and environmental factors and their interaction play a pivotal role inthe risk and development of the disease. In this thesis we aim to better understand the effect of genetic andenvironmental factors by investigating parental effects manifesting from early life until adulthood.In papers I and II we examined gene expression alterations and associated epigenetic changes due to early pregnancyanemia and gestational diabetes (GDM). Moreover, we investigated associations between these changes and neonatalanthropometry. We identified several differentially expressed genes between early pregnancy anemia, GDM andcontrols. Most of these genes were accompanied by epigenetic changes that correlated with their expression patterns.Interestingly, we identified several differentially expressed genes associated with neonatal anthropometry indicatingtheir possible role in fetal programming and risk of T2DM in later life due to maternal exposure to early pregnancyanemia and GDM.In paper III we investigated whether genetic variants which were previously reported to be associated with lipid traitswill exert different effects on obesity and blood lipid traits based on their parental origin. We examined These variantsin two European family cohorts, where parental origin of each variant was inferred and parental-specific associationwith obesity and blood lipid traits was analyzed. Our results corroborated previous reports and indicated that specificgenetic variants show parent-of-origin specific effects. Moreover, our results indicate possible sex-specific parentaleffects on some blood lipid traits.In paper IV we questioned whether such parental specific effects observed in paper III also manifested in early life. Asa result, we explored parent-of-origin effects on cardiometabolic and anthropometric traits in a birth cohort which wasfollowed up from delivery until 18 years. Our results indicate that the parental specific effects of cardiometabolic andanthropometric traits and associated genetic variants manifested in early life. Interestingly, however, not all parentaleffects were found to be fixed, and they seemed to transition over time specifically during puberty.In paper V we have examined the expression of imprinted genes to better understand their role in insulin secretion,beta-cell development, and function. First, we scrutinized gene expression data from adult pancreas, adult pancreaticislets, fetal pancreas, and single cell expression data. Next, we analyzed the association of these genes with glycemictraits. We identified imprinted genes that were specifically expressed in fetal pancreas both on a tissue and single celllevel. Variants in two genes associated with indices of insulin secretion indicating their possible role in beta-celldevelopment. Additionally, we identified imprinted genes enriched in both fetal and adult pancreas and associated withglucose and insulin traits in a parent-of-origin manner. This suggests the possible role of these genes in beta-cellfunction.In summary, in this thesis we investigate paternal and maternal effects as a function of fetal programming and parentof-origin effects to better understand their influence on type 2 diabetes and insulin secretion

“It’s the end of the world as we know it and we feel fantastic: examining the end of suffering”

This paper examines the consequences of the transhumanist goal to eliminate the suffering of all sentient beings. While transhumanists identify numerous approaches to this goal, the endgame is genetic modification of humans and natural predators. Pursuing this goal would cost trillions, and such treatments/technology would be available only to the wealthy. The transhumanist agenda around suffering is economically irresponsible, socially divisive, and inherently egotistical in its assumption that suffering is universally undesirable and meritless, and that scientists and the techno-elite have the right to modify sentient creatures. If transhumanists narrowed their focus to disease treatment and eradication, they could alleviate suffering while avoiding many of the negative consequences of their broader goal. Critically assessing the implications of the transhumanist agenda is crucial to the future of humanity, nature, and the planet as technology continues its exponential growth.Accepted manuscrip

Finding undetected protein associations in cell signaling by belief propagation

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio